This document presents research on optimizing pilot symbol power allocation in LTE systems. The key points are:
1) The researchers derive analytical expressions for optimal pilot symbol power allocation based on maximizing post-equalization signal-to-interference-and-noise ratio (SINR) under imperfect channel knowledge.
2) They analyze zero-forcing equalization and derive the post-equalization SINR expression for a MIMO system with imperfect channel estimation.
3) The researchers also derive mean square error expressions for least squares and linear minimum mean square error channel estimation methods.
4) Simulation results using an LTE simulator validate the analytical model for optimal pilot symbol power allocation.
This document provides guidelines for optimizing LTE radio frequency (RF) networks. It describes the network optimization process, including single site verification and RF optimization. RF optimization aims to control pilot pollution while optimizing coverage, signal quality, and handover success rates. The document discusses LTE RF optimization objectives such as RSRP, SINR, and handover success rate. It also covers troubleshooting coverage issues like weak coverage, lack of a dominant cell, and cross coverage. Optimization methods include adjusting antenna parameters, transmit power, and network configuration parameters.
This document provides an overview of tests for installing and maintaining LTE eNodeB base stations. It describes the key tests to check characteristics like downlink and uplink speeds, channel bandwidths, frequency bands, frame structure, and modulation schemes. The document then explains specific tests to check aspects like transmission power, occupied bandwidth, spectrum emission mask, ACLR, spurious emissions, and modulation quality of control and data channels. It provides procedures for configuring a tester and interpreting results for each test.
This document provides an overview of 5G technologies and discusses key areas of research and development. It begins by looking back at early 5G research from 2003 and the maturation of 4G standards. It then explores several candidate 5G technologies including massive MIMO, device-to-device communication, and network function virtualization. The document also discusses efforts towards developing 5G by various groups and the challenges of developing a comprehensive 5G vision given the early stage of research. Energy efficiency and a potential 90% reduction in network energy consumption by 2020 is highlighted as an important focus area.
The document summarizes radio frequency aspects of 3GPP Release 10 LTE-Advanced technology. Key points discussed include operating bands and transmission bandwidth configurations up to 100MHz supported by carrier aggregation. Feasibility studies covered aspects like UE and base station transmitter/receiver architectures, power levels and emissions for supporting wider channel bandwidths through multiple component carriers. Radio resource management requirements were also addressed to ensure good mobility performance across networks utilizing LTE-Advanced.
This document contains a resume for Sagar Maruti Kuchekar. It includes details about his education, work experience in telecom domains like LTE, GSM and device testing over 3 years, and skills with technologies such as RF planning and optimization. It also lists his academic qualifications, past employers and roles, including as an RF planning and optimization engineer, and responsibilities carried out in each role.
LTE uses various frequency bands and duplexing techniques to provide high-speed data and peak download speeds of up to 300 Mbps. It supports mobility of up to 350 km/h and uses advanced technologies like OFDM, SC-FDMA, MIMO and turbo coding to achieve low latency and high bandwidth. LTE specifications define channel bandwidths of 1.4, 3, 5, 10, 15 and 20 MHz with modulation schemes of QPSK, 16QAM and 64QAM.
Factors affecting lte throughput and calculation methodologyAbhijeet Kumar
This document discusses LTE throughput calculation and application in wireless rollout projects. It provides a history of LTE development and commercialization. It then explains factors that impact LTE throughput calculations including frequency bandwidth, resource blocks, modulation schemes, coding rates, UE categories, and MIMO capabilities. The document demonstrates calculations for theoretical peak throughput in different scenarios and factors that should be considered in LTE network planning and deployment projects.
This document provides guidelines for LTE radio frequency (RF) network optimization. It describes the network optimization process including single site verification and RF optimization. Key aspects of RF optimization covered include preparing for optimization by collecting data, analyzing problems related to coverage, signal quality and handover success rate, and adjusting parameters like transmit power, antenna tilts and neighboring cell configurations. Common issues addressed are weak coverage, coverage holes, lack of a dominant cell, and cross coverage between cells. Optimization methods and specific cases are presented to resolve different problems.
This document provides guidelines for optimizing LTE radio frequency (RF) networks. It describes the network optimization process, including single site verification and RF optimization. RF optimization aims to control pilot pollution while optimizing coverage, signal quality, and handover success rates. The document discusses LTE RF optimization objectives such as RSRP, SINR, and handover success rate. It also covers troubleshooting coverage issues like weak coverage, lack of a dominant cell, and cross coverage. Optimization methods include adjusting antenna parameters, transmit power, and network configuration parameters.
This document provides an overview of tests for installing and maintaining LTE eNodeB base stations. It describes the key tests to check characteristics like downlink and uplink speeds, channel bandwidths, frequency bands, frame structure, and modulation schemes. The document then explains specific tests to check aspects like transmission power, occupied bandwidth, spectrum emission mask, ACLR, spurious emissions, and modulation quality of control and data channels. It provides procedures for configuring a tester and interpreting results for each test.
This document provides an overview of 5G technologies and discusses key areas of research and development. It begins by looking back at early 5G research from 2003 and the maturation of 4G standards. It then explores several candidate 5G technologies including massive MIMO, device-to-device communication, and network function virtualization. The document also discusses efforts towards developing 5G by various groups and the challenges of developing a comprehensive 5G vision given the early stage of research. Energy efficiency and a potential 90% reduction in network energy consumption by 2020 is highlighted as an important focus area.
The document summarizes radio frequency aspects of 3GPP Release 10 LTE-Advanced technology. Key points discussed include operating bands and transmission bandwidth configurations up to 100MHz supported by carrier aggregation. Feasibility studies covered aspects like UE and base station transmitter/receiver architectures, power levels and emissions for supporting wider channel bandwidths through multiple component carriers. Radio resource management requirements were also addressed to ensure good mobility performance across networks utilizing LTE-Advanced.
This document contains a resume for Sagar Maruti Kuchekar. It includes details about his education, work experience in telecom domains like LTE, GSM and device testing over 3 years, and skills with technologies such as RF planning and optimization. It also lists his academic qualifications, past employers and roles, including as an RF planning and optimization engineer, and responsibilities carried out in each role.
LTE uses various frequency bands and duplexing techniques to provide high-speed data and peak download speeds of up to 300 Mbps. It supports mobility of up to 350 km/h and uses advanced technologies like OFDM, SC-FDMA, MIMO and turbo coding to achieve low latency and high bandwidth. LTE specifications define channel bandwidths of 1.4, 3, 5, 10, 15 and 20 MHz with modulation schemes of QPSK, 16QAM and 64QAM.
Factors affecting lte throughput and calculation methodologyAbhijeet Kumar
This document discusses LTE throughput calculation and application in wireless rollout projects. It provides a history of LTE development and commercialization. It then explains factors that impact LTE throughput calculations including frequency bandwidth, resource blocks, modulation schemes, coding rates, UE categories, and MIMO capabilities. The document demonstrates calculations for theoretical peak throughput in different scenarios and factors that should be considered in LTE network planning and deployment projects.
This document provides guidelines for LTE radio frequency (RF) network optimization. It describes the network optimization process including single site verification and RF optimization. Key aspects of RF optimization covered include preparing for optimization by collecting data, analyzing problems related to coverage, signal quality and handover success rate, and adjusting parameters like transmit power, antenna tilts and neighboring cell configurations. Common issues addressed are weak coverage, coverage holes, lack of a dominant cell, and cross coverage between cells. Optimization methods and specific cases are presented to resolve different problems.
The document discusses various parameters used in LTE drive testing including:
- RSRP, RSRQ, SINR, RSSI, CQI, PCI, BLER, and throughput which provide information on signal strength, quality, and performance. Phone-based drive testing allows monitoring of these parameters and correlation with data performance. MIMO and handovers between LTE and other technologies can also be evaluated. Key metrics include coverage, capacity, and end-user experience.
To meet customers' requirements for high-quality networks, LTE trial networks must be optimized during and after project implementation. Radio frequency (RF) optimization is necessary in the entire optimization process. This document provides guidelines on network optimization for network planning and optimization personnel.
Wcdma dt analysis using TEMS InvestigationMichael Ofili
The document discusses drive test and call quality test procedures for analyzing mobile network coverage and service performance. It provides an introduction to the test tools used, including phones and data cards that support UMTS, HSDPA, HSUPA, EDGE, and GPRS technologies. Key metrics analyzed include signal strength, quality, throughput rates, call setup success, handover success, call drops, and delays. Issues examined include overshooting, pilot pollution, and missing neighbors. Potential solutions involve adjusting antenna parameters, modifying configuration settings, and optimizing base station placement and power levels.
The document discusses several topics related to LTE cell planning including:
1. The general LTE cell planning process includes information collection, pre-planning, detailed planning, and cell planning which focuses on frequency, tracking area (TA), physical cell ID (PCI), and physical random access channel (PRACH) planning.
2. There are several new frequency bands for LTE including 700MHz, AWS, 2.6GHz, and reusing existing GSM bands.
3. Topics like interference coordination (ICIC), TA planning to reduce signaling, PCI planning requirements, cyclic prefix impact on symbol energy, and PRACH parameters and configurations are covered.
The document provides information about telecommunication training. It defines telecommunications as the transmission of information over distances through electronic means. It notes that India has over 920 million telecom subscribers. It outlines some of the job roles in the telecom industry such as network planning, deployment, operations and maintenance, and equipment testing. It states that the training will prepare candidates for these types of roles by providing them with practical skills in areas like 5G, 4G, RF design, and optimization.
The document summarizes radio frequency aspects of 3GPP Release 10 LTE-Advanced technology. Key points discussed include expanded channel bandwidth up to 100MHz enabled by carrier aggregation, operating bands beyond initial LTE bands, deployment scenarios, and considerations for UE and base station transmissions and receptions to support wider channel widths through multiple component carriers. Feasibility studies are needed to establish radio transmission and reception specifications as well as radio resource management for LTE-Advanced.
This document outlines Huawei's radio network planning and optimization work flow. It includes processes for pre-planning, network planning, optimization, and network acceptance. The network planning process includes RF surveys, planning concepts, and practical planning. Optimization includes drive test optimization, KPI optimization, and network monitoring. The document provides details on each stage of the process and refers to other documents for more specific guidelines. It is intended to standardize and improve the workflow for radio network planning.
This document discusses random access channel self-tuning in LTE networks. It begins with an introduction to random access in LTE, including its uses and procedures. It then discusses self-optimizing networks and random access channel optimization as a use case. Key performance metrics for random access channel are identified and experiments are described that tune parameters to measure their effects on performance and interference. The conclusion emphasizes that random access channel performance is heavily dependent on design parameters and encourages self-optimizing networks research.
The key performance indicators for measuring 3G cell performance include accessibility metrics like RRC success rate, RAB success rate, and CSSR. Retainability is measured by dropped call rates for speech, video, and packet switched connections. Mobility is measured by handover success rates between cells and between 3G and 2G networks. Factors that affect HSDPA throughput include downlink power, the number of downlink codes allocated for HSDPA, and transport channel capacity. Tuning parameters like increasing the number of HSDPA codes or changing the scheduling algorithm can improve HSDPA throughput.
This document discusses optimization of GSM networks through adjusting various network parameters. It covers topics such as single band optimization philosophy, the network optimization process, optimization phases, BSS optimization parameters like cell selection and reselection, power control, and handover control. Drive testing and analysis are also involved in the optimization process.
1) The document describes key performance indicators (KPIs) for measuring the performance of an LTE radio network. It discusses KPIs related to accessibility, retainability, mobility, and latency.
2) Accessibility KPIs measure aspects like call setup success rate, RRC setup success rate, and E-RAB setup success rate. Retainability KPIs measure call drop rate and call setup completion rate. Mobility KPIs measure handover success rates within LTE and between LTE and other technologies.
3) For each KPI, the document provides a definition, calculation formula, and description of which network events and counters are needed to measure the KPI. Baseline
This document discusses jitter, latency, and delay in network communications. It provides definitions and explanations of these terms:
1. Jitter is the variation in the delay of received packets caused by network congestion, queuing, or errors, rather than packets being transmitted at an even pace. This can cause gaps in audio if packets are missing.
2. Delay and latency refer to the time it takes a bit to be transmitted from source to destination. Jitter is a type of delay that varies over time.
3. Solutions to reduce jitter include increasing the receive jitter buffer size and delay, using larger RTP packets, and lowering audio quality.
The document introduces LTE network planning and RNP solutions. It discusses the flat LTE network architecture and protocols including OFDM and MIMO. LTE network planning includes coverage and capacity planning using link budget and capacity estimation. The RNP solution introduces tools for performance enhancement like interference avoidance and co-antenna analysis.
The document discusses optimization of 3G radio networks, focusing on the RF Optimization phase. It describes the various stages of network optimization including single site verification, RF optimization of clusters of sites, parameter optimization testing, and ongoing reference route testing and analysis. The RF Optimization process involves preparing clusters and drive routes, analyzing data to identify issues, determining solutions such as antenna adjustments, implementing changes, and retesting. Analysis approaches discussed include examining cell dominance, coverage, interference, uplink coverage, pilot pollution, neighbor lists, soft handover performance, and drop calls.
The document discusses radio frequency (RF) network planning and optimization. It describes the responsibilities of RF planners, which include designing site plans and frequency plans. It also describes the responsibilities of RF optimization personnel, which include maintaining network performance metrics and studying new features. The document outlines training courses on RF network planning and optimization, covering topics like coverage, capacity, frequency planning, optimization features and parameters, and key performance indicator monitoring.
TEMS tools are used at various stages of radio network design, rollout, operation and improvement. During the design and rollout phase, TEMS is used for network integration testing, initial tuning, and GPRS performance verification. In the operation and improvement phase, it is used for traditional optimization and network feature optimization. TEMS allows measurement of key performance indicators, analysis of issues like low signal strength, interference, handover problems and call setup failures. It helps identify root causes and evaluate potential solutions.
This document discusses key factors impacting LTE network performance including expected performance metrics, dependencies, and challenges. It provides an overview of call setup times and throughputs expected under ideal conditions, then discusses how factors like deployment issues, RF interference, backhaul limitations, scheduler configuration, and mobility parameters can negatively influence performance and result in increased call setup times, lower throughputs, and handover failures. The document aims to help network operators identify areas to focus on for optimizing LTE network performance at launch.
The document contains questions and answers related to GSM and LTE drive test parameters. It discusses key topics like reference signal receive power (RSRP), reference signal receive quality (RSRQ), signal to noise ratio (SINR), received signal strength indicator (RSSI), physical cell ID (PCI), channel quality indicator (CQI), block error rate (BLER), downlink and uplink throughput, and WCDMA/3G questions and answers related to link budget, TMA, processing gain, and calculating maximum number of users.
This document provides an overview and technical details regarding beamforming and sounding reference signal optimization for LTE. It discusses sector beamforming for common channels using weighted factors. It compares RL15 single-stream beamforming (TM7) to RL25 dual-stream beamforming (TM8), describing their implementations. The document also covers sounding reference signal configurations, including hopping patterns and parameters. Performance results and configuration parameters for beamforming are presented.
1. The document proposes a scheme called Superimposed Spatial Modulation (SSM) that allows for doubling of information conveyed in the spatial position of transmitting antennas.
2. SSM superimposes two independent spatial modulation systems on the same antenna array. Each system transmits different components of orthogonal symbols.
3. This approach increases spectral efficiency over conventional spatial modulation by doubling the size of the possible antenna constellations.
In this paper, we analyzed a numerical evaluation of the performance of MIMO radio systems in the LTE network environment. Downlink physical layer of the OFDM-MIMO based radio interface is considered for system model and a theoretical analysis of the bit error rate of the two space-time codes (SFBC 2×1 and FSTD 4×2 codes are adopted by the LTE norm as a function of the signal to noise ratio. Analytical expressions are given for transmission over a Rayleigh channel without spatial correlation which is then compared with Monte-Carlo simulations. Further evaluated channel capacity and simulation results show throughput almost reaches to the capacity limit.
Comparative Analysis of Distortive and Non-Distortive Techniques for PAPR Red...IDES Editor
OFDM is a popular and widely accepted modulation
and multiplexing technique in the area of wireless
communication. IEEE 802.15, a wireless specification defined
for WPAN is an emerging wireless technology for short range
multimedia applications. Two general categories of 802.15
are the low rate 802.15.4 (ZigBee) and high rate 802.15.3
(UWB). In their physical (PHY) layer design, OFDM is a
competing technique due to the various advantages it renders
in the practical wireless media. OFDM has been a popular
technique for many years and adopted as the core technique
in a number of wireless standards. It makes the system more
immune to interference like InterSymbol Interference (ISI)
and InterCarrier Interference (ICI) and dispersive effects of
the channel. It is also a spectrally efficient scheme since the
spectra of the signal are overlapping in nature. Despite these
advantages OFDM suffers from a serious problem of high
Peak to Average Power. This limits the system’s capabilities
and increases the complexity. This paper compares the signal
distortion technique of Amplitude Clipping and the
distortionless technique of SLM for Peak to Average Power
reduction
The document discusses various parameters used in LTE drive testing including:
- RSRP, RSRQ, SINR, RSSI, CQI, PCI, BLER, and throughput which provide information on signal strength, quality, and performance. Phone-based drive testing allows monitoring of these parameters and correlation with data performance. MIMO and handovers between LTE and other technologies can also be evaluated. Key metrics include coverage, capacity, and end-user experience.
To meet customers' requirements for high-quality networks, LTE trial networks must be optimized during and after project implementation. Radio frequency (RF) optimization is necessary in the entire optimization process. This document provides guidelines on network optimization for network planning and optimization personnel.
Wcdma dt analysis using TEMS InvestigationMichael Ofili
The document discusses drive test and call quality test procedures for analyzing mobile network coverage and service performance. It provides an introduction to the test tools used, including phones and data cards that support UMTS, HSDPA, HSUPA, EDGE, and GPRS technologies. Key metrics analyzed include signal strength, quality, throughput rates, call setup success, handover success, call drops, and delays. Issues examined include overshooting, pilot pollution, and missing neighbors. Potential solutions involve adjusting antenna parameters, modifying configuration settings, and optimizing base station placement and power levels.
The document discusses several topics related to LTE cell planning including:
1. The general LTE cell planning process includes information collection, pre-planning, detailed planning, and cell planning which focuses on frequency, tracking area (TA), physical cell ID (PCI), and physical random access channel (PRACH) planning.
2. There are several new frequency bands for LTE including 700MHz, AWS, 2.6GHz, and reusing existing GSM bands.
3. Topics like interference coordination (ICIC), TA planning to reduce signaling, PCI planning requirements, cyclic prefix impact on symbol energy, and PRACH parameters and configurations are covered.
The document provides information about telecommunication training. It defines telecommunications as the transmission of information over distances through electronic means. It notes that India has over 920 million telecom subscribers. It outlines some of the job roles in the telecom industry such as network planning, deployment, operations and maintenance, and equipment testing. It states that the training will prepare candidates for these types of roles by providing them with practical skills in areas like 5G, 4G, RF design, and optimization.
The document summarizes radio frequency aspects of 3GPP Release 10 LTE-Advanced technology. Key points discussed include expanded channel bandwidth up to 100MHz enabled by carrier aggregation, operating bands beyond initial LTE bands, deployment scenarios, and considerations for UE and base station transmissions and receptions to support wider channel widths through multiple component carriers. Feasibility studies are needed to establish radio transmission and reception specifications as well as radio resource management for LTE-Advanced.
This document outlines Huawei's radio network planning and optimization work flow. It includes processes for pre-planning, network planning, optimization, and network acceptance. The network planning process includes RF surveys, planning concepts, and practical planning. Optimization includes drive test optimization, KPI optimization, and network monitoring. The document provides details on each stage of the process and refers to other documents for more specific guidelines. It is intended to standardize and improve the workflow for radio network planning.
This document discusses random access channel self-tuning in LTE networks. It begins with an introduction to random access in LTE, including its uses and procedures. It then discusses self-optimizing networks and random access channel optimization as a use case. Key performance metrics for random access channel are identified and experiments are described that tune parameters to measure their effects on performance and interference. The conclusion emphasizes that random access channel performance is heavily dependent on design parameters and encourages self-optimizing networks research.
The key performance indicators for measuring 3G cell performance include accessibility metrics like RRC success rate, RAB success rate, and CSSR. Retainability is measured by dropped call rates for speech, video, and packet switched connections. Mobility is measured by handover success rates between cells and between 3G and 2G networks. Factors that affect HSDPA throughput include downlink power, the number of downlink codes allocated for HSDPA, and transport channel capacity. Tuning parameters like increasing the number of HSDPA codes or changing the scheduling algorithm can improve HSDPA throughput.
This document discusses optimization of GSM networks through adjusting various network parameters. It covers topics such as single band optimization philosophy, the network optimization process, optimization phases, BSS optimization parameters like cell selection and reselection, power control, and handover control. Drive testing and analysis are also involved in the optimization process.
1) The document describes key performance indicators (KPIs) for measuring the performance of an LTE radio network. It discusses KPIs related to accessibility, retainability, mobility, and latency.
2) Accessibility KPIs measure aspects like call setup success rate, RRC setup success rate, and E-RAB setup success rate. Retainability KPIs measure call drop rate and call setup completion rate. Mobility KPIs measure handover success rates within LTE and between LTE and other technologies.
3) For each KPI, the document provides a definition, calculation formula, and description of which network events and counters are needed to measure the KPI. Baseline
This document discusses jitter, latency, and delay in network communications. It provides definitions and explanations of these terms:
1. Jitter is the variation in the delay of received packets caused by network congestion, queuing, or errors, rather than packets being transmitted at an even pace. This can cause gaps in audio if packets are missing.
2. Delay and latency refer to the time it takes a bit to be transmitted from source to destination. Jitter is a type of delay that varies over time.
3. Solutions to reduce jitter include increasing the receive jitter buffer size and delay, using larger RTP packets, and lowering audio quality.
The document introduces LTE network planning and RNP solutions. It discusses the flat LTE network architecture and protocols including OFDM and MIMO. LTE network planning includes coverage and capacity planning using link budget and capacity estimation. The RNP solution introduces tools for performance enhancement like interference avoidance and co-antenna analysis.
The document discusses optimization of 3G radio networks, focusing on the RF Optimization phase. It describes the various stages of network optimization including single site verification, RF optimization of clusters of sites, parameter optimization testing, and ongoing reference route testing and analysis. The RF Optimization process involves preparing clusters and drive routes, analyzing data to identify issues, determining solutions such as antenna adjustments, implementing changes, and retesting. Analysis approaches discussed include examining cell dominance, coverage, interference, uplink coverage, pilot pollution, neighbor lists, soft handover performance, and drop calls.
The document discusses radio frequency (RF) network planning and optimization. It describes the responsibilities of RF planners, which include designing site plans and frequency plans. It also describes the responsibilities of RF optimization personnel, which include maintaining network performance metrics and studying new features. The document outlines training courses on RF network planning and optimization, covering topics like coverage, capacity, frequency planning, optimization features and parameters, and key performance indicator monitoring.
TEMS tools are used at various stages of radio network design, rollout, operation and improvement. During the design and rollout phase, TEMS is used for network integration testing, initial tuning, and GPRS performance verification. In the operation and improvement phase, it is used for traditional optimization and network feature optimization. TEMS allows measurement of key performance indicators, analysis of issues like low signal strength, interference, handover problems and call setup failures. It helps identify root causes and evaluate potential solutions.
This document discusses key factors impacting LTE network performance including expected performance metrics, dependencies, and challenges. It provides an overview of call setup times and throughputs expected under ideal conditions, then discusses how factors like deployment issues, RF interference, backhaul limitations, scheduler configuration, and mobility parameters can negatively influence performance and result in increased call setup times, lower throughputs, and handover failures. The document aims to help network operators identify areas to focus on for optimizing LTE network performance at launch.
The document contains questions and answers related to GSM and LTE drive test parameters. It discusses key topics like reference signal receive power (RSRP), reference signal receive quality (RSRQ), signal to noise ratio (SINR), received signal strength indicator (RSSI), physical cell ID (PCI), channel quality indicator (CQI), block error rate (BLER), downlink and uplink throughput, and WCDMA/3G questions and answers related to link budget, TMA, processing gain, and calculating maximum number of users.
This document provides an overview and technical details regarding beamforming and sounding reference signal optimization for LTE. It discusses sector beamforming for common channels using weighted factors. It compares RL15 single-stream beamforming (TM7) to RL25 dual-stream beamforming (TM8), describing their implementations. The document also covers sounding reference signal configurations, including hopping patterns and parameters. Performance results and configuration parameters for beamforming are presented.
1. The document proposes a scheme called Superimposed Spatial Modulation (SSM) that allows for doubling of information conveyed in the spatial position of transmitting antennas.
2. SSM superimposes two independent spatial modulation systems on the same antenna array. Each system transmits different components of orthogonal symbols.
3. This approach increases spectral efficiency over conventional spatial modulation by doubling the size of the possible antenna constellations.
In this paper, we analyzed a numerical evaluation of the performance of MIMO radio systems in the LTE network environment. Downlink physical layer of the OFDM-MIMO based radio interface is considered for system model and a theoretical analysis of the bit error rate of the two space-time codes (SFBC 2×1 and FSTD 4×2 codes are adopted by the LTE norm as a function of the signal to noise ratio. Analytical expressions are given for transmission over a Rayleigh channel without spatial correlation which is then compared with Monte-Carlo simulations. Further evaluated channel capacity and simulation results show throughput almost reaches to the capacity limit.
Comparative Analysis of Distortive and Non-Distortive Techniques for PAPR Red...IDES Editor
OFDM is a popular and widely accepted modulation
and multiplexing technique in the area of wireless
communication. IEEE 802.15, a wireless specification defined
for WPAN is an emerging wireless technology for short range
multimedia applications. Two general categories of 802.15
are the low rate 802.15.4 (ZigBee) and high rate 802.15.3
(UWB). In their physical (PHY) layer design, OFDM is a
competing technique due to the various advantages it renders
in the practical wireless media. OFDM has been a popular
technique for many years and adopted as the core technique
in a number of wireless standards. It makes the system more
immune to interference like InterSymbol Interference (ISI)
and InterCarrier Interference (ICI) and dispersive effects of
the channel. It is also a spectrally efficient scheme since the
spectra of the signal are overlapping in nature. Despite these
advantages OFDM suffers from a serious problem of high
Peak to Average Power. This limits the system’s capabilities
and increases the complexity. This paper compares the signal
distortion technique of Amplitude Clipping and the
distortionless technique of SLM for Peak to Average Power
reduction
Capacity Enhancement of MIMO-OFDM System in Rayleigh Fading ChannelIOSR Journals
MIMO-OFDM system in Rayleigh Fading Channel is very popular technique for mobile
communication now a day’s for research. Here we want increase the capacity of MIMO-OFDM of system by
using adaptive modulation, Algebraic Space-Time Codes (ASTC) encoder for MIMO Systems are based on
quaternion algebras .we found that ergodic capacity has some limitation which reduce the system’s
performance to overcome this we use ASTC code . ASTC code are full rank, full rate and non vanishing constant
minimum determinant for increasing spectral efficiency and reducing Peak to Average Power Ratio (PAPR) .
Capacity Enhancement of MIMO-OFDM System in Rayleigh Fading ChannelIOSR Journals
Abstract: MIMO-OFDM system in Rayleigh Fading Channel is very popular technique for mobile communication now a day’s for research. Here we want increase the capacity of MIMO-OFDM of system by using adaptive modulation, Algebraic Space-Time Codes (ASTC) encoder for MIMO Systems are based on quaternion algebras .we found that ergodic capacity has some limitation which reduce the system’s performance to overcome this we use ASTC code . ASTC code are full rank, full rate and non vanishing constant minimum determinant for increasing spectral efficiency and reducing Peak to Average Power Ratio (PAPR) . Keywords— Adaptive modulation ASTC code, Capacity, BER, Ergodic capacity, PAPR, Spectral Efficiency and SNR
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Optical Spatial Modulation with Transmitter-Receiver AlignmentsMarwan Hammouda
This paper proposes an optical spatial modulation (OSM) technique to enhance the data rate of indoor optical wireless communication systems. OSM works by activating only one out of multiple light emitting diodes at each time instant to transmit data. The paper shows that properly aligning the positions and orientations of the transmit and receive units can significantly improve the performance of OSM by decorrelating the optical MIMO channel. Through alignment, the paper achieves a 14 dB gain in signal-to-noise ratio required for a bit-error rate of 10^-3 compared to misaligned setups. The paper also compares the power and bandwidth efficiency of OSM to on-off keying, pulse position modulation, and pulse amplitude modulation.
Analysis of Space Time Codes Using Modulation TechniquesIOSR Journals
Abstract: In this Paper, Analysis of channel codes for improving the data rate and reliability of communication over fading channels using multiple transmit antennas has been considered. The codes, namely ’Space Time Codes’ render full diversity and amend coding gain. Performance criteria for designing such codes, under this assumption that the fading is slow and nonselective frequency, is also analysed. Under this research, Study of Frame Error Rate(FER) and outage capacity is compared for different no. Of transmit and receive antennas as well as for different modulation techniques. According to theoretical results FER decreases with increasing SNR and No. Of receiving antennas. Numerical and practical result shows that FER decreases with increasing SNR and no. Of receiving antennas. Keywords: Space time Block Codes ,Space time trellis Codes,Frame Error Rate(FER),Outage capacity,Pairwise Error Probability
CS Based Channel Estimation for OFDM Systems under Long Delay Channels Using ...IJERA Editor
Orthogonal frequency division multiplexing (OFDM) is a technique which are used in the next-generation wireless communication. Channel estimation in the OFDM technique is one of the big challenges, ever since high-resolution channel estimation can significantly improve the equalization at the receiver and consequently enhance the communication performances. Channel computation using superimposed pilot sequences is also a fully new area, idea for using superimposed pilot sequences has been proposed by various authors for different applications. In this paper, we are introduced a high accurate, low complexity compressive sensing (CS) based channel estimation namely Auxiliary information based Subspace Pursuit (ASP) in TFT-OFDM systems. ASP based channel estimation in TFT-OFDM system is based on two steps. First is, by exploiting the signal structure of recently proposed TDM-OFDM scheme, the supporting channel information is obtained. Second is, we propose the supporting information based subspace pursuit (SP) algorithm to use a very small amount of frequency domain pilots embedded in the OFDM block used for the exact channel estimation. Moreover, the obtained auxiliary channel information is adopted to reduce the complexity of the conventional SP algorithm. Simulation results demonstrate a important reduction of the number of pilots relative to least-squares channel estimation and supporting high-order modulations like 256 QAM.
Analyses and performance of techniques papr reduction for stbc mimo ofdm syst...ijwmn
An OFDM system is combined with multiple-input mult
iple-output (MIMO) in order to increase the
diversity gain and system capacity over the time va
riant frequency-selective channels. However, a maj
or
drawback of MIMO-OFDM system is that the transmitte
d signals on different antennas might exhibit high
peak-to-average power ratio (PAPR).In this paper, w
e present a PAPR analysis reduction of space-time-
block-coded (STBC) MIMO-OFDM system for 4G wireless
networks. Several techniques have been used to
reduce the PAPR of the (STBC) MIMOOFDM system: clip
ping and filtering, partial transmit sequence
(PTS) and selected mapping (SLM). Simulation result
s show that clipping and filtering provides a bette
r
PAPR reduction than the others methods and only SLM
technique conserve the PAPR reduction in
reception part of signal.
IMPROVEMENT OF LTE DOWNLINK SYSTEM PERFORMANCES USING THE LAGRANGE POLYNOMIAL...IJCNCJournal
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Similar to Optimal pilot symbol power allocation in lte (20)
1. Copyright 2011 IEEE. Published in the proceedings of VTC-Fall 2011, San Francisco, CA, USA, 2011.
Optimal Pilot Symbol Power Allocation in LTE
ˇ
Michal Simko, Stefan Pendl, Stefan Schwarz, Qi Wang, Josep Colom Ikuno and Markus Rupp
Institute of Telecommunications, Vienna University of Technology
Gusshausstrasse 25/389, A-1040 Vienna, Austria
Email: msimko@nt.tuwien.ac.at
Web: http://www.nt.tuwien.ac.at/ltesimulator
Abstract—The UMTS Long Term Evolution (LTE) allows the B. Contribution
pilot symbol power to be adjusted with respect to that of the
In this paper, we derive analytical expressions for optimal
data symbols. Such power increase at the pilot symbols results
in a more accurate channel estimate, but in turn reduces the power allocation for a MIMO system with Zero Forcing (ZF)
amount of power available for the data transmission. In this equalizer under imperfect channel state information. We uti-
paper, we derive optimal pilot symbol power allocation based lize the post-equalization SINR, as the optimization function,
on maximization of the post-equalization Signal to Interference which is analogous to the throughput maximization in a real
and Noise Ratio (SINR) under imperfect channel knowledge.
system.
Simulation validates our analytical mode for optimal pilot symbol
power allocation. The main contributions of the paper are:
Index Terms—LTE, Channel Estimation, OFDM, MIMO. • By maximizing the post-equalization SINR, we deliver
optimal values for the pilot symbol power adjustment
in MIMO Orthogonal Frequency Division Multiplexing
I. I NTRODUCTION (OFDM) systems.
• The post-equalization SINR expression is derived for a
Current systems for cellular wireless communication are ZF receiver under imperfect channel knowledge.
designed for coherent detection. Therefore, channel estimator • We analytically derive the Mean Square Error (MSE)
is a crucial part of a receiver. UMTS Long Term Evolution expression of the Least Squares (LS) channel estimator
(LTE) provides a possibility to change the power radiated at utilizing linear interpolation.
the pilot subcarriers relative to the that at data subcarriers. • Simulation results with an LTE compliant simulator [6, 7]
Clearly, this additional degree of freedom in the system design confirm optimal values for pilot symbol power.
provides potential for optimization. • As with our previous work, all data, tools, as well
implementations needed to reproduce the results of this
A. Related Work paper can be downloaded from our homepage [8].
The remainder of the paper is organized as follows. In
In order to optimize the pilot symbol power allocation Section II we describe the mathematical system model. In
a model that takes into account the pilot power adjusting, Section III, we derive the post-equalization SINR expression
receiver structure and channel estimation error at the same for ZF with imperfect channel knowledge. The channel estima-
time, is needed. It has been shown by simulation that pilot tors of this work are briefly discussed in Section IV and their
symbol power allocation has a strong impact on capacity [1]. MSEs are derived. We formulate the optimization problem for
Authors of [2] show by simulation the impact of different optimal pilot symbol power allocation in Section V. Finally,
power allocations on the system’s Bit Error Rate (BER). we present LTE simulation results in Section VI and conclude
However, their analysis is based on Signal to Noise Ratio our paper in Section VII.
(SNR) so that they only approximate the impact of imperfect
channel knowledge on BER for Binary Phase-shift Keying II. S YSTEM M ODEL
(BPSK) modulation. In [3], optimal pilot symbol allocation In this section, we briefly point out the key aspects of LTE
is derived analytically for Phase-shift Keying (PSK) modula- relevant for this paper, as well as an system model.
tion of order two and four, using BER as the optimization In the time domain the LTE signal consists of frames with
criterion. In [4] optimal pilot symbol power in Multiple Input a duration of 10 ms. Each frame is split into ten equally long
Multiple Output (MIMO) system is derived based on lower subframes and each subframe into two equally long slots with
bound for capacity. Authors of [5] investigate power allocation a duration of 0.5 ms. Depending on the cyclic prefix length,
between pilot and data symbols for MIMO systems using post- being either extended or normal, each slot consists of Ns = 6
equalization Signal to Interference and Noise Ratio (SINR) as or Ns = 7 OFDM symbols, respectively. In LTE, the subcarrier
the optimization function. However, they only approximate the spacing is fixed to 15 kHz. Twelve adjacent subcarriers of one
SINR expression and their model is tightly connected with slot are grouped into a so-called resource block. The number
a Linear Minimum Mean Square Error (LMMSE) channel of resource blocks in an LTE slot ranges from 6 up to 100,
estimator. corresponding to a bandwidth from 1.4 MHz up to 20 MHz.
2. A received OFDM symbol in frequency domain can be III. P OST- EQUALIZATION SINR
written as In this section, we derive an analytical expression for the
Nt post-equalization SINR of a MIMO system under imperfect
ynr = Xnt hnt ,nr + nnr , (1) channel knowledge and ZF equalizer given by the system
nt =1 model in Equation (4). In the following section, we omit the
where the vector hnt ,nr contains the channel coefficients in subcarrier index k, since the concept we present is independent
the frequency domain between the nt -th transmiter and nr -th of it.
receiver antennas and nnr is additive white zero mean Gaus- If the equalizer has perfect channel knowledge available,
sian noise with variance σn at the nr -th receiver antenna. The
2 the ZF estimate of the data symbol s is given as
diagonal matrix Xnt = diag (xnt ) comprises the precoded −1
ˆ = GH G
s GH y. (9)
data symbols xd,nt and the pilot symbols xp,nt at the nt -th
transmit antenna placed by a suitable permutation matrix P s
The data estimate ˆ given by Equation (9) results in the post-
on the main diagonal equalization SINR of the l-th layer given as [10]
T
xnt = P xT t xT t . (2) σs
2
p,n d,n γl = −1 , (10)
σn eH (GH G)
2
l el
The length of the vector xnt is Nsub corresponding to the
number of subcarriers. Let us denote by Np and Nd , the where the vector el is an Nl × 1 zero vector with the l-th ele-
number of pilot symbols and the number of precoded data ment being 1. This vector serves to extract the corresponding
symbols, respectively. The precoded data symbols xd,nt are layer power after the equalizer.
obtained from the data symbols via precoding Let us proceed to the case of imperfect channel knowledge.
T T
We define the perfect channel as the channel estimate plus the
[xd,1,k · · · xd,Nt ,k ] = Wk [s1,k s2,k · · · sNl ,k ] , (3) error matrix due to the imperfect channel estimation
where xd,nt ,k is a precoded data symbol at the nt -th transmit ˆ
H = H + E, (11)
antenna port and the k-th subcarrier, Wk is the precoding
matrix at the k-th subcarrier and snl ,k is the data symbol of where the elements of the matrix E are independent of each
the nl -th layer at the k-th subcarrier. other with variance σe . Inserting Equation (11) in Equa-
2
Note that according to Equation (2), also the vectors ynr , tion (4), the input output relation changes to
hnt ,nr and wnr can be divided into two parts corresponding ˆ
to the pilot symbol positions and to the data symbol positions. y = H + E Ws + n. (12)
For the derivation of the post-equalization SINR, we will Since the channel estimation error matrix E is unknown at
use the input-output relation at the subcarrier level, given as: the receiver, the ZF solution is given again by Equation (9),
ˆ
but channel matrix H is replaced by its estimate H, which is
yk = Hk Wk sk + nk . (4)
known at the receiver
Matrix Hk is the MIMO channel matrix of size Nr × Nt . −1
s ˆ ˆ
ˆ = GH G ˆ
GH y, (13)
Furthermore, matrix Wk is a unitary precoding matrix of size
Nt × Nl . In LTE the precoding matrix can be chosen from a
ˆ ˆ
with matrix G being equal to HW. The error after the
finite set of precoding matrices [9]. The vector sk comprises
the data symbols of all layers at the k-th subcarrier. We denote equalization process using ZF is given as
the effective channel matrix that includes the effect of the −1
s ˆ ˆ
ˆ − s = GH G ˆ
GH (EWs + n) . (14)
channel and precoding by Gk
Gk = Hk Wk . (5) From Equation (14) we can compute the MSE matrix
H
Furthermore, let us denote the average data power transmitted MSE = E (ˆ − s) (ˆ − s)
s s (15)
at one layer by σs , the total data power by σx , and the average
2 2
−1
pilot symbol power by σp 2 = σn + σx σe
2 2 2 ˆ ˆ
GH G ,
1 with σe being the MSE of the channel estimator. Equation (15)
2
σs = E
2
sl,k 2
= , (6)
2
Nl directly leads to the SINR of the individual layers
Nt
σx =
2
E xd,nt 2 σs
2
2 = 1, (7) γl = −1 . (16)
nt =1 2 2 2 ˆ ˆ
(σn + σe σx ) eH GH G el
l
Nt
σp =
2
E xp,nt 2
2 = 1. (8) Note, that in practice variables σs , σx and σn are replaced by
2 2 2
nt =1 their estimates.
3. Interpolation By plugging (18) into (20), expanding the square and using
the identities
npi
Channel gain
E Δh∗i hpi = E
p hp = 0, (22)
xpi i
c2 = 1 − c1 , (23)
e p1 p2 pm i pn we obtain for the LS MSE at an interpolated position:
Subcarrier index (i,i) (p ,pn ) (p ,pm )
Fig. 1. Linear inter- and extrapolation of the channel on pilot subcarriers to σei = Rh,h + c2 (Rh,h
2
1
n
+ σn ) + c2 (Rh,h
2
2
m
+ σn )
2
intermediate subcarriers. (p ,pn ) (i,p ) (p ,i)
+2c1 c2 Rh,h
m
− 2c1 Rh,hn − 2c2 Rh,h
m
IV. C HANNEL E STIMATION (24)
In this section, we present state-of-the-art channel estimators (i,j)
and derive analytical expressions for their MSE. Due to the Here, Rh,h denotes the element in the i-th row and j-th
orthogonal pilot symbol pattern utilized in LTE, the MIMO column of matrix Rh,h = E hhH . For an extrapolated
channel can be estimated as Nt Nr individual Single Input position, we utilize the two consecutive neighboring pilot
Single Output (SISO) channels. Therefore, in the following positions, p1 and p2 , to obtain the estimated channel element
section, we omit the antenna indices. according to:
ˆ ˆ e − p1 ˆ ˆ
A. LS Channel Estimation he = hp1 + (hp2 − hp1 ) (25)
p2 − p1
The LS channel estimator [11, 12] for the pilot symbol
c1
positions is given as the solution to the minimization problem:
2 Then, the same Formula (24), with appropriately modified
ˆp ˆ
hLS = arg min yp − Xp hp = X−1 yp
p (17) indices, applies for the extrapolated MSE σee as well. The
2
ˆ
hp 2
MSE at an extrapolated position is generally larger than at an
The remaining channel coefficients at the data subcarriers have interpolated position, because the cross correlation between the
to be obtained by means of interpolation. In this work, we channels at position e and p2 is smaller than those between
utilize linear interpolation of the pilot subcarriers on each the channels at position i and pn .
antenna to obtain the channel vector h containing the channel The total MSE of LS channel estimator is given as mean
elements of all subcarriers in one OFDM symbol. over all subcarriers. Due to the dense pilot symbol pattern and
In order to derive the theoretical MSE, we distinguish three thus strong correlation over frequency, the total MSE can be
cases, as shown in Figure 1: expressed as
• MSE at a pilot subcarrier pi : σep
2
σe = ce σn ,
2 2
(26)
• MSE at an interpolated subcarrier i: σei
2
• MSE at an extrapolated subcarrier e: σee
2
where ce is a constant. Equation (26) follows from Equa-
At a pilot position pi the estimated channel element equals: tion (23) and Equation (24).
ˆ yp np
hpi = i = hpi + i (18) B. LMMSE Channel Estimation
xpi xpi
The LMMSE channel estimator requires the second order
Δhpi
statistics of the channel and the noise. It can be shown that
The MSE of the channel estimator is given by the noise the LMMSE channel estimate is obtained by multiplying the
variance σep = σn . To obtain the variance at an interpolated
2 2
LS estimate with a filtering matrix ALMMSE [13–15]:
position consider the following equation to compute the in-
ˆ ˆ ˆp
hLMMSE = ALMMSE hLS (27)
terpolated channel element hi from the two neighboring pilot
ˆ p and hp :
positions h m ˆ
n
In order to find the LMMSE filtering matrix, the MSE
ˆ ˆ i − pm ˆ ˆ
hi = hpm + ·(hpn − hpm ) (19) 2
pn − pm σe = E
2 ˆp
h − ALMMSE hLS , (28)
2
c1
has to be minimized, leading to
Then, the MSE can be computed as:
−1
2 ALMMSE = Rh,hp Rhp ,hp + σn I
2
, (29)
σi = E
2 ˆ
hi − hi (20)
2
where the matrix Rhp ,hp = E hp hH is the channel autocor-
p
2
=E ˆ ˆ ˆ
hi − hpm + c1 · (hpn − hpm ) (21) relation matrix at the pilot symbol positions, and the matrix
2 Rh,hp = E hhH is the channel crosscorrelation matrix.
p
4. x 106
To derive the theoretical MSE, we plug Equation (29) into 1.8
Equation (28): 1.7
4x4
LS channel estimator
σe =E
2 2 ˆp
h − (Rh,hp (Rhp ,hp − σn I)−1 hLS ) · (30)
1.6
1.5
H
h − (Rh,hp (Rhp ,hp − 2 ˆp
σn I)−1 hLS ) 1.4
f(o)
2x2
1.3
After a straightforward manipulation, the average MSE at each 1.2 LMMSE channel estimator
subcarrier is expressed as 1.1
1x1
1 −1 1
σe =
2
tr Rh,h − Rh,hp Rhp ,hp + σn I
2
Rhp ,h . -10 -5 0 5 10
Nsub poff[dB]
Fig. 2. Power allocation function f (poff ) for different numbers of transmit
V. P OWER A LLOCATION antennas and LS (solid line) and LMMSE (dashed line) channel estimators
In this section, we describe the problem of optimal pilot TABLE I
VALUES OF THE PARAMETERS OF f (poff ) FOR DIFFERENT NUMBER OF
power allocation in LTE based on the maximization of the TRANSMIT ANTENNAS FOR 1.4 MH Z BANDWIDTH , ITU P EDA [17]
post-equalization SINR under imperfect channel knowledge. CHANNEL MODEL , LS AND LMMSE CHANNEL ESTIMATORS
Although the shown results are from the application to LTE, Parameter Tx = 1 Tx = 2 Tx = 4
the presented concept can be applied to any MIMO OFDM Nd 960 912 864
Np 48 96 144
system.
If we increase the power at the pilot symbol by a factor c2 ,
p LS
the MSE of the channel estimator improves by the factor c2 ce 0.3704 0.3704 0.5556
p poff,opt [dB] ≈5.8 ≈3.6 ≈3.5
σe
2
σe =
˜2 . (31) LMMSE
c2
p ce 0.0394 0.0394 0.0544
poff,opt [dB] ≈-0.7 ≈-2.8 ≈-3.2
However, the power of the data symbols has to be decreased by
a factor c2 in order to keep the total transmit power constant. The target is to find an optimal value of poff,opt that max-
d
Obviously, the two factors c2 and c2 are connected. For this imizes the post-equalization SINR while keeping the overall
p d
purpose, we define a variable poff , expressing the power offset transmit power constant
between the mean energy of the pilot symbols and the data maximize γl (37)
symbols, and refer to it as pilot offset. The variables c2 , c2
p d
poff
and poff are interconnected as follows: subject to Nd σx + Np σp = const
2 2
Np + N d In order to maximize the post-equalization SINR, the power
cp = (32)
poff Nd + Np allocation function f (poff ) in the denominator of Equation (35)
Np + Nd has to be minimized. The minimum of the power allocation
cd = Np
= poff cp (33)
Nd + poff function f (poff ) can be found by simply differentiating it and
solving for 0. By these means, the optimal value of the variable
Plugging in the variables c2 and c2 in Equation (16), we obtain
d p poff is given as solution to the following expression:
the SINR expression with adjusted power of the pilot symbols
−2 (poff Nd + Np ) p−3 + 2 (poff Nd + Np ) Nd p−2 + ce = 0.
2
off off
σs c2
2
(38)
γl = d
. (34)
σe 2 2
2 −1
σn +
2
c2 σx cd eH (GH G)
l el An analytical solution for Equation (38) can be obtained by
p
If we insert Equation (32) and Equation (33) into Equation (34) means of Ferrari’s solution [16]. Figure 2 depicts f (poff ) for
and simplify the expression, we obtain: different numbers of transmit antennas for LTE. Typical values
of parameters Nd , Np and ce are given in Table I. Note,
1 that although Nd and Np depend on the utilized bandwidth,
γl = , (35)
Nl σn eH (GH G)−1 el
2
the minimum of f (poff ) is independent of it, since Nd and
l
(Nd +Np )2
f (poff )
Np scale with the same constant with increasing bandwidth.
for which the function f (poff ) is given as The value of ce is different for four transmit antennas due
to the lower number of pilot symbols at the third and fourth
2 1
f (poff ) = (poff Nd + Np ) + ce . (36) antenna. The last row of Table I gives the optimal values of
p2
off poff,opt for different numbers of transmit antennas and an ITU
We refer to it as power allocation function. Note, that it is PedA [17] type channel model. While utilizing the LMMSE
independent of channel realization and noise power. Therefore, channel estimator, one obtains negative values for poff,opt .
it is possible to find an optimum value for pilot symbol power This means, that optimally, the pilot symbol power has to
allocation independent of the SNR and channel realization. be reduced in order to maximize the post-equalization SINR.
5. TABLE II
S IMULATOR SETTINGS FOR FAST FADING SIMULATIONS linear interpolation. Throughput simulation results validate the
Parameter Value accuracy of our analytical model for the optimum pilot power
Bandwidth 1.4 MHz adjustment. All data, tools and scripts are available online in
Number of transmit antennas 1, 2, 4
Number of receive antennas 1, 2, 4 order to allow other researchers to reproduce the results shown
Receiver type ZF in the paper [8].
Transmission mode Open-loop spatial multiplexing
Channel type ITU PedA [17] ACKNOWLEDGMENTS
MCS 9
coding rate 616/1 024 ≈ 0.602 The authors would like to thank the LTE research group
symbol alphabet 16 QAM for continuous support and lively discussions. This work
has been funded by the Christian Doppler Laboratory for
3.5 Wireless Technologies for Sustainable Mobility, KATHREIN-
Werke KG, and A1 Telekom Austria AG. The financial support
3 by the Federal Ministry of Economy, Family and Youth
and the National Foundation for Research, Technology and
2.5
2x2
Development is gratefully acknowledged.
throughput [Mbit/s]
2
4x4
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