This document provides guidelines for estimating LTE backhaul traffic. It finds that user plane data makes up 80-90% of backhaul traffic. Cell throughput peaks during quiet times when a single user can utilize the full bandwidth, reaching over 150Mbps, but such peaks are rare. During busy times, cell throughput is around 20Mbps due to many users and cell edge effects. The document models total traffic for single and multi-eNodeB backhaul networks and recommends provisioning based on peak single-cell throughput for individual eNodeBs and average busy throughput for aggregated traffic.
This document summarizes a study on the performance of LTE networks. The researchers conducted passive and active measurements on a commercial LTE network with over 300,000 users to analyze network characteristics and resource utilization. They found that while LTE provides higher bandwidth than 3G, TCP flows often underutilize available bandwidth due to factors like limited receive windows. On average, flows used only 52% of available bandwidth, lengthening transfers and wasting energy. The researchers developed techniques to estimate bandwidth and identify inefficient application behaviors to recommend protocol and design improvements.
Traffic offloading impact on the performanceIJCNCJournal
Long Term Evolution (LTE) is defined by the Third Generation Partnership Project (3GPP) standards as
Release 8/9. The LTE supports at max 20 MHz channel bandwidth for a carrier. The number of LTE users
and their applications are increasing, which increases the demand on the system BW. A new feature of the
LTE-Advanced (LTE-A) which is defined in the 3GPP standards as Release 10/11 is called Carrier Aggregation (CA), this feature allows the network to aggregate more carriers in-order to provide a higher bandwidth. Carrier Aggregation has three main cases: Intra-band contiguous, Intra-band non-contiguous, Inter-band contiguous. In addition to the Carrier Aggregation feature, LTE-A supports Heterogeneous Networks (HetNets). HetNets consists of a mix of macro-cells, remote radio heads, and low power nodes such as pico-cells, and femto-cells. HetNets allow cellular network operators to support higher data traffic
by offloading it to a smaller cells such as femto-cells. The aim of this paper is to evaluate the Quality of Service (QoS) performance of the Modified Largest Weighted Delay First (MLWDF), the Exponential Rule (Exp-Rule), and the Logarithmic Rule (Log-Rule) scheduling algorithms while offloading 50% of the macro-cell's traffic to five femto-cells, 100% of the macro-cell's traffic to five femto-cells, 100% of the macro-cell's traffic to ten femto-cells, and to compare it with the case in-which traffic offloading is not
applied. The QoS performance evaluation is based on the system's average throughput, Packet Loss Rate (PLR), average packet delay, and fairness among users. The LTE-Sim-5 with modifications is used in the simulation process. Simulation results show that offloading 100% of the Macro-cell's traffic to five femtocells had the highest maximum throughput, and the best PLR values especially when using the Log-Rule, in-which using it maintained the PLR values around 0.15 despite increasing the number of users. The least average packet delay was achieved when offloading 100% of the Macro-cell's traffic to ten femto-cells, the delay dropped to below 5 ms. The fairness indicators for the three scheduling algorithms while traffic
offloading was applied fluctuated in a linear way between a range of values of 0.7 and 0.9.
1 improvement of tcp congestion window over ltetanawan44
This document discusses improving the performance of TCP congestion control over LTE-Advanced networks. It proposes a new congestion avoidance mechanism that uses the available bandwidth of the connection to better detect the network path capacity and improve congestion avoidance. The mechanism is tested using the NS-2 network simulator to model LTE-Advanced traffic. The document provides background on LTE-Advanced network architecture and existing TCP congestion control mechanisms. It aims to develop an enhanced TCP variant that can efficiently transfer high data rates over the large bandwidth, low latency links of LTE-Advanced networks.
SGSN.
When a mobile terminal that was in an idle state attempts to send data, the following procedure occurs:
SGSN.
1) The mobile terminal sends a request to reestablish the radio bearer to the eNodeB.
• Steps (12) - (14):
If the radio bearer between the
2) The eNodeB forwards this request to the MME.
The SGSN sends an update loca-
mobile terminal and eNodeB has been
3) The MME instructs the S-GW to send any buffered downlink data to the mobile terminal and the radio bearer is re
A CELLULAR BONDING AND ADAPTIVE LOAD BALANCING BASED MULTI-SIM GATEWAY FOR MO...pijans
As it is well known, the QoS(quality of service) provided by mobile Internet access point devices is far from
the QoS level offered by the common ADSL modem-router due to several reasons: in fact, mobile Internet
access networks are not designed to support real-time data traffic because of several drawbacks
concerning the wireless medium such as resource sharing, traffic congestion, radio link coverage etc.,
which impact directly such parameters as delay, jitter, and packet loss rate that are strictly connected to
the quality of user experience. The main scope of the present paper is to introduce a dual USIM HSPA
gateway for ad hoc and sensors networks thanks to which it will be possible to guarantee a QoS suitable
for a series of network-centric application such as real-time communications and monitoring, video
surveillance, real-time sensor networks, telemedicine, vehicular and mobile sensor networks and so on. The
main idea is to exploit multiple radio access networks in order to enhance the available end-to-end
bandwidth and the perceived quality of experience. The scope has been reached by combining multiple
radio access with dynamic load balancing and the VPN (virtual private network) bond technique.
Design and analysis of routing protocol for cognitive radio ad hoc networks i...IJECEIAES
Multi-hop routing protocol in cognitive radio mobile ad hoc networks (CRMANETs) is a critical issue. Furthermore, the routing metric used in multi-hop CRMANETs should reflect the bands availability, the links quality, the PU activities and quality of service (QoS) requirements of SUs. For the best of our knowledge, many of researchers investigated the performance of the different routing protocols in a homogeneous environment only. In this paper, we propose a heterogeneous cognitive radio routing protocol (HCR) operates in heterogeneous environment (i.e. the route from source to destination utilize the licensed and unlicensed spectrum bands). The proposed routing protocol is carefully developed to make a tradeoff between the channel diversity of the routing path along with the CRMANETs throughput. Using simulations, we discuss the performance of the proposed HCR routing protocol and compare it with the AODV routing protocol using a discrete-event simulation which we developed using JAVA platform.
Duplexing mode, ARB and modulation approaches parameters affection on LTE upl...IJECEIAES
The next generation of radio technologies designed to increase the capacity and speed of mobile networks. LTE is the first technology designed explicitly for the Next Generation Network NGN and is set to become the de-facto NGN mobile access network standard. It takes advantage of the NGN's capabilities to provide an always-on mobile data experience comparable to wired networks. In this paper LTE uplink waveforms displayed with various duplexing mode, Allocated Resources Blocks ARB, Modulation types and total information per frame, QPSK and 16 QAM used as modulation techniques and tested under AWGN and Rayleigh channels, similarity and interference of the generated waveforms tested using auto-correlation and cross-correlation respectively.
IMPROVED QUALITY OF SERVICE PROTOCOL FOR REAL TIME TRAFFIC IN MANETIJCNCJournal
This document proposes an improved quality of service protocol for real-time traffic in mobile ad hoc networks. It presents a modified version of the AODV routing protocol that provides two key improvements: 1) A balanced best-effort traffic aware route discovery mechanism that selects paths with lower ratios of best-effort packets to minimize their impact on real-time traffic. 2) A packet forwarding procedure that gives transmission priority to real-time packets by transmitting them immediately from the queue while best-effort packets have to wait, improving throughput for real-time applications. Simulation results show the proposed protocol performs better than basic AODV in terms of throughput and delay for real-time traffic.
This document summarizes a study on the performance of LTE networks. The researchers conducted passive and active measurements on a commercial LTE network with over 300,000 users to analyze network characteristics and resource utilization. They found that while LTE provides higher bandwidth than 3G, TCP flows often underutilize available bandwidth due to factors like limited receive windows. On average, flows used only 52% of available bandwidth, lengthening transfers and wasting energy. The researchers developed techniques to estimate bandwidth and identify inefficient application behaviors to recommend protocol and design improvements.
Traffic offloading impact on the performanceIJCNCJournal
Long Term Evolution (LTE) is defined by the Third Generation Partnership Project (3GPP) standards as
Release 8/9. The LTE supports at max 20 MHz channel bandwidth for a carrier. The number of LTE users
and their applications are increasing, which increases the demand on the system BW. A new feature of the
LTE-Advanced (LTE-A) which is defined in the 3GPP standards as Release 10/11 is called Carrier Aggregation (CA), this feature allows the network to aggregate more carriers in-order to provide a higher bandwidth. Carrier Aggregation has three main cases: Intra-band contiguous, Intra-band non-contiguous, Inter-band contiguous. In addition to the Carrier Aggregation feature, LTE-A supports Heterogeneous Networks (HetNets). HetNets consists of a mix of macro-cells, remote radio heads, and low power nodes such as pico-cells, and femto-cells. HetNets allow cellular network operators to support higher data traffic
by offloading it to a smaller cells such as femto-cells. The aim of this paper is to evaluate the Quality of Service (QoS) performance of the Modified Largest Weighted Delay First (MLWDF), the Exponential Rule (Exp-Rule), and the Logarithmic Rule (Log-Rule) scheduling algorithms while offloading 50% of the macro-cell's traffic to five femto-cells, 100% of the macro-cell's traffic to five femto-cells, 100% of the macro-cell's traffic to ten femto-cells, and to compare it with the case in-which traffic offloading is not
applied. The QoS performance evaluation is based on the system's average throughput, Packet Loss Rate (PLR), average packet delay, and fairness among users. The LTE-Sim-5 with modifications is used in the simulation process. Simulation results show that offloading 100% of the Macro-cell's traffic to five femtocells had the highest maximum throughput, and the best PLR values especially when using the Log-Rule, in-which using it maintained the PLR values around 0.15 despite increasing the number of users. The least average packet delay was achieved when offloading 100% of the Macro-cell's traffic to ten femto-cells, the delay dropped to below 5 ms. The fairness indicators for the three scheduling algorithms while traffic
offloading was applied fluctuated in a linear way between a range of values of 0.7 and 0.9.
1 improvement of tcp congestion window over ltetanawan44
This document discusses improving the performance of TCP congestion control over LTE-Advanced networks. It proposes a new congestion avoidance mechanism that uses the available bandwidth of the connection to better detect the network path capacity and improve congestion avoidance. The mechanism is tested using the NS-2 network simulator to model LTE-Advanced traffic. The document provides background on LTE-Advanced network architecture and existing TCP congestion control mechanisms. It aims to develop an enhanced TCP variant that can efficiently transfer high data rates over the large bandwidth, low latency links of LTE-Advanced networks.
SGSN.
When a mobile terminal that was in an idle state attempts to send data, the following procedure occurs:
SGSN.
1) The mobile terminal sends a request to reestablish the radio bearer to the eNodeB.
• Steps (12) - (14):
If the radio bearer between the
2) The eNodeB forwards this request to the MME.
The SGSN sends an update loca-
mobile terminal and eNodeB has been
3) The MME instructs the S-GW to send any buffered downlink data to the mobile terminal and the radio bearer is re
A CELLULAR BONDING AND ADAPTIVE LOAD BALANCING BASED MULTI-SIM GATEWAY FOR MO...pijans
As it is well known, the QoS(quality of service) provided by mobile Internet access point devices is far from
the QoS level offered by the common ADSL modem-router due to several reasons: in fact, mobile Internet
access networks are not designed to support real-time data traffic because of several drawbacks
concerning the wireless medium such as resource sharing, traffic congestion, radio link coverage etc.,
which impact directly such parameters as delay, jitter, and packet loss rate that are strictly connected to
the quality of user experience. The main scope of the present paper is to introduce a dual USIM HSPA
gateway for ad hoc and sensors networks thanks to which it will be possible to guarantee a QoS suitable
for a series of network-centric application such as real-time communications and monitoring, video
surveillance, real-time sensor networks, telemedicine, vehicular and mobile sensor networks and so on. The
main idea is to exploit multiple radio access networks in order to enhance the available end-to-end
bandwidth and the perceived quality of experience. The scope has been reached by combining multiple
radio access with dynamic load balancing and the VPN (virtual private network) bond technique.
Design and analysis of routing protocol for cognitive radio ad hoc networks i...IJECEIAES
Multi-hop routing protocol in cognitive radio mobile ad hoc networks (CRMANETs) is a critical issue. Furthermore, the routing metric used in multi-hop CRMANETs should reflect the bands availability, the links quality, the PU activities and quality of service (QoS) requirements of SUs. For the best of our knowledge, many of researchers investigated the performance of the different routing protocols in a homogeneous environment only. In this paper, we propose a heterogeneous cognitive radio routing protocol (HCR) operates in heterogeneous environment (i.e. the route from source to destination utilize the licensed and unlicensed spectrum bands). The proposed routing protocol is carefully developed to make a tradeoff between the channel diversity of the routing path along with the CRMANETs throughput. Using simulations, we discuss the performance of the proposed HCR routing protocol and compare it with the AODV routing protocol using a discrete-event simulation which we developed using JAVA platform.
Duplexing mode, ARB and modulation approaches parameters affection on LTE upl...IJECEIAES
The next generation of radio technologies designed to increase the capacity and speed of mobile networks. LTE is the first technology designed explicitly for the Next Generation Network NGN and is set to become the de-facto NGN mobile access network standard. It takes advantage of the NGN's capabilities to provide an always-on mobile data experience comparable to wired networks. In this paper LTE uplink waveforms displayed with various duplexing mode, Allocated Resources Blocks ARB, Modulation types and total information per frame, QPSK and 16 QAM used as modulation techniques and tested under AWGN and Rayleigh channels, similarity and interference of the generated waveforms tested using auto-correlation and cross-correlation respectively.
IMPROVED QUALITY OF SERVICE PROTOCOL FOR REAL TIME TRAFFIC IN MANETIJCNCJournal
This document proposes an improved quality of service protocol for real-time traffic in mobile ad hoc networks. It presents a modified version of the AODV routing protocol that provides two key improvements: 1) A balanced best-effort traffic aware route discovery mechanism that selects paths with lower ratios of best-effort packets to minimize their impact on real-time traffic. 2) A packet forwarding procedure that gives transmission priority to real-time packets by transmitting them immediately from the queue while best-effort packets have to wait, improving throughput for real-time applications. Simulation results show the proposed protocol performs better than basic AODV in terms of throughput and delay for real-time traffic.
QOS-B ASED P ERFORMANCE E VALUATION OF C HANNEL -A WARE /QOS-A WARE S CHEDULI...csandit
This document evaluates the quality of service performance of three channel-aware/QoS-aware scheduling algorithms (Modified Largest Weighted Delay First, Exponential Rule, Logarithmic Rule) for video applications over LTE and LTE-Advanced networks. It first provides background on LTE network architecture and operation. It then describes how the simulator was modified to implement carrier aggregation in LTE-Advanced, allowing evaluation of scheduling performance with increased bandwidth. Simulation results show that carrier aggregation improved average throughput, reduced packet loss and delay, and increased fairness compared to LTE without aggregation.
The document describes a proposed unified algorithm for load balancing (LB) and handover optimization (HOO) in Long-Term Evolution (LTE) networks. The algorithm uses a Fuzzy System (FS) tuned by the Q-Learning reinforcement learning algorithm to modify handover parameters at the cell adjacency level. This aims to improve key performance indicators related to both LB and HOO. Simulation results show the proposed joint algorithm provides better performance than independent LB and HOO entities operating simultaneously. The algorithm reduces complexity for the self-organizing network coordination entity by handling LB and HOO jointly rather than as separate functions.
1. The document proposes an optimal Threshold Offloading (TO) algorithm to efficiently offload mobile data traffic from macrocells to femtocells in LTE networks. The TO algorithm considers the tradeoff between network signaling overhead from user mobility and femtocell offloading capability.
2. An analytical model is developed to quantify the performance of the TO algorithm and validate it through simulations. The results show that the TO algorithm can significantly reduce signaling overhead with minor reduction in femtocell offloading capability.
3. The paper provides network operators guidelines to set optimal offloading thresholds according to their management policies, offering a systematic approach based on the mathematical analysis.
The document describes an opportunistic packet scheduling and media access control (OSMA) protocol for wireless LANs and multi-hop ad hoc networks. The OSMA protocol aims to alleviate the head-of-line blocking problem and exploit multiuser diversity by allowing a node to schedule transmissions to receivers with good channel conditions. The key mechanisms of OSMA are multicast RTS frames containing a list of candidate receivers, and priority-based CTS frames where the receiver with the best channel and highest priority replies first to avoid collisions. Simulation results show the OSMA protocol can significantly improve network throughput while maintaining fairness between links.
A survey on routing algorithms and routing metrics for wireless mesh networksMohammad Siraj
This document summarizes a survey on routing algorithms and metrics for wireless mesh networks. It discusses the requirements of efficient mesh routing protocols including being distributed, adaptable to topology changes, loop-free, secure, scalable, and supporting quality of service. It reviews several important proactive routing protocols including destination-sequenced distance-vector routing, optimized link state routing, and mesh networking routing protocol. It also discusses reactive routing protocols and examples like dynamic source routing and ad hoc on-demand distance vector routing. Finally, it examines routing metrics and their impact on the performance of wireless mesh networks.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Inspecting Vanet for Determined Ways with Watertight Connectivityiosrjce
This paper is examining the VANET techniques by understanding the various papers published by the
authors in IEEE transactions. In this paper the new technique is explored where no of the routing method is
applied for connecting the nodes. But using the other information such as speed ,density,time,range and
calculating the time required by the vehicle on the road of interests we can link them full time period of their
run. We can use the standard frequency bands widths allocated by the Governments
Performing Network Simulators of TCP with E2E Network Model over UMTS NetworksAM Publications,India
Wireless links losses result in poor TCP throughput since losses are perceived as congestion by TCP with the evolution of 3G technologies like Universal Mobile Telecommunication System (UMTS), the usage of TCP has become more popular for a reliable end-to-end (e2e) data delivery. However, TCP was initially designed for wired networks and therefore it suffers performance degradation due to the radio signal getting affected by fading, shadowing and interference. There are many strategies proposed by the research community on how to improve the performance of TCP over wireless links such as introducing link-layer retransmission, explicitly notifying the sender of network conditions or using new variants of TCP. As UMTS network coverage and availability are currently experiencing rapid growth, optimization of various internal components of its wireless network is very important. One of the optimization is the introduction of High Speed Downlink Packet Access (HSDPA). This architecture not only allows higher data rates but also more reliable data transfer by the introduction of Hybrid ARQ (HARQ). With this enhancement to the UMTS network, it becomes vital to see the performance of TCP in such a network. Therefore in this thesis, we try to evaluate two aspects of UMTS networks: first, the impact of HSDPA parameters like scheduling algorithm and RLC/MAC-hs buffer size on overall performance of TCP and second, to study the behaviour of two categories of TCP rate and flow control: loss based and delay based. Our simulation shows that delay based TCP tends to perform better than loss based TCP in our selected scenarios. The simulations are performed using the network simulator NS-2 with an e2e network model for enhanced UMTS (EURANE).
Differentiated Classes of Service and Flow Management using An Hybrid Broker1IDES Editor
Recently, mobile networks have been overloaded
with a considerable amount of data traffic. The current paper
proposes a management service for mobile environments,
using policies and quality metrics, which ensure a better usage
of network resources with a more fine-grained management
based on flows with different classes of service and
transmission rates. This management of flows is supported
through a closed innovative control loop among a flexible
brokerage service in the network, and agents at the mobile
terminals. It also allows the terminals to make well-informed
decisions about their connections to enhance the number of
connected flows per technology and the individual service level
offered to each flow. Our results indicate that the proposed
solution optimizes the usage of available 4G network resources
among a high number of differentiated flows in several
scenarios where access technologies are extremely overloaded
whilst protecting, through a low complexity scheme, the flows
associated to users that have celebrated more expensive
contracts with their network operators.
Implementation of High Speed Railway Mobile Communication Systemrahulmonikasharma
High speed railways (HSR) provide highly efficient transport mode which improves the quality of railway services, saves time of the passengers which leads to greater customer satisfaction as well as improves the economics of the society. This has introduced significant challenges like developing new technologies, improving the existing architecture and controlling costs etc. Due to the improvements in the speed, ability to access internet and stream live media there is a requirement of an advanced high speed communication and signaling system. This system demands higher bandwidth, higher reliability and shorter response time for efficient operation and safety. This paper introduces the existing system deployed by the railway based on Global System for Mobile communication (GSM) , analyzes it and presents a much more advanced communication and signaling system based on 4G Long Term Evolution (LTE) technology.
This document compares the performance of HS-TCP and TCP in a hierarchical mobile IPv6 (HMIPv6) network. It summarizes a simulation conducted using the Network Simulator 2 (NS-2) to model an HMIPv6 topology with one home agent, two foreign agents, one mobile node, and one correspondent node. The simulation measured throughput as the mobile node moved between the foreign agents while communicating with the correspondent node. The results showed that HS-TCP had better throughput than TCP, with HS-TCP performing 32-96% better depending on the bandwidth level.
Mobile systems face challenges when connecting to Next Generation Networks (NGN) due to differences between circuit-switched mobile networks and the IP-based NGN. A new Base Station Gateway is proposed to provide these connections. NGN aims to reduce costs and offer integrated services via common IP technology, while mobile networks have evolved through generations based on circuit switching and packet technologies. Connecting mobile networks to the NGN backbone allows access to NGN services but requires protocol conversion through mobile media gateways.
Route Optimization (RO) in Mobile Internet Protocol
Version Six (MIPv6) is a technique that enables a
Mobile Node (MN) and a Corresponding Node (CN)
to communicate directly by bypassing the Home Agent
(HA). RO is usually faced with the problem of Internet
Protocol (IP) multilayer tunnels due to pinball or suboptimal
routing. The generic consideration in
designing route optimization scheme is to use
minimum signaling information in the IPv6 packet
header. In order for optimization to take place in
MIPv6, a protocol called route optimization protocol
must be introduced. Route optimization protocol is
used basically to improve performance. Also RO can
also be described as a mechanism that eliminates the
inefficiency in tunneling of packets from MRs to their
HA before being sent to CNs over the Internet.
However, Network Mobility (NEMO) can be described
as a network whose point of attachment to the Internet
varies with time.
The tradeoff between the two protocols can provide a
significant impact on the networks. Furthermore, one
potential choice of selecting any of the protocols can
increase or decrease the degree of application in used.
The tradeoff in offloading solution can vary from
mobile access network and core mobile network.
Optimizing traffic breakout and support for mobility
are paramount to service operators. The study focused
on the development and evaluation of an experimental
test bed of route optimization in MIPv6 and
NEMO.The tradeoff between the two protocols was
examined. The results of the experimental test bed
shows the benefit of next generation of Internet
system, especially for real-time applications that
need to provide seamless connection with low handoff
latency.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A New Model of Genetic Zone Routing Protocol (GZRP): The Process of Load Bala...TELKOMNIKA JOURNAL
The stages of the process of Genetic Algorithm (GA), are: Encoding Genotype and Chromosome;
Set Initialization Population; Evaluation Fitness Function; and Selection Process as well as in the later
stages Cross Over Process and Mutation. Outputs from the tests performed in this study can be obtained
by comparing the Genes of the Child (condition data traffic on the UMTS Hybrid - 802.11g network after
the GA) against Gen Holding (traffic data before the GA process).
The research was conducted by calculating the environmental factors, namely: The scheme Two
- Ray Model Propagation and Overlapping Channel Interference Factor, the Doppler Effect be ignored
because the User Equipment (UE) is considered not to shift significant arenas on the IEEE 802.11g
networks. The results of the research is as follows: In the process of cross over, there is a significant
change in the bandwidth, data traffic capacity and Power parameter changes by 9 MHz, 36 MB, and 40
dBm. In the process of mutation, there is a significant change in the bandwidth, data traffic capacity, and
Power parameter by 17 MHz, 32 MB, and 20 dBm.
Air Interface Virtualization using FBMC and OFDM ConfigurationsMalik Saad
Real time on air experiment is performed on Software-defined Radio (SDR) for radio virtualization using orthogonal frequency division multiplexing and filter bank multi carrier to support diverse mobile service requirements for user equipment.
Call Admission Control Scheme With Multimedia Scheduling Service in WiMAX Net...Waqas Tariq
WiMAX network introduces a multimedia data scheduling service with different quality of service (QoS) requirements. Transmission opportunities are scheduled by the service according to the types of traffic data for the different connections or users. In the paper, we first propose a uniform definition of QoS level for the multimedia data types in the service. The QoS level of a connection are determined by the type of data of the connection and its allocated resources. Based on these QoS levels, we propose a call admission control (CAC) scheme for the entry admission of a new connection without degrading the network performance and the QoS of ongoing connections. The key idea of this scheme is to regulate the arriving traffic of the network such that the network can work at an optimal point, given under a heavy load traffic. Taking advantage of the simulation experiments, we confirm the fact that the proposed scheme can achieve better trade-off between the overall performance of network system and the QoS level of individual connection.
The document discusses various proposed solutions for enabling fair coexistence between LTE-U and Wi-Fi networks sharing the same unlicensed spectrum. It summarizes four main solutions: 1) Dynamic Channel Selection which has limitations in dense networks with no vacant channels, 2) Listen-Before-Talk which risks LTE-U oppressing Wi-Fi access, 3) Carrier Sensing Adaptive Transmission which may not preserve enough space for Wi-Fi in multi-operator small cells, 4) Adaptive User/Resource Allocation framework which has practical limitations in complex small cells with independent Wi-Fi networks. The document proposes a hybrid approach could address the weaknesses of each individual solution.
Improving Performance of TCP in Wireless Environment using TCP-PIDES Editor
Improving the performance of the transmission
control protocol (TCP) in wireless environment has been an
active research area. Main reason behind performance
degradation of TCP is not having ability to detect actual reason
of packet losses in wireless environment. In this paper, we are
providing a simulation results for TCP-P (TCP-Performance).
TCP-P is intelligent protocol in wireless environment which
is able to distinguish actual reasons for packet losses and
applies an appropriate solution to packet loss.
TCP-P deals with main three issues, Congestion in
network, Disconnection in network and random packet losses.
TCP-P consists of Congestion avoidance algorithm and
Disconnection detection algorithm with some changes in TCP
header part. If congestion is occurring in network then
congestion avoidance algorithm is applied. In congestion
avoidance algorithm, TCP-P calculates number of sending
packets and receiving acknowledgements and accordingly set
a sending buffer value, so that it can prevent system from
happening congestion. In disconnection detection algorithm,
TCP-P senses medium continuously to detect a happening
disconnection in network. TCP-P modifies header of TCP
packet so that loss packet can itself notify sender that it is
lost.This paper describes the design of TCP-P, and presents
results from experiments using the NS-2 network simulator.
Results from simulations show that TCP-P is 4% more
efficient than TCP-Tahoe, 5% more efficient than TCP-Vegas,
7% more efficient than TCP-Sack and equally efficient in
performance as of TCP-Reno and TCP-New Reno. But we can
say TCP-P is more efficient than TCP-Reno and TCP-New
Reno since it is able to solve more issues of TCP in wireless
environment.
This document discusses the requirements for an LTE-capable transport network to deliver an optimized end-user experience. It focuses on capacity and latency. For capacity, a "single-peak, all-average" model is recommended that balances maximum capacity and economic feasibility. Latency must be low enough for applications like online gaming, with LTE offering latency around 20ms but the transport network also needing optimization to deliver that experience end-to-end. Dimensioning, aggregation, and latency guidelines are provided to help design an LTE transport network.
QOS-B ASED P ERFORMANCE E VALUATION OF C HANNEL -A WARE /QOS-A WARE S CHEDULI...csandit
Long Term Evolution (LTE) is defined by the Third G
eneration Partnership Project (3GPP)
standards as Release 8/9. The LTE supports at max 2
0 MHz channel bandwidth for a carrier.
The number of LTE users and their applications are
increasing, which increases the demand on
the system BW. A new feature of the LTE-Advanced (L
TE-A) which is defined in the 3GPP
standards as Release 10/11 is called Carrier Aggreg
ation (CA), this feature allows the network
to aggregate more carriers in-order to provide a hi
gher bandwidth. Carrier Aggregation has
three main cases: Intra-band contiguous, Intra-band
non-contiguous, Inter-band contiguous.
The main contribution of this paper was in implemen
ting the Intra-band contiguous case by
modifying the LTE-Sim-5, then evaluating the Qualit
y of Service (QoS) performance of the
Modified Largest Weighted Delay First (MLWDF), the
Exponential Rule (Exp-Rule), and the
Logarithmic Rule (Log-Rule) scheduling algorithms
Long term evolution (LTE) is replacing the 3G services slowly but steadily and become a preferred choice
for data for human to human (H2H) services and now it is becoming preferred choice for voice also. In
some developed countries the traditional 2G services gradually decommissioned from the service and
getting replaced with LTE for all H2H services. LTE provided high downlink and uplink bandwidth
capacity and is one of the technology like mobile ad hoc network (MANET) and vehicular ad hoc network
(VANET) being used as the backbone communication infrastructure for vehicle networking applications.
When Compared to VANET and MANET, LTE provides wide area of coverage and excellent infrastructure
facilities for vehicle networking. This helps in transmitting the vehicle information to the operator and
downloading certain information into the vehicle nodes (VNs) from the operators server. As per the ETSI
publications the number of machine to machine communication (MTC) devices are expected to touch 50
billion by 2020 and this will surpass H2H communication. With growing congestion in the LTE network,
accessing the network for any request from VN especially during peak hour is a big challenge because of
the congestion in random access channel (RACH). In this paper we will analyse this RACH congestion
problem with the data from the live network. Lot of algorithms are proposed for resolving the RACH
congestion on the basis of simulation results so we would like to present some practical data from the live
network to this issue to understand the extent RACH congestion issue in the real time scenario.
QOS-B ASED P ERFORMANCE E VALUATION OF C HANNEL -A WARE /QOS-A WARE S CHEDULI...csandit
This document evaluates the quality of service performance of three channel-aware/QoS-aware scheduling algorithms (Modified Largest Weighted Delay First, Exponential Rule, Logarithmic Rule) for video applications over LTE and LTE-Advanced networks. It first provides background on LTE network architecture and operation. It then describes how the simulator was modified to implement carrier aggregation in LTE-Advanced, allowing evaluation of scheduling performance with increased bandwidth. Simulation results show that carrier aggregation improved average throughput, reduced packet loss and delay, and increased fairness compared to LTE without aggregation.
The document describes a proposed unified algorithm for load balancing (LB) and handover optimization (HOO) in Long-Term Evolution (LTE) networks. The algorithm uses a Fuzzy System (FS) tuned by the Q-Learning reinforcement learning algorithm to modify handover parameters at the cell adjacency level. This aims to improve key performance indicators related to both LB and HOO. Simulation results show the proposed joint algorithm provides better performance than independent LB and HOO entities operating simultaneously. The algorithm reduces complexity for the self-organizing network coordination entity by handling LB and HOO jointly rather than as separate functions.
1. The document proposes an optimal Threshold Offloading (TO) algorithm to efficiently offload mobile data traffic from macrocells to femtocells in LTE networks. The TO algorithm considers the tradeoff between network signaling overhead from user mobility and femtocell offloading capability.
2. An analytical model is developed to quantify the performance of the TO algorithm and validate it through simulations. The results show that the TO algorithm can significantly reduce signaling overhead with minor reduction in femtocell offloading capability.
3. The paper provides network operators guidelines to set optimal offloading thresholds according to their management policies, offering a systematic approach based on the mathematical analysis.
The document describes an opportunistic packet scheduling and media access control (OSMA) protocol for wireless LANs and multi-hop ad hoc networks. The OSMA protocol aims to alleviate the head-of-line blocking problem and exploit multiuser diversity by allowing a node to schedule transmissions to receivers with good channel conditions. The key mechanisms of OSMA are multicast RTS frames containing a list of candidate receivers, and priority-based CTS frames where the receiver with the best channel and highest priority replies first to avoid collisions. Simulation results show the OSMA protocol can significantly improve network throughput while maintaining fairness between links.
A survey on routing algorithms and routing metrics for wireless mesh networksMohammad Siraj
This document summarizes a survey on routing algorithms and metrics for wireless mesh networks. It discusses the requirements of efficient mesh routing protocols including being distributed, adaptable to topology changes, loop-free, secure, scalable, and supporting quality of service. It reviews several important proactive routing protocols including destination-sequenced distance-vector routing, optimized link state routing, and mesh networking routing protocol. It also discusses reactive routing protocols and examples like dynamic source routing and ad hoc on-demand distance vector routing. Finally, it examines routing metrics and their impact on the performance of wireless mesh networks.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Inspecting Vanet for Determined Ways with Watertight Connectivityiosrjce
This paper is examining the VANET techniques by understanding the various papers published by the
authors in IEEE transactions. In this paper the new technique is explored where no of the routing method is
applied for connecting the nodes. But using the other information such as speed ,density,time,range and
calculating the time required by the vehicle on the road of interests we can link them full time period of their
run. We can use the standard frequency bands widths allocated by the Governments
Performing Network Simulators of TCP with E2E Network Model over UMTS NetworksAM Publications,India
Wireless links losses result in poor TCP throughput since losses are perceived as congestion by TCP with the evolution of 3G technologies like Universal Mobile Telecommunication System (UMTS), the usage of TCP has become more popular for a reliable end-to-end (e2e) data delivery. However, TCP was initially designed for wired networks and therefore it suffers performance degradation due to the radio signal getting affected by fading, shadowing and interference. There are many strategies proposed by the research community on how to improve the performance of TCP over wireless links such as introducing link-layer retransmission, explicitly notifying the sender of network conditions or using new variants of TCP. As UMTS network coverage and availability are currently experiencing rapid growth, optimization of various internal components of its wireless network is very important. One of the optimization is the introduction of High Speed Downlink Packet Access (HSDPA). This architecture not only allows higher data rates but also more reliable data transfer by the introduction of Hybrid ARQ (HARQ). With this enhancement to the UMTS network, it becomes vital to see the performance of TCP in such a network. Therefore in this thesis, we try to evaluate two aspects of UMTS networks: first, the impact of HSDPA parameters like scheduling algorithm and RLC/MAC-hs buffer size on overall performance of TCP and second, to study the behaviour of two categories of TCP rate and flow control: loss based and delay based. Our simulation shows that delay based TCP tends to perform better than loss based TCP in our selected scenarios. The simulations are performed using the network simulator NS-2 with an e2e network model for enhanced UMTS (EURANE).
Differentiated Classes of Service and Flow Management using An Hybrid Broker1IDES Editor
Recently, mobile networks have been overloaded
with a considerable amount of data traffic. The current paper
proposes a management service for mobile environments,
using policies and quality metrics, which ensure a better usage
of network resources with a more fine-grained management
based on flows with different classes of service and
transmission rates. This management of flows is supported
through a closed innovative control loop among a flexible
brokerage service in the network, and agents at the mobile
terminals. It also allows the terminals to make well-informed
decisions about their connections to enhance the number of
connected flows per technology and the individual service level
offered to each flow. Our results indicate that the proposed
solution optimizes the usage of available 4G network resources
among a high number of differentiated flows in several
scenarios where access technologies are extremely overloaded
whilst protecting, through a low complexity scheme, the flows
associated to users that have celebrated more expensive
contracts with their network operators.
Implementation of High Speed Railway Mobile Communication Systemrahulmonikasharma
High speed railways (HSR) provide highly efficient transport mode which improves the quality of railway services, saves time of the passengers which leads to greater customer satisfaction as well as improves the economics of the society. This has introduced significant challenges like developing new technologies, improving the existing architecture and controlling costs etc. Due to the improvements in the speed, ability to access internet and stream live media there is a requirement of an advanced high speed communication and signaling system. This system demands higher bandwidth, higher reliability and shorter response time for efficient operation and safety. This paper introduces the existing system deployed by the railway based on Global System for Mobile communication (GSM) , analyzes it and presents a much more advanced communication and signaling system based on 4G Long Term Evolution (LTE) technology.
This document compares the performance of HS-TCP and TCP in a hierarchical mobile IPv6 (HMIPv6) network. It summarizes a simulation conducted using the Network Simulator 2 (NS-2) to model an HMIPv6 topology with one home agent, two foreign agents, one mobile node, and one correspondent node. The simulation measured throughput as the mobile node moved between the foreign agents while communicating with the correspondent node. The results showed that HS-TCP had better throughput than TCP, with HS-TCP performing 32-96% better depending on the bandwidth level.
Mobile systems face challenges when connecting to Next Generation Networks (NGN) due to differences between circuit-switched mobile networks and the IP-based NGN. A new Base Station Gateway is proposed to provide these connections. NGN aims to reduce costs and offer integrated services via common IP technology, while mobile networks have evolved through generations based on circuit switching and packet technologies. Connecting mobile networks to the NGN backbone allows access to NGN services but requires protocol conversion through mobile media gateways.
Route Optimization (RO) in Mobile Internet Protocol
Version Six (MIPv6) is a technique that enables a
Mobile Node (MN) and a Corresponding Node (CN)
to communicate directly by bypassing the Home Agent
(HA). RO is usually faced with the problem of Internet
Protocol (IP) multilayer tunnels due to pinball or suboptimal
routing. The generic consideration in
designing route optimization scheme is to use
minimum signaling information in the IPv6 packet
header. In order for optimization to take place in
MIPv6, a protocol called route optimization protocol
must be introduced. Route optimization protocol is
used basically to improve performance. Also RO can
also be described as a mechanism that eliminates the
inefficiency in tunneling of packets from MRs to their
HA before being sent to CNs over the Internet.
However, Network Mobility (NEMO) can be described
as a network whose point of attachment to the Internet
varies with time.
The tradeoff between the two protocols can provide a
significant impact on the networks. Furthermore, one
potential choice of selecting any of the protocols can
increase or decrease the degree of application in used.
The tradeoff in offloading solution can vary from
mobile access network and core mobile network.
Optimizing traffic breakout and support for mobility
are paramount to service operators. The study focused
on the development and evaluation of an experimental
test bed of route optimization in MIPv6 and
NEMO.The tradeoff between the two protocols was
examined. The results of the experimental test bed
shows the benefit of next generation of Internet
system, especially for real-time applications that
need to provide seamless connection with low handoff
latency.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A New Model of Genetic Zone Routing Protocol (GZRP): The Process of Load Bala...TELKOMNIKA JOURNAL
The stages of the process of Genetic Algorithm (GA), are: Encoding Genotype and Chromosome;
Set Initialization Population; Evaluation Fitness Function; and Selection Process as well as in the later
stages Cross Over Process and Mutation. Outputs from the tests performed in this study can be obtained
by comparing the Genes of the Child (condition data traffic on the UMTS Hybrid - 802.11g network after
the GA) against Gen Holding (traffic data before the GA process).
The research was conducted by calculating the environmental factors, namely: The scheme Two
- Ray Model Propagation and Overlapping Channel Interference Factor, the Doppler Effect be ignored
because the User Equipment (UE) is considered not to shift significant arenas on the IEEE 802.11g
networks. The results of the research is as follows: In the process of cross over, there is a significant
change in the bandwidth, data traffic capacity and Power parameter changes by 9 MHz, 36 MB, and 40
dBm. In the process of mutation, there is a significant change in the bandwidth, data traffic capacity, and
Power parameter by 17 MHz, 32 MB, and 20 dBm.
Air Interface Virtualization using FBMC and OFDM ConfigurationsMalik Saad
Real time on air experiment is performed on Software-defined Radio (SDR) for radio virtualization using orthogonal frequency division multiplexing and filter bank multi carrier to support diverse mobile service requirements for user equipment.
Call Admission Control Scheme With Multimedia Scheduling Service in WiMAX Net...Waqas Tariq
WiMAX network introduces a multimedia data scheduling service with different quality of service (QoS) requirements. Transmission opportunities are scheduled by the service according to the types of traffic data for the different connections or users. In the paper, we first propose a uniform definition of QoS level for the multimedia data types in the service. The QoS level of a connection are determined by the type of data of the connection and its allocated resources. Based on these QoS levels, we propose a call admission control (CAC) scheme for the entry admission of a new connection without degrading the network performance and the QoS of ongoing connections. The key idea of this scheme is to regulate the arriving traffic of the network such that the network can work at an optimal point, given under a heavy load traffic. Taking advantage of the simulation experiments, we confirm the fact that the proposed scheme can achieve better trade-off between the overall performance of network system and the QoS level of individual connection.
The document discusses various proposed solutions for enabling fair coexistence between LTE-U and Wi-Fi networks sharing the same unlicensed spectrum. It summarizes four main solutions: 1) Dynamic Channel Selection which has limitations in dense networks with no vacant channels, 2) Listen-Before-Talk which risks LTE-U oppressing Wi-Fi access, 3) Carrier Sensing Adaptive Transmission which may not preserve enough space for Wi-Fi in multi-operator small cells, 4) Adaptive User/Resource Allocation framework which has practical limitations in complex small cells with independent Wi-Fi networks. The document proposes a hybrid approach could address the weaknesses of each individual solution.
Improving Performance of TCP in Wireless Environment using TCP-PIDES Editor
Improving the performance of the transmission
control protocol (TCP) in wireless environment has been an
active research area. Main reason behind performance
degradation of TCP is not having ability to detect actual reason
of packet losses in wireless environment. In this paper, we are
providing a simulation results for TCP-P (TCP-Performance).
TCP-P is intelligent protocol in wireless environment which
is able to distinguish actual reasons for packet losses and
applies an appropriate solution to packet loss.
TCP-P deals with main three issues, Congestion in
network, Disconnection in network and random packet losses.
TCP-P consists of Congestion avoidance algorithm and
Disconnection detection algorithm with some changes in TCP
header part. If congestion is occurring in network then
congestion avoidance algorithm is applied. In congestion
avoidance algorithm, TCP-P calculates number of sending
packets and receiving acknowledgements and accordingly set
a sending buffer value, so that it can prevent system from
happening congestion. In disconnection detection algorithm,
TCP-P senses medium continuously to detect a happening
disconnection in network. TCP-P modifies header of TCP
packet so that loss packet can itself notify sender that it is
lost.This paper describes the design of TCP-P, and presents
results from experiments using the NS-2 network simulator.
Results from simulations show that TCP-P is 4% more
efficient than TCP-Tahoe, 5% more efficient than TCP-Vegas,
7% more efficient than TCP-Sack and equally efficient in
performance as of TCP-Reno and TCP-New Reno. But we can
say TCP-P is more efficient than TCP-Reno and TCP-New
Reno since it is able to solve more issues of TCP in wireless
environment.
This document discusses the requirements for an LTE-capable transport network to deliver an optimized end-user experience. It focuses on capacity and latency. For capacity, a "single-peak, all-average" model is recommended that balances maximum capacity and economic feasibility. Latency must be low enough for applications like online gaming, with LTE offering latency around 20ms but the transport network also needing optimization to deliver that experience end-to-end. Dimensioning, aggregation, and latency guidelines are provided to help design an LTE transport network.
QOS-B ASED P ERFORMANCE E VALUATION OF C HANNEL -A WARE /QOS-A WARE S CHEDULI...csandit
Long Term Evolution (LTE) is defined by the Third G
eneration Partnership Project (3GPP)
standards as Release 8/9. The LTE supports at max 2
0 MHz channel bandwidth for a carrier.
The number of LTE users and their applications are
increasing, which increases the demand on
the system BW. A new feature of the LTE-Advanced (L
TE-A) which is defined in the 3GPP
standards as Release 10/11 is called Carrier Aggreg
ation (CA), this feature allows the network
to aggregate more carriers in-order to provide a hi
gher bandwidth. Carrier Aggregation has
three main cases: Intra-band contiguous, Intra-band
non-contiguous, Inter-band contiguous.
The main contribution of this paper was in implemen
ting the Intra-band contiguous case by
modifying the LTE-Sim-5, then evaluating the Qualit
y of Service (QoS) performance of the
Modified Largest Weighted Delay First (MLWDF), the
Exponential Rule (Exp-Rule), and the
Logarithmic Rule (Log-Rule) scheduling algorithms
Long term evolution (LTE) is replacing the 3G services slowly but steadily and become a preferred choice
for data for human to human (H2H) services and now it is becoming preferred choice for voice also. In
some developed countries the traditional 2G services gradually decommissioned from the service and
getting replaced with LTE for all H2H services. LTE provided high downlink and uplink bandwidth
capacity and is one of the technology like mobile ad hoc network (MANET) and vehicular ad hoc network
(VANET) being used as the backbone communication infrastructure for vehicle networking applications.
When Compared to VANET and MANET, LTE provides wide area of coverage and excellent infrastructure
facilities for vehicle networking. This helps in transmitting the vehicle information to the operator and
downloading certain information into the vehicle nodes (VNs) from the operators server. As per the ETSI
publications the number of machine to machine communication (MTC) devices are expected to touch 50
billion by 2020 and this will surpass H2H communication. With growing congestion in the LTE network,
accessing the network for any request from VN especially during peak hour is a big challenge because of
the congestion in random access channel (RACH). In this paper we will analyse this RACH congestion
problem with the data from the live network. Lot of algorithms are proposed for resolving the RACH
congestion on the basis of simulation results so we would like to present some practical data from the live
network to this issue to understand the extent RACH congestion issue in the real time scenario.
The document discusses LTE technology developments and the vision for 2020. It notes that Release 12 of LTE, expected in 2014, will significantly extend mobile broadband availability, improve service quality, and help meet exponentially growing data demands through approaches like using 3x more spectrum, achieving 6x greater spectral efficiency, and deploying small cells for 56x higher average cell density. LTE is positioned as the dominant air interface standard moving forward for both existing operators and new deployments worldwide. Release 12 aims to not only satisfy current users but facilitate new usage profiles and applications through enhancements.
TECHNIQUES FOR OFFLOADING LTE EVOLVED PACKET CORE TRAFFIC USING OPENFLOW: A C...IJCNCJournal
Cellular users of today have an insatiable appetite for bandwidth and data. Data-intensive applications, such as video on demand, online gaming and video conferencing, have gained prominence. This, coupled with recent innovations in the mobile network such as LTE/4G, poses a unique challenge to network
operators in how to extract the most value from their deployments while reducing their Total Cost of Operations(TCO). To this end, a number of enhancements have been proposed to the “conventional” LTE mobile network. Most of these recognize the monolithic and non-elastic nature of the mobile backend and propose complimenting core functionality with concepts borrowed from Software Defined Networking
(SDN). In this paper, we will attempt to explore some existing options within the LTE standard to address traffic challenges. We then survey some SDN-enabled alternatives and comment on their merits and drawbacks.
A CELLULAR BONDING AND ADAPTIVE LOAD BALANCING BASED MULTI-SIM GATEWAY FOR MO...pijans
As it is well known, the QoS(quality of service) provided by mobile Internet access point devices is far from
the QoS level offered by the common ADSL modem-router due to several reasons: in fact, mobile Internet
access networks are not designed to support real-time data traffic because of several drawbacks
concerning the wireless medium such as resource sharing, traffic congestion, radio link coverage etc.,
which impact directly such parameters as delay, jitter, and packet loss rate that are strictly connected to
the quality of user experience. The main scope of the present paper is to introduce a dual USIM HSPA
gateway for ad hoc and sensors networks thanks to which it will be possible to guarantee a QoS suitable
for a series of network-centric application such as real-time communications and monitoring, video
surveillance, real-time sensor networks, telemedicine, vehicular and mobile sensor networks and so on. The
main idea is to exploit multiple radio access networks in order to enhance the available end-to-end
bandwidth and the perceived quality of experience. The scope has been reached by combining multiple
radio access with dynamic load balancing and the VPN (virtual private network) bond technique.
A Cellular Bonding and Adaptive Load Balancing Based Multi-Sim Gateway for Mo...pijans
As it is well known, the QoS(quality of service) provided by mobile Internet access point devices is far from
the QoS level offered by the common ADSL modem-router due to several reasons: in fact, mobile Internet
access networks are not designed to support real-time data traffic because of several drawbacks
concerning the wireless medium such as resource sharing, traffic congestion, radio link coverage etc.,
which impact directly such parameters as delay, jitter, and packet loss rate that are strictly connected to
the quality of user experience. The main scope of the present paper is to introduce a dual USIM HSPA
gateway for ad hoc and sensors networks thanks to which it will be possible to guarantee a QoS suitable
for a series of network-centric application such as real-time communications and monitoring, video
surveillance, real-time sensor networks, telemedicine, vehicular and mobile sensor networks and so on. The
main idea is to exploit multiple radio access networks in order to enhance the available end-to-end
bandwidth and the perceived quality of experience. The scope has been reached by combining multiple
radio access with dynamic load balancing and the VPN (virtual private network) bond technique.
This document discusses throughput performance analysis of Voice over IP (VoIP) in Long Term Evolution (LTE) networks. It begins with an introduction to LTE and the increasing demand for high-speed wireless communication. It then describes the generic frame structures used in LTE, including Type 1 and Type 2 frames for Frequency Division Duplexing (FDD) and Time Division Duplexing (TDD) respectively. Next, it covers LTE's quality of service framework and use of Real-time Transport Protocol (RTP) for audio and video transmission. Finally, it provides an overview of VoIP technology and its characteristics, such as delay requirements and use of codecs like AMR to provide constant bit rate transmission of compressed
Throughput Performance Analysis VOIP over LTEiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Novel Approach for Cell Selection and Synchronization in LTE-AdvancedT. L. Singal
Long Term Evolution (LTE) is the result of the standardization work done by the 3rd Generation Partnership Project (3GPP) to achieve a new high speed radio access in the mobile communications frame. Cell selection by a mobile UE is another issue in LTE. In particularly, an interesting challenge in the physical layer of LTE is how the mobile unit immediately after powering on, select a radio cell and locks on to it. More specifically, to understand how the mobile unit establishes the connection with the strongest cell station in surrounding region. To do this, the mobile unit has to overcome the challenges of estimating the channel to communicate with the cell site and frequency synchronization. To appropriately synchronize the mobile unit with the base station when multiple mobile unit are communicating with same receiver from various distances.
The important goal of this thesis is represented as demonstrating a self-organising based process for current versions of heterogeneous LTE-Advanced networks to simultaneously improve both quality of service and ability. The main index terms of this research could be exhibited as: SON; LTE-A, HetNets; Femtocell; Interference, Multi-Layer; Handover, Access Control; Power Control, eICIC. The self-organizing method of this research is described as the primary goal, to be got through the following targets: ThesisScientist.com
This document analyzes the performance of the LTE physical layer under 3GPP standards parameters. It summarizes an analysis of downlink and uplink throughput for LTE operating in both FDD and TDD modes with different system bandwidths, antenna configurations, modulation schemes, and coding rates. The key results showed that LTE can support downlink throughputs up to 300Mbps with 20MHz bandwidth using MIMO 4x4, and uplink throughputs up to 75Mbps.
In this paper, we propose a new traffic flow model of the Long Term Evaluation (LTE) network for the Evolved Universal Terrestrial Radio Access Network (E-UTRAN). Here only one Evolve Node B (eNB) nearest to the Mobility Management Entity (MME) and Serving Gateway (S-GW) will use the S1 link to bridge the E-UTRAN and Evolved Packet Core (EPC). All the eNBs of a tracking area will be connected to each other by the X2 link. Determination of capacity of a links of such a network is a challenging job since each node offers its own traffic and at the same time conveys traffic of other nodes. In this paper, we apply maximum flow algorithm including superposition theorem to solve the traffic flow of radio network. Using the total flow per subcarrier, a new traffic model is also developed in the paper. The relation among the traffic parameters: ‘blocking probability’, ‘offered traffic’, ‘instantaneous capacity’, ‘average holding time’, and ‘number of users’ are shown graphically under both QPSK and 16-QAM. The concept of the network will be helpful to improve the SINR of the received signal ofeNBslocated long distance relative to MME/S-GW.
PERFORMANCE EVALUATION OF LTE NETWORK USING MAXIMUM FLOW ALGORITHMijcsit
In this paper, we propose a new traffic flow model of the Long Term Evaluation (LTE) network for the Evolved Universal Terrestrial Radio Access Network (E-UTRAN). Here only one Evolve Node B (eNB) nearest to the Mobility Management Entity (MME) and Serving Gateway (S-GW) will use the S1 link to
bridge the E-UTRAN and Evolved Packet Core (EPC). All the eNBs of a tracking area will be connected to each other by the X2 link. Determination of capacity of a links of such a network is a challenging job since each node offers its own traffic and at the same time conveys traffic of other nodes. In this paper, we apply maximum flow algorithm including superposition theorem to solve the traffic flow of radio network. Using the total flow per subcarrier, a new traffic model is also developed in the paper. The relation among the traffic parameters: ‘blocking probability’, ‘offered traffic’, ‘instantaneous capacity’, ‘average holding
time’, and ‘number of users’ are shown graphically under both QPSK and 16-QAM. The concept of the network will be helpful to improve the SINR of the received signal ofeNBslocated long distance relative to MME/S-GW.
The document discusses the need for new wireless technologies to support increasing demand for data and high-speed services. It notes that technologies need to focus on using more spectrum, improving spectral efficiency, employing smaller cell sizes like femtocells, and incentivizing off-peak traffic. The rest of the document provides details on how LTE wireless technology addresses these needs through technical specifications and network architecture, including the use of an Evolved Packet Core and separating the user and control planes.
Creating The Future Economically-Viable Networks_JAN17Emre Yilmaz
1) The document discusses the need for new radio network systems to meet increasing data traffic demands and diversifying connection needs in the future.
2) A key challenge is ensuring the economic viability of these new systems for network operators, as costs per megabyte have decreased. Any new system must prioritize cost reductions.
3) The author proposes several approaches to reduce network costs, such as using intelligent optimization to reduce energy usage, redesigning network architecture to centralize equipment, and employing a multi-tiered network structure using different spectrum bands.
Video steaming Throughput Performance Analysis over LTEiosrjce
This document analyzes the video streaming throughput performance over LTE networks using the OPNET simulation tool. It simulates two scenarios: 1) downlink and uplink video conferencing with static users and 2) the same with users moving at 30m/s. The key metrics measured are packet delay variation and end-to-end delay. The results show that static users experience higher packet delay variation than mobile users, likely due to increased traffic accumulation. End-to-end delay is also higher for static users compared to those moving at 30m/s.
This document analyzes the video streaming throughput performance over LTE networks using the OPNET simulation tool. It simulates two scenarios: 1) downlink and uplink video conferencing with static users and 2) the same with users moving at 30m/s. The key metrics measured are packet delay variation and end-to-end delay. The results show that static users experience higher packet delay variation than mobile users, likely due to increased traffic accumulation. End-to-end delay is also higher for static users compared to those moving at 30m/s.
Efficient Vertical Handoff Management in LTE Cellular NetworksIRJET Journal
This document proposes a neuro-fuzzy based approach for efficient vertical handover management in LTE cellular networks. It discusses how single criteria handover decisions can cause inefficient handovers. It then describes a neuro-fuzzy system that uses fuzzy logic and neural networks to make multi-criteria handover decisions based on parameters like RSS, network load, bandwidth, and jitter. The system collects input values, evaluates them using fuzzy rules, aggregates the outputs, and selects the best network. Simulation results show that this approach can improve handover performance and QoS in heterogeneous wireless networks.
Similar to Ngmn whitepaper guideline_for_lte_backhaul_traffic_estimation_2 (20)
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
3. 3
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 3 of 18
Executive Summary
A model is developed to predict traffic levels in transport networks used to backhaul LTE eNodeBs.
Backhaul traffic is made up of a number of different components of which user plane data is the largest,
comprising around 80-90% of overall traffic, slightly less when IPsec encryption is added. The remainder
consists of the transport protocol overhead and traffic forwarding to another base-station during handover.
Network signalling, management and synchronisation were assumed to be negligible.
User plane traffic was depends on the characteristics of cell throughput that can be delivered by the LTE air
interface. Simulations of LTE cell throughput showed very high peaks were possible, corresponding to the
maximum UE (user equipment) capabilities of up to 150Mbps. However, such peaks were only found to
occur under very light network loads of less than one user per cell. During ‘busy times’ with high user traffic
demands, cell throughputs were significantly lower than the quiet time peaks: A heavily loaded 20MHz 2x2
LTE downlink cell limits at around 20Mbps cell throughput. In this scenario, the overall spectral efficiency of
the cell is brought down by the presence of ‘cell edge users’, with poor signal quality and correspondingly low
data rates.
These results reveal that the cell throughput characteristics for data carrying networks are quite different to
those of voice carrying networks. In a data dominated LTE network, the peak cell throughputs in the
hundreds of Mbps will occur during quiet times. Conversely in voice dominated networks, cell throughput is
related to the number of active calls, hence peaks occur during the ‘busy hours’. Since cell throughput peaks
occur rarely and during quiet times, it is assumed that they do not occur simultaneously on neighbouring
cells. On the other hand, the ‘busy time’ mean traffic will occur on all cells at the same time. The total user
plane traffic for a tri-cell eNodeB (an LTE base station) is modelled as the larger of the peak from one cell, or
the combined busy time mean of the three cells. The same rule is applied to the calculation of traffic from
multiple aggregated eNodeBs.
For the LTE downlink, peak cell throughput is around 4-6x the busy time mean, so for backhaul traffic
aggregates of less than 4-6 cells typical of the ‘last mile’ of the transport network, it is the quiet time peak
that dominates capacity provisioning. For aggregates of 6 or more cells (e.g. two or more tricell eNodeBs), it
is the busy time mean that dominates provisioning of the ‘core’ and ‘aggregation’ regions of the transport
network. From a technical perspective, it may not seem practical to provision the last mile backhaul for a
peak rate that rarely occurs in practice. However, the ability to deliver such rates may be driven by marketing
requirements, as consumers are more likely to select networks or devices which can advertise higher
maximum rates.
The results presented in this paper represent mature LTE networks with sufficient device penetration to fully
load all cells during the busy times. It is recognised that it may take several years to reach such a state, and
even then, not all cells may reach full load. The lighter levels of loading likely in the early years of the
network will reduce the ‘busy time mean’ figures applicable to the aggregation and core regions of the
transport network. However, the quiet time peaks if anything will be more prevalent, and so provisioning in
the last mile will have to accommodate them from day one.
The transport provisioning figures given this paper are provided as guidelines to help the industry understand
the sorts of traffic levels and characteristics that LTE will demand. They should not be interpreted as
requirements, and it should be recognised that provisioning may need to be adjusted according to the
particular deployment conditions of individual RAN sites. Results are given for a range of uplink and downlink
scenarios applicable to Release 8 of the LTE specifications. These include 10MHz and 20MHz system
bandwidths, various MIMO configurations, and different UE categories.
4. 4
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 4 of 18
Contents
1. Introduction 5
1.1. Structure of the Report 5
2. Evaluation of User and Cell Throughput 6
2.1. Fundamentals of Cell and User Throughput 6
2.2. Cell throughput during Busy and Quiet times 7
2.3. Backhaul Provisioning for User Traffic 8
2.4. Data points for Mean and Peak Cell Throughput 9
Simulation Results for P 10
2.5. eak and Mean Cell Throughput 10
3. Single eNodeB Transport Provisioning 11
3.1.1. X2 Traffic 11
3.1.2. Control Plane, OAM and Synchronisation Signalling 11
3.1.3. Transport Protocol Overhead 11
3.1.4. IPsec 11
3.2. Summary of Single eNodeB Traffic 12
4. Multi-eNodeB Transport Provisioning 13
4.1. Principles of Multi-ENodeB Provisioning 13
4.2. Provsioning for Multiple eNodeBs (No IPsec) 14
4.3. Provisioning with IPsec Encryption 15
5. Interpretation and Adaptation of Results to Real World Networks 16
5.1. Network maturity and device penetration 16
5.2. Load variation between sites 16
5.3. High mobility sites 16
5.4. Small or isolated cells 16
6. Conclusions 17
7. References 18
5. 5
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 5 of 18
1. Introduction
The new LTE mobile broadband standard promises significantly higher data rates for consumers than current
HSPA technology, and at a significantly lower cost per bit for the Operator. Field tests show that end user
download rates in excess of 150Mbps are achievable where conditions allow [3]. While this seems like great
news for the end users, there are concerns in the operator community on how to backhaul what initially
appears to be vast volumes of data: If just one user can download at 150Mbps, what is the total backhaul
traffic from a multi-cell base station supporting tens of users?
This paper answers this question by considering the total user traffic that LTE base stations can handle both
during the busy hours and in the quiet times. To this, we add other components of backhaul traffic including
signalling, transport overheads and the new X2 interface. This provides us with figures for the total backhaul
traffic per eNodeB (an LTE base station), representing the provisioning needed in the ‘last mile’ of the
transport network, illustrated in Figure 1. Provisioning for the ‘aggregation’ and ‘core’ parts of the transport
network is then derived by combining traffic from multiple eNodeBs, using simple assumptions for the
statistical multiplexing gains.
UE traffic
served by eNodeBs
Last mile
serves eNodeBs
aggregation
core
eNodeBs
Transport
network
External
Networks
Figure 1 Places in the LTE/EPC network where traffic can be characterized
This study predicts traffic levels in the transport network using a theoretical modelling approach. This is
needed in the early years of LTE roll out when network sizes and device penetration are too low to be able to
perform useful measurements of backhaul traffic. Once loading levels in LTE networks increase, empirical
methods can be used to validate, adjust and ultimately replace the theoretical models described in this
paper. The study was performed as part of the NGMN’s Optimised Backhaul Project. The method and
assumptions have been agreed between the leading LTE Equipment Vendors and Operators.
The backhaul traffic figures produced by this study represent mature LTE networks with a sufficient number
of subscribers to fully load eNodeBs during busy times. In practice, it may take several years after roll out to
reach this state, and even then, only some of the eNodeBs in the network will be fully loaded. Backhaul
traffic may also be impacted by the type of deployment: For example, sites near motorways may see higher
levels of handover signalling, and isolated sites may generate higher traffic levels due to a lack of other cell
interference. In many cases, LTE will be deployed on sites supporting other RAN technologies such as GSM
or HSPA, which will generate their own backhaul traffic. In summary, the provisioning figures given in this
report for mature LTE eNodeBs may need to be adjusted to suit the particular conditions of an operator’s
network. It should be understood that these are recommendations rather than requirements and different
operators may have different provisioning strategies.
1.1. Structure of the Report
Since backhaul is predominantly user plane traffic, the study starts with an analysis of this component in
section 2. Section 3 goes on to describe the other components of backhaul which must be considered when
provisioning for each eNodeB. These include X2 traffic overheads and security. Section 4 considers how to
aggregate traffic generated a number of eNodeBs. Section 5 discusses how the results should be interpreted
and adapted for application to real world networks. Conclusions are drawn in section 6.
6. 6
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 6 of 18
2. Evaluation of User and Cell Throughput
2.1. Fundamentals of Cell and User Throughput
Backhaul traffic is predominantly user data, so the analysis considers this first and adds other components
such as overheads and signalling later. Figure 2 illustrates the key concepts in evaluating the total user traffic
carried by an eNodeB. The terms ‘cell’, ‘cell site’ and ‘base station’ are often used interchangeably,
however in this paper, they follow the 3GPP convention: User Equipments (UEs) are served by one of many
‘cells’ in the coverage area. A “macro” LTE base station (eNodeB) typically controls three cells, ‘micro’ and
‘pico’ eNodeBs typically only control one cell and some city centre eNodeBs are starting to use six cells.
Backhaul traffic per eNodeB is the total of all cells controlled by that eNodeB. Cell throughput is the sum of
traffic for each of the UEs served by that cell. Each UE’s throughput varies depending on the quality of their
radio link to the eNodeB, and the amount of spectrum resource assigned to them.
Othercell
interference
Multiple UEs
sharing cell
Othercells
around same
eNodeB
Uu links have different
Spectralefficiencies
Transport
Provisioning
ForN eNodeBs
Figure 2 Factors which impact user traffic to be backhauled
LTE transceivers use ‘adaptive modulation and coding’ to adjust their data rate to the radio conditions. In
good conditions where the UE is close to the eNodeB and there is little interference, more bits of information
can be carried without error for each unit of spectrum. This is called spectrum efficiency, and is measured in
bits per second, per Hz (bits/s/Hz). Radio conditions are characterized by the Signal to Interference plus
Noise Ratio, or SINR. 64QAM modulation can send 6 bits/s/Hz, but requires high SINR, whereas QPSK only
sends 2 bits/s/Hz, but can still be received without error in the poor signal conditions found near the ‘cell
edge’ during busy hour when interference is high. Variable rate coding is also used to provide finer tuning to
match the data rate to the SINR.
The LTE RAN (Radio Access Network) operates at N=1 reuse, which means that each cell in the network
can (re)use the entire bandwidth of the spectrum block owned by the operator. Apart from some overheads,
most of this bandwidth is shared amongst the served UEs to carry their data. Clearly when there are more
users, each UE is assigned a smaller share.
UE throughput (bits/s) is the product of its spectral efficiency (bits/s/Hz) and the assigned share of the cell’s
spectrum (Hz). Cell throughput is the sum of all UE throughputs served by that cell. Since the total spectrum
cannot change (i.e. the system bandwidth), cell throughput is the total spectrum multiplied by the cell
average spectral efficiency of UEs served by that cell.
7. 7
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 7 of 18
2.2. Cell throughput during Busy and Quiet times
Figure 3 illustrates the variation in cell average spectral efficiency during busy and quiet times in the network.
During busy times (Figure 3a), there are many UEs being served by each cell. The UEs have a range of
spectrum efficiencies, depending on the quality of their radio links. Since there are many UEs, it is unlikely
that they will all be good or all be bad, so the cell average spectral efficiency (and hence cell throughput) will
be somewhere in the middle.
During quiet times however, there may only be one UE served by the cell. The cell spectrum efficiency (and
throughput) will depend entirely on that of the served UE, and there may be significant variations. Figure 3
(b) shows the scenario under which the highest UE and cell throughputs occur: One UE with a good link has
the entire cell’s spectrum to itself. This is the condition which represents the “headline” figures for peak data
rate. Peak download rates of 150Mbps have been demonstrated for LTE with 20MHz bandwidth (and 2x2
MIMO) [3], and peak rates beyond 1Gbps are proposed in later releases of the standard.
Spectral
Efficiency
bps/Hz
Bandwidth,Hz
64QAM
16QAM
QPSK
cell
average
Busy Time
More averaging
UE1
UE2
UE3
:
:
:
Many
UEs
Quiet Time
More variation
UE1
64QAM
Cellaverage
UE1
bps/Hz
QPSK
Cellaverage
UE1
bps/Hz
Hz Hz
a) ManyUEs / cell b) One UE with a good link c) One UE, weak link
Figure 3 Cell Average Spectrum Efficiency during Busy and Quiet Times
Figure 4 shows the resulting cell throughput: Throughput varies little about the ‘busy time mean’ due to the
averaging effect of the many UEs using the network. Surprisingly, it is during the quiet times that peak cell
(and thus backhaul) throughputs will occur, when one UE with a good link has the entire cell to themselves.
8. 8
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 8 of 18
time
Cell Tput
Busy time
Several active UEs
sharing the cell
Quiet time
One UE at a time
Cell Tput = UE Tput
peak
Busy time
mean
For illustration purposes only
peak
Figure 4 Illustration of Cell Throughput during Busy and Quiet Times
2.3. Backhaul Provisioning for User Traffic
Radio spectrum for mobile broadband is an expensive and limited resource, so backhaul should be
generously provisioned to exceed cell throughput in most cases. At the same time, LTE needs to operate at
a significantly lower cost per bit, so operators cannot afford to over-provision either. In this analysis, we
assume backhaul should be provisioned to cope with all but the top 5% of cell throughputs (i.e. the 95%-ile
of the cell throughput distribution).
In practice, last mile provisioning for the peak rate may be influenced by marketing as well as technical
reasons. Comparison of technologies or service offerings across a wide range of conditions is difficult, and
so peak rates are often assumed to be a metric which represents the general performance. Regardless of
whether this assumption is correct or not, the advertised peak rate is still likely to influence the end user’s
choice of network. Last mile provisioning should ensure that the advertised peak rates are at least feasible, if
only rarely achieved in mature networks.
Provisioning for a single cell should be based on the quiet time peak rate of that cell. However, when
provisioning for a Tri-cell eNodeB, or multiple eNodeBs, it is unlikely that the quiet time peaks will occur at
the same time. However, the busy time mean will occur in all cells simultaneously – it’s busy time after all. A
common approach to multi-cell transport provisioning, and that used in this study, is:
Backhaul Provisioning for N cells = max (N x busy time mean, Peak)
Peak cell throughputs are most applicable to the ‘last mile’ of the transport network, for backhauling of a
small number of eNodeBs. Towards the core the traffic of many cells are aggregated together, and the busy
hour mean is the dominant factor.
The backhaul traffic characteristics presented here for mobile broadband networks are different to what has
been experienced in the past with voice networks. A voice call requires a fixed data rate, so backhaul traffic
levels are linked to the number of calls at that time. During busy hour there are more calls, hence more
backhaul traffic. When providing data services, the network aims to serve users as quickly as possible by
maximizing their data rate. As we have seen, even with only one user, the cell can be fully utilized and peak
backhaul rates required.
9. 9
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 9 of 18
2.4. Data points for Mean and Peak Cell Throughput
Ideally and in the future, LTE backhaul provisioning will be based on measurements of real traffic levels in
live commercial networks. However, it will be some time before networks are deployed and operating at full
load. Whilst early trial results have confirmed the single user peak rates are achievable in the field [3], it is
not so easy to create trial conditions representing busy hour. We therefore look to simulation results as the
source of this information for now.
Many LTE simulation studies to date [2,6,7] assume that UEs will continuously download at whatever data
rate they can achieve. This is called the ‘Full buffer’ traffic model. The backhaul provisioning study assumed,
a more sophisticated ‘FTP’ traffic model where each UE downloads a fixed sized file. In the full buffer model,
‘near-in’ UEs with good links consume more data than ‘cell edge’ UEs with lower data rates. Favouring UEs
with good links gives higher UE and Cell throughputs. In the file transfer model, all UEs consume the same
volume of data, regardless of their location or data rate. The transport provisioning study uses simulation
results based on the fixed file transfer traffic model as it is considered to be more representative of real user
traffic.
Other aspects of the simulations such as cell layouts and propagation models are generally consistent with
3GPP case 1 used for LTE development [4]. Full details can be found in NGMN’s Performance Evaluation
Methodology [8]. A summary of key assumptions is as follows:
Urban Environment (Interference limited)
Inter site distance (ISD) 500m
UE Speed: 3km/h
2GHz Path loss model: L=I +37.6*log(R), R in kilometres, I= 128.1 dB for 2 GHz
Multipath model: SCME (urban macro, high spread)
eNodeB antenna type: Cross polar (closely spaced in case of 4x2)
‘Interference limiting’ is when the interference from adjacent cells is significantly higher than thermal noise,
which occurs when cell spacing is small. As cell spacing increases, thermal noise becomes significant for
some users, and the deployment becomes ‘coverage limited’. Interference limited deployments produce
higher cell throughputs than coverage limited deployments. A deployment using an 800MHz carrier can be
interference limited with a larger cell spacing than one at 2GHz. Provided the deployment is interference
limited, the carrier frequency has little impact on cell throughputs – and thus transport provisioning. The
simulation results were for a 2GHz deployment with 500m cell spacing and were found to be interference
limited in both DL and UL. They are therefore considered to be representative of an interference limited
scenario at other carrier frequencies.
10. 10
Next Generation Mobile Networks
LTE Transport Provisioning
2.5. Simulation Results for Peak and Mean Cell Throughput
Figure 5 shows cell throughputs for a variety of downlink and uplink configurations. The peak cell throughput
is based on the 95%-ile user throughput under light network loads corresponding to fewer than one UE per
cell.The uplink peak is around 2-3x the mean, and the downlink peak is 4-6x the mean. These high peak to
mean ratios suggest that significant aggregation gains are available with LTE cell traffic.
0 20 40 60 80 100 120 140
1: 1x2, 10 MHz, category 3 (50 Mbps)
2: 1x2, 20 MHz, category 3 (50 Mbps)
3: 1x2, 20 MHz, category 5 (75 Mbps)
4: 1x2, 20 MHz, category 3 (50 Mbps) MU‐MIMO
5: 1x4, 20 MHz, category 3 (50 Mbps)
1: 2x2, 10 MHz, category 2 (50 Mbps)
2: 2x2, 10 MHz, category 3 (100 Mbps)
3: 2x2, 20 MHz, category 3 (100 Mbps)
4: 2x2, 20 MHz, category 4 (150 Mbps)
5: 4x2, 20 MHz, category 4 (150 Mbps)
UplinkDownlink
Mbps
Quiet time peak
Busy time mean
Figure 5 Mean and Peak (95%-ile) User Plane Traffic per Cell for different LTE Configurations
11. 11
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 11 of 18
3. Single eNodeB Transport Provisioning
S1 Userplane traffic
(for3 cells)
+ControlPlane
+X2 U and C-plane
+OA&M,Sync,etc
+Transportprotocoloverhead
+IPsec overhead(optional)
Core network
RAN
Figure 6 Components of Backhaul Traffic
Backhaul traffic comprises a number of components in addition to the user plane traffic as illustrated in
Figure 6. The optimised backhaul group agreed on the following assumptions:
3.1.1. X2 Traffic
The new X2 interface between eNodeBs is predominantly user traffic forwarded during UE handover
between eNodeBs. Further analysis of X2 functionality and traffic requirements can be found in [12]. The
volume of X2 traffic is often expressed as a volume of S1 traffic, with equipment vendors stating figures of
1.6% [9], 3% [10] and 5% [11]. It was agreed to use 4% as a cautious average of these figures. X2 traffic
only applies to the mean busy time, as the ‘peak’ cell throughput figure can only occur when there is one UE
in good signal conditions – away from where a handover may occur.
It should be noted that the actual volume of traffic depends on the amount of handover, so cells on
motorways for example would see a higher proportion of X2 traffic than an eNodeB covering an office. It was
suggested that an X2 overhead around 10% is appropriate for sites serving highly mobile users. Reference
[11] also describes the ‘batch handover’ scenario, where multiple UEs on a bus or train handover
simultaneously, temporarily causing high levels of X2.
3.1.2. Control Plane, OAM and Synchronisation Signalling
Control Plane Signalling on both S1 (eNodeB to Core) and X2 (eNodeB to eNodeB) is considered to be
negligible in comparison to associated user plane traffic, and can be ignored. The same is true for OAM
(Operations, Administration and Maintenance) and synchronisation signalling.
3.1.3. Transport Protocol Overhead
Backhaul traffic is carried through the Evolved Packet Core in ‘tunnels’, which enable the UE to maintain the
same IP address as it moves between eNodeBs and gateways. LTE uses either GTP (GPRS tunnelling
protocol), which is also used in GSM and UMTS cores, or Mobile IP tunnels. The relative size of the tunnel
overhead depends on the end user’s packet size distribution. Smaller packets (like VoIP) incur larger
overheads. The NGMN backhaul group has assumed an overhead of 10% represents the general case.
3.1.4. IPsec
User plane data on the S1-U interface between the eNodeB and Serving Gateway is not secure, and could
be exposed if the transport network is not physically protected. In many cases, the operator owns their
transport network, and additional security is not needed. However, if user traffic were to traverse a third party
‘untrusted’ network, then it should be protected. In such situations, 3GPP specify IPSec Encapsulated
Security Payload (ESP) in tunnel mode should be used. Unfortunately this adds further overhead to the user
data. The NGMN backhaul group assume IPSec ESP adds an additional 14% on top of the transport
protocol overhead (making 25% in total)
12. 12
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 12 of 18
3.2. Summary of Single eNodeB Traffic
Table 1 shows the calculation of eNodeB backhaul including S1 and X2 user traffic as well as transport and
IPSec overheads. Figure 7 shows a graph of the resulting backhaul traffic per Tricell eNodeB. In most of the
uplink cases, the busy time mean of the three cells is greater than the single cell peak.
Mean Peak overhead 4% overhead 10% overhead 25%
(as load->
infinity)
(95%ile
@ low
load)
busy time
mean
peak
(95%ile)
busy time
mean peak
busy time
mean
peak
(95%ile)
busy time
mean
peak
(95%ile)
DL 1: 2x2, 10 MHz, cat2 (50 Mbps) 10.5 37.8 31.5 37.8 1.3 0 36.0 41.6 41.0 47.3
DL 2: 2x2, 10 MHz, cat3 (100 Mbps) 11.0 58.5 33.0 58.5 1.3 0 37.8 64.4 42.9 73.2
DL 3: 2x2, 20 MHz, cat3 (100 Mbps) 20.5 95.7 61.5 95.7 2.5 0 70.4 105.3 80.0 119.6
DL 4: 2x2, 20 MHz, cat4 (150 Mbps) 21.0 117.7 63.0 117.7 2.5 0 72.1 129.5 81.9 147.1
DL 5: 4x2, 20 MHz, cat4 (150 Mbps) 25.0 123.1 75.0 123.1 3.0 0 85.8 135.4 97.5 153.9
UL 1: 1x2, 10 MHz, cat3 (50 Mbps) 8.0 20.8 24.0 20.8 1.0 0 27.5 22.8 31.2 26.0
UL 2: 1x2, 20 MHz, cat3 (50 Mbps) 15.0 38.2 45.0 38.2 1.8 0 51.5 42.0 58.5 47.7
UL 3: 1x2, 20 MHz, cat5 (75 Mbps) 16.0 47.8 48.0 47.8 1.9 0 54.9 52.5 62.4 59.7
UL 4: 1x2, 20 MHz, cat3 (50
Mbps)*
14.0 46.9 42.0 46.9 1.7 0 48.0 51.6 54.6 58.6
UL 5: 1x4, 20 MHz, cat3 (50 Mbps) 26.0 46.2 78.0 46.2 3.1 0 89.2 50.8 101.4 57.8
Scenario
Tri-cell Tput
Total U-plane + Transport overhead
No IPsec IPsecX2 OverheadSingle Cell Single base station
All values in Mbps
Table 1 Transport Provisioning for Various Configurations of Tri-cell LTE eNodeB
0 20 40 60 80 100 120 140 160
1: 1x2, 10 MHz, category 3 (50 Mbps)
2: 1x2, 20 MHz, category 3 (50 Mbps)
3: 1x2, 20 MHz, category 5 (75 Mbps)
4: 1x2, 20 MHz, category 3 (50 Mbps) MU‐MIMO
5: 1x4, 20 MHz, category 3 (50 Mbps)
1: 2x2, 10 MHz, category 2 (50 Mbps)
2: 2x2, 10 MHz, category 3 (100 Mbps)
3: 2x2, 20 MHz, category 3 (100 Mbps)
4: 2x2, 20 MHz, category 4 (150 Mbps)
5: 4x2, 20 MHz, category 4 (150 Mbps)
UplinkDownlink
Mbps
Quiet time peak
Busy time mean
Figure 7 Busy Time Mean and Quiet Time Peak (95%ile) Backhaul Traffic for a Tricell eNodeB
(No IPsec)
13. 13
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 13 of 18
4. Multi-eNodeB Transport Provisioning
4.1. Principles of Multi-ENodeB Provisioning
0
20
40
60
80
100
120
140
160
0 1 2 3 4
Mbps
1 cell
Peak
Number of eNodeBs = N
Provision for Peak
single cell eNodeBs:
1 2 3 4 5 6 7 8 9 10 11 12
blend
Figure 8 Principles for Provisioning for Multiple eNodeBs
The previous section evaluated the busy time mean and peak backhaul traffic for single cell and tricell
eNodeBs, which is applicable to provisioning of ‘last mile’ backhaul. Figure 8 shows how these figures can
be used to provision backhaul capacity in the ‘aggregation’ and ‘core’ parts of the transport network for any
number of eNodeBs. We consider the correlation between the peak cell throughputs across a number of
aggregated eNodeBs. Figure 8 illustrates two bounds: An upper bound assumes that peak throughputs
occur at the same moment in all cells. This is a worst case scenario, is highly unlikely to occur in practice,
and would be an expensive provisioning strategy. The lower bound assumes peaks are uncorrelated but that
the busy time mean applies to all cells simultaneously. The provisioning for N eNodeBs is therefore the
larger of the single cell peak or N x the busy time mean, thus:
Lower Provisioning Bound for N cells = Max (peak, N x busy time mean,)
This lower bound assumes zero throughputs on all but the cell which is peaking during quiet times. This is
based on the assumption that the peak rates only occur during very light network loads (a single UE per cell,
and little or no interference from neighbouring cells). An improvement on this approach would be to consider
the throughput on all aggregated cells during the quiet time peak. This would produce a curve of the form of
the dotted line labelled ‘blend’ in Figure 8. A yet more conservative approach would be to assume that whilst
one cell is peaking, the others are generating traffic at the mean busy time rate., thus:
Conservative Lower Bound for N cells = Max [peak+(N-1) x busy time mean, N x busy time mean)
Note that the busy time mean figures are taken as the average over 57 cells in the simulation, so any
aggregation benefit for slight variations in mean cell throughput has already been taken into account. When
provisioning for small numbers of eNodeBs, it may be prudent to add a margin to accommodate variations in
cell throughput about the busy time mean.
14. 14
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 14 of 18
4.2. Provisioning for Multiple eNodeBs (No IPsec)
Figure 9 and Figure 10 show transport provisioning for any number of eNodeBs, for downlink and uplink
configurations, respectively. Both log and linear version of the same graph are included to illustrate
provisioning for small and large numbers of eNodeBs.
The x - axis is labelled for the Tricell eNodeBs commonly used to provide macro layer coverage across a
wide area. This scale can easily be converted to represent single cell eNodeBs such as micro and pico cells
used to provide capacity infill.
The provisioning curves comprise a plateau to the left, representing single cell peak, and a linear slope to the
right, with a gradient representing the busy time mean. The plateaux illustrate the benefit of aggregating
small numbers of cells together (up to about 5). For two or more tricell eNodeBs, provisioning is proportional
to the number of eNodeBs, and no further aggregation gains are available. In reality, aggregation gains
depend on the degree of correlation between traffic sources, which in turn depend on the services being
demanded and complex socio-environmental factors. As LTE networks mature, traffic measurements will
become available to help improve understanding in this area.
It can be seen that provisioning is most impacted by the system bandwidth and the MIMO antenna
configuration, whereas UE capability makes little difference.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10
Gbps
Tricell eNodeBs
5: 4x2, 20 MHz, cat4 (150 Mbps)no IPsec
4: 2x2, 20 MHz, cat4 (150 Mbps)no IPsec
3: 2x2, 20 MHz, cat3 (100 Mbps)no IPsec
2: 2x2, 10 MHz, cat3 (100 Mbps)no IPsec
1: 2x2, 10 MHz, cat2 (50 Mbps)no IPsec
0.01
0.1
1
10
100
1000
1 10 100 1000 10000
Gbps
Tricell eNodeBs
single cell eNodeBs:
1 2 3 6 9 12 15 18 21 24 27 30
Figure 9 Downlink Transport Provisioning (No IPsec)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10
Gbps
Tricell eNodeBs
5: 1x4, 20 MHz, cat3 (50 Mbps) no IPsec
4: 1x2, 20 MHz, cat3 (50 Mbps)*no IPsec
3: 1x2, 20 MHz, cat5 (75 Mbps) no IPsec
2: 1x2, 20 MHz, cat3 (50 Mbps) no IPsec
1: 1x2, 10 MHz, cat3 (50 Mbps) no IPsec
0.01
0.1
1
10
100
1000
1 10 100 1000 10000
Gbps
Tricell eNodeBs
single cell eNodeBs:
1 2 3 6 9 12 15 18 21 24 27 30
Figure 10 Uplink Transport Provisioning (No IPsec)
*UL case 4 assumes Multi User MIMO
15. 15
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 15 of 18
4.3. Provisioning with IPsec Encryption
0.1
1
10
100
1000
1 10 100 1000 10000
Gbps
Tricell eNodeBs
single cell eNodeBs:
1 2 3 6 9 12 15 18 21 24 27 30
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10
Gbps
Tricell eNodeBs
4: 2x2, 20 MHz, cat4 (150 Mbps)IPsec
4: 2x2, 20 MHz, cat4 (150 Mbps)no IPsec
2: 2x2, 10 MHz, cat3 (100 Mbps)IPsec
2: 2x2, 10 MHz, cat3 (100 Mbps)no IPsec
Figure 11 Transport Provisioning with IPSec
Figure 11 illustrates the increase in transport provisioning needed for IPsec Encrypted Security Payload, for
two example downlink configurations. According to the overhead assumptions of 25% with and 10% without,
it can be seen that IPsec increases the provisioning requirement by 14%.
16. 16
Next Generation Mobile Networks
LTE Transport Provisioning
5. Interpretation and Adaptation of Results to Real World Networks
There is no ‘one size fits all’ rule for backhaul provisioning and the results presented in this paper should not
be taken out of context. The analysis used in this paper is based on mature macro-cellular LTE networks,
where user traffic demands are sufficient to reach an ‘interference limited’ state on all cells during busy
times. Interference (as opposed to coverage) limited networks are those that have reached full capacity. In
real world networks however, there several factors which impact the actual traffic levels generated by
eNodeBs. The following sections highlight some of these factors and describe their impact on busy time
mean and quiet time peak characteristics. It is recommended that operators take these factors into account
and adapt the mature network provisioning figures to fit their unique deployment conditions.
Last mile
Provisioning
dominated by peak
Aggregation & core
dominated by mean
eNodeBs
Transport
Network
External
Networks
Figure 12 Impact of busy time mean and quiet time peak on different parts of the transport network
Figure 12 shows how different parts of the transport network are impacted by the different characteristics of
the proposed traffic model. The peak tends to be dominant in last mile provisioning, whereas the busy time
mean, because it is assumed to occur simultaneously across the network, impacts provisioning towards the
core.
5.1. Network maturity and device penetration
The eNodeB traffic characteristics represent mature networks, where cells will be simultaneously serving
multiple UEs during busy times. ‘Busy time’ can be viewed as when the offered load from UEs approaches
the cell’s capacity. In the early days after rollout, there may not be sufficient device penetration for this to
occur anywhere in the network. During this period, although ‘busy time’ load may not be reached, the
generally light network loading conditions will still be conducive to achieving high peak rates for the few ‘early
adopter’ UEs. Interpreting this to the backhaul, the last mile will still need to be provisioned for the chosen
peak rate from day one (likely driven by marketing or device capability). On the other hand, provisioning in
the aggregation and core of the transport network can initially be reduced, and then gradually ramped up as
the loading increases towards the levels described in this report.
5.2. Load variation between sites
It has been observed that large proportion of backhaul traffic is generated by small proportion of sites,
suggesting wide variation in traffic levels across the sites. Since the figures in this report assume all cells are
equally busy, they may overestimate traffic levels in the aggregation and core of the transport network. A
network covering a wide area may operate at average cell loads of around 50% of the full loads given in this
report. As previously mentioned, last mile provisioning will be dictated by the quiet time peak rate and which
should be the same for all cells.
5.3. High mobility sites
Sites serving motorways or railway tracks will have higher handover rates than most other sites. As
described in section 3.1.1, this will result in a higher level of mobility signalling over the X2 interface. This
additional overhead applies only to the busy time mean, as peak rates don’t occur during handovers.
5.4. Small or isolated cells
Where cells benefit from some isolation from their neighbours, the reduced levels of interference can lead to
higher levels of backhaul traffic. It is anticipated this may occur in small cells ‘down in the clutter’ near street
level or indoors. An isolated site with no near neighbours will also benefit for the same reasons. As well as
increases to the busy time mean, there will be an increased likelihood of the quiet time peaks occurring at
such sites.
17. 17
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 17 of 18
6. Conclusions
This report proposes a model for predicting traffic levels in transport networks used to backhaul mature, fully
loaded LTE eNodeBs. Guidance is also given on how results can be adapted to suit other conditions, such
as light loading in the early days after roll out. This theoretical approach based on simulations provides a
useful stop gap until real world networks are sufficiently loaded to be able to perform measurements to
characterise backhaul traffic.
Backhaul traffic comprises several components, of which user plane data is by far the largest. This is
evaluated on a per cell basis and there are often multiple cells per eNodeB. LTE network simulations
revealed the characteristics of cell throughput: During busy times, the many users sharing the cell have an
averaging effect, and cell throughput is characterised by the cell average spectral efficiency. Surprisingly, it is
during quiet times that the highest cell throughputs occur, when one UE with a good radio link has the entire
cell’s spectrum resource to itself. A typical 2x2 10MHz cell provides up to 11Mbps of downlink user traffic
during busy times, but during quiet times can supply an individual user with up to 59Mbps. This peak rate
represents that achieved by the top 5% of users in a simulation with a low offered load. In practice, peak
provisioning might also be influenced by the need to advertise a particular rate to attract consumers.
The backhaul traffic for eNodeB contains user data for one or more cells, plus traffic forwarded over the “X2”
interface during handovers, plus overheads for transport protocols and security. Signalling for control plane,
system management and synchronisation were assumed to be negligible. When calculating traffic
provisioning for multiple eNodeBs, it is assumed that the quiet time peaks do not occur at the same moment
across all eNodeBs, but that the busy time mean traffic does.
Figure 13 shows transport provisioning curves for the ‘vanilla’ LTE with 2x2 downlink and 1x2 uplink
configurations for both 10MHz and 20MHz system bandwidths. X-axis scales are given for both tricell and
single cell eNodeBs. Provisioning curves for other eNodeB configurations are given in the report. IPsec
encryption would increase these provisioning figures by 14%. Curves in Figure 13 represent a general case
for fully loaded eNodeBs. Actual traffic levels for individual eNodeBs may vary about these levels depending
on the deployment scenario and loading level.
Single cell eNBs:
1 2 3 6 9 12 15 18 21 24 27 30
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3 4 5 6 7 8 9 10
ProvisioningGbps
Tricell eNodeBs
DL 2x2, 20MHz
UL 1x2 20MHz
DL 2x2, 10MHz
UL 1x2 10MHz
Figure 13 LTE Transport Provisioning for Downlink and Uplink (no IPsec)
The degree of traffic aggregation is smallest in the ‘last mile’ of the transport network, and greatest in the
‘core’. Since the ‘last mile’ typically backhauls only a small number of eNodeBs, provisioning tends to be
dominated by the peak rate required individual cells. Towards the ‘core’ it is the busy time mean rate
occurring simultaneously across all cells which determines provisioning.
Overall, this study shows that although LTE is capable of generating some very high peak rates, when the
traffic of multiple cells and/or eNodeBs are aggregated together, the transport provisioning requirements are
quite reasonable.
18. 18
Next Generation Mobile Networks
LTE Transport Provisioning
NGMN Confidential 18 of 18
7. References
[1] “Requirements for Evolved Universal Terrestrials Radio Access Network”, 3GPP Specification 25.913,
http://www.3gpp.org/ftp/Specs/html-info/25913.htm
[2] “LS on LTE performance verification work”, 3GPP document R1-072580, May 2007,
http://www.3gpp.org/ftp/tsg_ran/WG1_RL1/TSGR1_49/Docs/R1-072580.zip
[3] “Latest results from the LTE/SAE Trial Initiative”, February 2009
http://www.lstiforum.org/file/news/Latest_LSTI_Results_Feb09_v1.pdf
[4] 3GPP TR 25.815 v7.1.1, Physical layer aspects for evolved Universal Terrestrial Radio Access
(UTRA), Sept. 2006.
[5] “The LTE/SAE Trial Initiative”: www.lstiforum.com
[6] “Summary of Downlink Performance Evaluation”, 3GPP document R1-072578, May 2007.
[7] “NGMN TE WP1 Radio Performance Evaluation Phase 2 Report” v1.3 5/3/08
[8] “NGMN Radio Access Performance Evaluation Methodology”, v1, Jan 2008,
http://www.ngmn.org/nc/downloads/techdownloads.html
[9] “Right Sizing RAN Transport Requirements”, Ericsson Presentation, Transport Networks for Mobile
Operators, 2010
[10] “LTE requirements for bearer networks”, Huawei Publications, June 2009,
http://www.huawei.com/publications/view.do?id=5904&cid=10864&pid=61
[11] “Sizing X2 Bandwidth For Inter-Connected eNodeBs”, I. Widjaja and H. La Roche, Bell Labs, Alcatel-
Lucent
[12] “Backhauling X2”, Cambridge Broadband Networks , Dec 2010,
http://www.cbnl.com/product/whitepapers/