On the Real-Time Hardware Implementation Feasibility of Joint Radio
Resource Management Policies for Heterogeneous Wireles...
Since the JRRM techniques use utility functions to estimate the users’ QoS demands, only updated information
about each RA...
INTRODUCTION:
The evolution of mobile and wireless communication systems is being characterized by the coexistence of
dive...
on the measured buffer delay, while the authors propose a mechanism based on load thresholds for real time
services.
Propo...
LITRAURE SURVEY:
Joint Call Admission Control Algorithm for Fair Radio Resource Allocation in Heterogeneous Wireless
Netwo...
suitable for wireless communications. On the other hand, the paper presents and evaluates an on-line video
traffic model, ...
EXISTING SYSTEM:
Existing of the benefits of common radio resource management (CRRM) for traffic management in an
environm...
PROPOSED SYSTEM:
JRRM techniques, initially proposed in aimed at providing the highest possible homogeneous user satisfact...
HARDWARE & SOFTWARE REQUIREMENTS:
HARDWARE REQUIREMENTS:
 Processor - Pentium –IV
 Speed - 1.1 GHz
 RAM - 256 MB (min)
...
IMPLEMENTATION:
The proposed, implemented and evaluated techniques are based on linear programming and optimization
algori...
MODULES:
HETEROGENEOUS WIRELESS SYSTEMS:
RADIO ACCESS TECHNOLOGIES (RAT):
JRRM SERVER CLIENT MODULE:
JOINT RADIO RESOURCE ...
REFERENCES:
[1] 3GPP, Improvement of Radio Resource Management (RRM) across RNS and RNS/BSS, Technical Report
25.881, v5.0...
[10] M.C. Lucas-Estan˜ , J. Goza´lvez, and J. Sa´nchez-Soriano, “Common Radio Resource Management Policy
for Multimedia Tr...
CLOUING
DOMAIN: WIRELESS NETWORK PROJECTS
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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT On the real time hardware implementation feasibility of joint radio resource management policies for heterogeneous wireless networks

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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT On the real time hardware implementation feasibility of joint radio resource management policies for heterogeneous wireless networks

  1. 1. On the Real-Time Hardware Implementation Feasibility of Joint Radio Resource Management Policies for Heterogeneous Wireless Networks ABSTRACT: We design of Joint Radio Resource Management (JRRM) techniques is a key and challenging aspect in future heterogeneous wireless systems where different Radio Access Technologies (RAT) will physically coexist. In this system, the total available radio resources need to be used in a coordinated way to guarantee adequate satisfaction levels to all users, and maximize the system revenues. In addition to carry out an efficient use of the available radio resources, JRRM algorithms need to exhibit good computational performance to guarantee their future implementation viability. We proposes novel JRRM techniques based on linear programming techniques, and investigates their computational cost when implemented in DSP platforms commonly used in mobile-based stations. The obtained results demonstrate the feasibility to implement the proposed JRRM algorithms in future heterogeneous wireless systems. JRRM techniques have been implemented following the JRRM server approach discussed in the 3GPP standards. This approach considers a centralized architecture that places the JRRM functionality in a node that collects information of all available RATs.1 GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
  2. 2. Since the JRRM techniques use utility functions to estimate the users’ QoS demands, only updated information about each RAT’s load must be transmitted to the JRRM server. Using this information, the implemented JRRM techniques manage the available radio resources to maximize the percentage of satisfied users.
  3. 3. INTRODUCTION: The evolution of mobile and wireless communication systems is being characterized by the coexistence of diverse Radio Access Technologies (RAT) with different, but sometimes complementary, technical characteristics. In parallel, novel user applications are continuously appearing with diverse Quality of Service (QoS) requirements. Despite the appearance of novel RATs with increasing performance, the research community agrees that future mobile and wireless communication systems will be composed of heterogeneous RATs physically coexisting and offering mobile services to a wide range of QoS-demanding users in a coordinated manner. Future heterogeneous wireless systems are the coordinated management of heterogeneous radio resources, usually referred as Joint Radio Resource Management (JRRM) or Common Radio Resource Management (CRRM). The Third Generation Partnership Project (3GPP) defines the JRRM concept and describes different supporting network architectures that ensure the interoperability between the different access technologies. JRRM policies need then to be designed so that the total available radio resources are efficiently distributed among active users in order to maximize the system revenue and provide the QoS levels demanded by users/services in multimedia environments to carry out the most efficient use of the total available resources. JRRM policies must decide for each incoming call the RAT over which it will be conveyed (RAT selection) and the number of radio resources within the selected RAT (intra-RAT RRM) that will be necessary to satisfy the user/service QoS requests. JRRM policy and resulting radio resource assignments should be capable to dynamically adapt to the current operating conditions, for example system load and active multimedia services. We describes the framework over which JRRM algorithms can be developed, and proposes some basic techniques to address the initial RAT selection dilemma based on pre-established service-to-RAT assignments and user location. Other studies have investigated how to exploit multi technology terminals capability to switch between RATs in order to free the capacity required to accept new calls from single-mode terminals. For example, the JRRM load balancing mechanism reported in aims at achieving a uniform traffic distribution between the available RATs. As the authors point out, such uniform distribution is desirable in order to maximize the trucking gain and minimize the probability of making unnecessary Vertical Handovers (VHO) of multi technology terminals between RATs. For Non real-time services, the load balancing is performed based
  4. 4. on the measured buffer delay, while the authors propose a mechanism based on load thresholds for real time services. Proposals to jointly address the RAT selection and intra-RAT RRM dilemmas have also been reported. For example, a Joint Call Admission Control (JCAC) algorithm that combines the RAT selection and Call Admission Control (CAC) mechanisms in order to reduce the call blocking and dropping probabilities, and ensure a fair radio resource allocation. We proposed novel JRRM policies that simultaneously assign to each user an adequate combination of RAT and number of radio resources within such RAT to guarantee the user/service QoS requirements. The proposed JRRM techniques are based on linear programming and optimization techniques. QoS performance that can be achieved by novel JRRM techniques, but have not investigated their computational cost and implementation feasibility in evaluation of the computational efficiency of new proposals is widely conducted in other research fields like audio and video real-time compression, where the time spent by the algorithm to process the data is crucial to provide a good performance to the end user. The result of this study is of relevance to the research community since it demonstrates the feasibility of implementing complex JRRM policies, and provides the first indications on their hardware computational performance in time requirements are not as demanding as for audio and video compression techniques, JRRM decisions in mobile networks should be made as quickly as possible in order to be able to efficiently adapt the use of the radio resources networks.
  5. 5. LITRAURE SURVEY: Joint Call Admission Control Algorithm for Fair Radio Resource Allocation in Heterogeneous Wireless Networks Supporting Heterogeneous Mobile Terminals Author: O.E. Falowo and H.A. Chan, pp. 1-5, Jan. 2010 This paper proposes a joint call admission control (JCAC) algorithm to reduce this problem of unfairness. The proposed JCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, and radio access technology (RAT) terminal support index. The objectives of the proposed JCAC algorithm are to reduce call blocking/ dropping probability, and ensure fairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous networks. We develop an analytical model to evaluate the performance of the proposed JCAC scheme in heterogeneous wireless networks and derive expression for call blocking / dropping probability. The performance of the proposed JCAC algorithm is compared with that of other JCAC algorithm. Results show the proposed algorithm reduces call blocking/ dropping probability in the networks, and ensure fairness in allocation of radio resources among heterogeneous terminals. Common Radio Resource Management Policy for Multimedia Traffic in Beyond 3G Heterogeneous Wireless Systems Author: M.C. Lucas-Estan˜ , J. Goza´lvez, and J. Sa´nchez-Soriano, pp. 1-5, Sept. 2008. Beyond 3G wireless systems will be composed of a variety of radio access technologies (RATs) with different, but also complementary, performance and technical characteristics. To exploit such diversity while guaranteeing the interoperability and efficient management of the different RATs, common radio resource management (CRRM) techniques need to be defined. This work proposes and evaluates a CRRM policy that simultaneously assigns to each user an adequate combination of RAT and number of radio resources within such RAT to guarantee its QoS requirements. The proposed CRRM technique is based on linear objective functions and programming tools. H.263 Video Traffic Modelling for Low Bit Rate Wireless Communications Author: O. Lazaro, D. Girma, and J. Dunlop, pp. 2124-2128, Sept. 2005. Video traffic exhibits a greater complexity than traditional services such as voice. Therefore, it is necessary to identify the relevant statistical properties, which characterise this type of traffic and provide the tools to properly model them so that networks could be efficiently dimensioned. This work presents on one hand a detailed analysis of the statistical features, which characterise H.263 video traffic streams coded at bit rates
  6. 6. suitable for wireless communications. On the other hand, the paper presents and evaluates an on-line video traffic model, which captures the most significant features observed. A Perspective on Radio Resource Management in B3G Author: O. Sallent, pp. 30-34, Sept. 2006. Beyond 3G usually refers to heterogeneous scenarios where different radio access technologies (RATs) coexist and operate in a coordinated way. This brings a new challenge to offer services to the users over an efficient and ubiquitous radio access. In this way, the user can be served through the RAT that fits better to the terminal capabilities and service requirements, and also a more efficient use of the radio resources can be achieved. This challenge calls for the introduction of new radio resource management (RRM) algorithms operating from a common perspective that take into account the overall amount of resources offered by the available RATs. In this context, this paper presents the framework for developing RRM algorithms in the B3G scenarios, including some possible approaches.
  7. 7. EXISTING SYSTEM: Existing of the benefits of common radio resource management (CRRM) for traffic management in an environment where several different radio access technologies co-exist with cells on several hierarchical layers. Load balancing, i.e., the capacity gains from CRRM concept are studied by dynamic simulations for both real- time and non-real-time traffic. The results show that CRRM improves the conversational and streaming capacity by 11% with 144 kbps and the interactive capacity up to 70-90% when 5 s delays is required with 80- 85% probability. Previous work on CRRM and RAT selection has mainly focused on the development of solutions aimed at maximizing the overall system capacity, the design of strategies from the QoS-provision viewpoint has received less attention. CRRM work reported in focused on the development of new CRRM techniques designed to efficiently distribute heterogeneous traffic among the available RATs in order to provide appropriate user/service QoS levels while adequately exploiting the available radio resources of each node distribute multimedia traffic in heterogeneous RAT.
  8. 8. PROPOSED SYSTEM: JRRM techniques, initially proposed in aimed at providing the highest possible homogeneous user satisfaction levels to all service types by exploiting the QoS/resource flexibility offered by different services present in a multimedia framework. For example, email users do not require the same number of radio resources than a video conferencing session to obtain the same user satisfaction levels. In this work, the user satisfaction is represented by utility values identifying the radio resources needed per service class to achieve certain user QoS satisfaction levels. Implemented JRRM techniques, this work considers a heterogeneous wireless environment where the General Packet Radio Service (GPRS), Enhanced Data rates for GSM Evolution (EDGE), and High Speed Downlink Packet Access (HSDPA) RATs physically coexist. The JRRM techniques have been implemented following the JRRM server approach discussed in the 3GPP standards. This approach considers a centralized architecture that places the JRRM functionality in a node that collects information of all available RATs.1 since the JRRM techniques use utility functions to estimate the users’ QoS demands, only updated information about each RAT’s load must be transmitted to the JRRM server. Using this information, the implemented JRRM techniques manage the available radio resources to maximize the percentage of satisfied users. Advanced JRRM techniques for heterogeneous wireless networks implemented and evaluated techniques are based on linear programming and optimization algorithms, and have been shown to achieve good system performance under multimedia traffic conditions. To evaluate their implementation feasibility, the JRRM techniques have been implemented in a DSP simulator software using open source linear programming solvers.
  9. 9. HARDWARE & SOFTWARE REQUIREMENTS: HARDWARE REQUIREMENTS:  Processor - Pentium –IV  Speed - 1.1 GHz  RAM - 256 MB (min)  Hard Disk - 20 GB  Floppy Drive - 1.44 MB  Key Board - Standard Windows Keyboard  Mouse - Two or Three Button Mouse  Monitor - SVGA SOFTWARE REQUIREMENTS:  Operating System : Windows XP  Front End : Java JDK 1.7  Scripts : Java Script.
  10. 10. IMPLEMENTATION: The proposed, implemented and evaluated techniques are based on linear programming and optimization algorithms, and have been shown to achieve good system performance under multimedia traffic conditions. To evaluate their implementation feasibility, the JRRM techniques have been implemented in a DSP simulator software using open source linear programming solvers. Demonstrated the feasibility of implementing the proposed JRRM techniques in real mobile communication systems using powerful hardware and software tools in the computational execution cost can be further reduced at the cost of eliminating the optimality condition in the radio resources distribution. However, the obtained results show that both JRRM proposals satisfy their various objectives.  The majority of services achieve their minimum QoS level, and only when such level is guaranteed, resources are additionally assigned to higher priority users.  The number of served users is the maximum possible satisfying the system and service constraints.  The service priorities criterion defined in correctly applied under radio resources shortage conditions.
  11. 11. MODULES: HETEROGENEOUS WIRELESS SYSTEMS: RADIO ACCESS TECHNOLOGIES (RAT): JRRM SERVER CLIENT MODULE: JOINT RADIO RESOURCE MANAGEMENT (JRRM): JRRM PERFORMANCE ANALYSIS:
  12. 12. REFERENCES: [1] 3GPP, Improvement of Radio Resource Management (RRM) across RNS and RNS/BSS, Technical Report 25.881, v5.0.0, Jan. 2002. [2] 3GPP, Improvement of Radio Resource Management (RRM) Across RNS and RNS/BSS Post-Rel-5, Technical Report 25.891, v0.3.0, June 2003. [3] J. Perez-Romero, O. Sallent, and R. Agusti, “Policy-Based Initial RAT Selection Algorithms in Heterogeneous Networks,” Proc. Seventh IFIP Int’l Conf. Mobile and Wireless Comm. Networks (MWCN ’05), Sept. 2005. [4] S.J. Lincke, “Vertical Handover Policies for Common Radio Resource Management,” Int’l J. Comm. Systems, vol. 18, no. 6, pp. 527-543, Aug. 2005. [5] A. Tolli, P. Hakalin, and H. Holma, “Performance Evaluation of Common Radio Resource Management (CRRM),” Proc. IEEE Int’l Conf. Comm. (ICC ’02), vol. 5, pp. 3429-3433, Apr. 2002. [6] O. Cabral, F.J. Velez, J. Rodriguez, V. Monteiro, A. Gameiro, and N.R. Prasad, “Optimal Load Suitability Based RAT Selection for HSDPA and IEEE 802.11e Information Theory, and Aerospace and Electronic Systems Technology (Wireless VITAE ’09), pp. 722-726, May 2009. [7] Y. Guang, C. Jie, Y. Kai, Z. Ping, and V.O.K. Li, “Joint Radio Resource Management based on the Species Competition Model,” Proc. IEEE Wireless Comm. and Networking Conf. (WCNC ’06), pp. 54-57, Apr. 2006. [8] O.E. Falowo and H.A. Chan, “Joint Call Admission Control Algorithm for Fair Radio Resource Allocation in Heterogeneous Wireless Networks Supporting Heterogeneous Mobile Terminals,” Proc. IEEE Seventh Consumer Comm. and Networking Conf. (CCNC), pp. 1-5, Jan. 2010. [9] L. Giupponi, R. Agusti, J. Pe´rez-Romero, and O. Sallent, “A Novel Approach for Joint Radio Resource Management based on Fuzzy Neural Methodology,” IEEE Trans. Vehicular Technology, vol. 57, no. 3, pp. 1789-1805, May 2008.
  13. 13. [10] M.C. Lucas-Estan˜ , J. Goza´lvez, and J. Sa´nchez-Soriano, “Common Radio Resource Management Policy for Multimedia Traffic in Beyond 3G Heterogeneous Wireless Systems,” Proc. IEEE 19th Int’l Symp. Personal, Indoor, and Mobile Radio Comm., pp. 1-5, Sept. 2008. [11] J. Goza´lvez, M.C. Lucas-Estan˜ , and J. Sa´nchez-Soriano, “Joint Radio Resource Management in Beyond 3G Heterogeneous Wireless Systems,” Proc. 11th Int’l Symp. Wireless Personal Multimedia Comm. (WPMC), pp. 1-5, Sept. 2008. [12] E.A. Yavuz and V.C.M. Leung, “Computationally Efficient Method to Evaluate the Performance of Guard- Channel-Based Call Admission Control in Cellular Networks,” IEEE Trans. Vehicular Technology, vol. 55, no. 4, pp.1412-1424, July 2006. [13] 3GPP, Services and Service Capabilities, Technical Specification 22.105, v6.3.0, 2005. [14] P. Barford, M. Crovella, “Generating Representative Web Workloads for Network and Server Performance Evaluation,” Proc. Int’l Conf. Measurement and Modeling of Computer Systems (SIGMETRICS/PERFORMANCE ’98), pp. 151-160, June 1998. [15] J. Ho, Y. Zhu, and S. Madhavapeddy, “Throughput and Buffer Analysis for GSM General Packet Radio Service (GPRS),” Proc. IEEE Wireless Comms. and Networking Conf. (WCNC 1999), pp. 1427-1431, Sept. 1999.
  14. 14. CLOUING DOMAIN: WIRELESS NETWORK PROJECTS

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