多播广播单频网的无线资源管理研究
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摘要
多播广播单频网(Multicast Broadcast Single Frequency Network, MBSFN)作为蜂窝网络中多播业务的传输模式,在提高系统频谱资源利用率的同时,可以增强网络传输的可靠性,以保证多播业务的服务质量。随着无线通信技术及移动多媒体业务的飞速发展,人们对无线资源的需求仍在急剧加大。在移动蜂窝网络中,多播和单播业务混合共享无线资源,如何实现多播与单播业务下高效的无线资源管理是亟须解诀的问题。因此,多播广播单频网的无线资源管理研究是一个新的且重要的问题,受到学术界和工业界的广泛关注。
本论文将主要针对MBSFN无线资源管理中的切换、负载均衡和调度算法这三方面的问题进行探讨与研究,同时也是作者在参加国家科技重大专项" IMT-Advanced新型无线传输技术研发(项目编号2008ZX03003-004)”和“面向IMT-Advanced跨层优化技术(项目编号2010ZX03003-001)”等科研项目过程中所取得的一些研究成果。
本论文的具体研究内容如下：
在MBSFN的切换算法研究方面,针对多播业务的资源需求问题,提出了最小化多播业务需求资源的切换算法和基于多播单播业务负载均衡的切换算法。通过分析单频网(Single Frequency Network, SFN)和点到点(Point-to-point, PTP)两种传输模式下的信号与干扰加噪声比(Signal to Interference plus Noise Ratio, SINR),建立了多播业务需求资源的系统模型,并以最小化多播业务的需求资源为优化目标,在这两种模式之间进行切换来获得最优解；进而考虑单播业务的需求资源,建立了小区需求资源的系统模型,并以最小化最高负载小区的需求资源为优化目标,基于动态的模式切换阈值进行最优化求解。仿真结果表明,所提算法可以有效减少多播业务的需求资源而提高了无线资源利用率。
在MBSFN的负载均衡算法研究方面,针对由于多播业务需求差异而导致的高负载小区的热点拥塞问题,提出了多播广播单频网区域内的负载均衡算法和多播广播单频网区域间的负载均衡算法。通过分析SFN和点到多点(Point-to-multipoint, PTM)两种传输模式下的SINR,建立了MBSFN区域内各小区需求资源的系统模型,并以最小化最高负载小区的需求资源为优化目标,基于最优化多播业务服务质量(Quality of Service, QoS)进行最优化求解；进而建立各MBSFN区域内最高负载小区需求资源的系统模型,并以最小化各MBSFN区域内最高负载小区需求资源的最大值为优化目标,采用模拟退火算法进行最优化求解。仿真结果表明,所提算法的最高负载小区需求资源最小,具有较好的负载均衡效果。
在MBSFN的调度算法研究方面,针对实时多播单播联合调度中的业务时延和用户终端能耗的折中问题,提出了保证实时多播业务QoS的调度算法和实时多播单播业务的节能调度算法。通过分析各多效因子来定义调度的优先级函数,并采用并行调度的方法来降低计算的复杂度；进而通过分析用户终端的传输能耗和状态转换能耗,建立了用户终端能耗的系统模型,并采用循环迭代的方法对用户终端平均能耗的最小化问题进行求解。仿真结果表明,所提算法在保证多播单播业务的时延性能下,可以大大地降低用户的终端能耗而达到节能的效果。
Multicast Broadcast Single Frequency Network (MBSFN), as a transmission mode for multicast services in cellular networks, can improve the utilization of spectrum resources and the coverage of wireless network to guarantee the quality of service (QoS) of multicast services. With the increasing demand of radio resources for the rapid development of radio access technologies and related services, multicast and unicast services share available radio resources. Therefore, the radio resources management (RRM) for multicast and unicast services is becoming a new but crucial issue in MBSFN field and catches attentions in both research and industry societies.
This dissertation mainly focuses on discussing and solving the three problems of handover, load balancing and packet scheduling in RRM for MBSFN, and it is also a collection of results achieved during the author participating in the National Science and Technology Major Project "Research of IMT-Advanced Wireless Transmission Technology (No.2008ZX03003-004)" and "Research of IMT-Advanced Cross-layer Optimization Technology (No.2010ZX03003-001)".
The main contents of this dissertation are issued as follows:
In the handover algorithms for MBSFN, to solve the problem of the demanded radio resources for multicast services, the handover algorithm of minimizing the demanded radio resources for multicast services and the handover algorithm of load balancing for the multicast and unicast services are proposed. The system model of the demanded radio resources for multicast services is formulated by analyzing Signal to Interference plus Noise Ratio (SINR) of single frequency network (SFN) mode and point-to-point (PTP) mode, and the minimization problem of the demanded radio resources for multicast services is solved by the switch between the two transmission modes of SFN and PTP; then the system model of the demanded radio resources for the multicast and unicast services in a cell is formulated by considering the demanded radio resources for unicast services, and the minimization problem of the demanded radio resources for the maximum load cell is solved by the dynamic handover threshold. The simulation results show that the proposed handover algorithms can greatly reduce the demanded radio resources of multicast services to improve the spectrum utilization.
In the load balancing algorithms for MBSFN, to avoid the network congestion of the different demanded radio resources for multicast services, the load balancing algorithm in MBSFN areas and the load balancing algorithm between MBSFN areas are proposed. The system model of the demanded radio resources for a cell in MBSFN areas is formulated by analyzing the SINR of SFN mode and point-to-multipoint (PTM) mode, and the minimization problem of the demanded radio resources for the maximum load cell is solved by multicast QoS optimization; then the system model of the demanded radio resources for the maximum load cell in MBSFN is formulated by the two transmission modes of SFN and PTM, and the Simulated Annealing (SA) algorithm is adopted to solve the minimization problem of the maximum of the demanded radio resources for the maximum load cell in each MBSFN area. The simulation results show that the proposed load balancing algorithms can efficiently reduce the demanded radio resources of the maximum load cell for the better load balancing.
In the packet scheduling algorithms for MBSFN, to solve the tradeoff problem in the delay performance of real-time multimedia services and the average energy of mobile stations, the packet scheduling algorithm for guaranteeing the QoS requirements of real-time multicast services and the energy efficient packet scheduling algorithm for the real-time multicast and unicast services are proposed. The function of scheduling priority is defined by analyzing multiple utility factors, and the parallel scheduling is adopted to reduce the computation complexity; then the system model of the average energy of mobile stations is formulated by analyzing the energy for the transmission and states exchange of mobile stations, and the circulation and iteration algorithm is adopted to solve the minimization problem of the average energy of mobile stations. The simulation results show that the proposed packet scheduling algorithms can guarantee the minimum average energy of mobile stations with satisfying the delay performance of real-time multimedia services.
本论文将主要针对MBSFN无线资源管理中的切换、负载均衡和调度算法这三方面的问题进行探讨与研究,同时也是作者在参加国家科技重大专项" IMT-Advanced新型无线传输技术研发(项目编号2008ZX03003-004)”和“面向IMT-Advanced跨层优化技术(项目编号2010ZX03003-001)”等科研项目过程中所取得的一些研究成果。
本论文的具体研究内容如下：
在MBSFN的切换算法研究方面,针对多播业务的资源需求问题,提出了最小化多播业务需求资源的切换算法和基于多播单播业务负载均衡的切换算法。通过分析单频网(Single Frequency Network, SFN)和点到点(Point-to-point, PTP)两种传输模式下的信号与干扰加噪声比(Signal to Interference plus Noise Ratio, SINR),建立了多播业务需求资源的系统模型,并以最小化多播业务的需求资源为优化目标,在这两种模式之间进行切换来获得最优解；进而考虑单播业务的需求资源,建立了小区需求资源的系统模型,并以最小化最高负载小区的需求资源为优化目标,基于动态的模式切换阈值进行最优化求解。仿真结果表明,所提算法可以有效减少多播业务的需求资源而提高了无线资源利用率。
在MBSFN的负载均衡算法研究方面,针对由于多播业务需求差异而导致的高负载小区的热点拥塞问题,提出了多播广播单频网区域内的负载均衡算法和多播广播单频网区域间的负载均衡算法。通过分析SFN和点到多点(Point-to-multipoint, PTM)两种传输模式下的SINR,建立了MBSFN区域内各小区需求资源的系统模型,并以最小化最高负载小区的需求资源为优化目标,基于最优化多播业务服务质量(Quality of Service, QoS)进行最优化求解；进而建立各MBSFN区域内最高负载小区需求资源的系统模型,并以最小化各MBSFN区域内最高负载小区需求资源的最大值为优化目标,采用模拟退火算法进行最优化求解。仿真结果表明,所提算法的最高负载小区需求资源最小,具有较好的负载均衡效果。
在MBSFN的调度算法研究方面,针对实时多播单播联合调度中的业务时延和用户终端能耗的折中问题,提出了保证实时多播业务QoS的调度算法和实时多播单播业务的节能调度算法。通过分析各多效因子来定义调度的优先级函数,并采用并行调度的方法来降低计算的复杂度；进而通过分析用户终端的传输能耗和状态转换能耗,建立了用户终端能耗的系统模型,并采用循环迭代的方法对用户终端平均能耗的最小化问题进行求解。仿真结果表明,所提算法在保证多播单播业务的时延性能下,可以大大地降低用户的终端能耗而达到节能的效果。
Multicast Broadcast Single Frequency Network (MBSFN), as a transmission mode for multicast services in cellular networks, can improve the utilization of spectrum resources and the coverage of wireless network to guarantee the quality of service (QoS) of multicast services. With the increasing demand of radio resources for the rapid development of radio access technologies and related services, multicast and unicast services share available radio resources. Therefore, the radio resources management (RRM) for multicast and unicast services is becoming a new but crucial issue in MBSFN field and catches attentions in both research and industry societies.
This dissertation mainly focuses on discussing and solving the three problems of handover, load balancing and packet scheduling in RRM for MBSFN, and it is also a collection of results achieved during the author participating in the National Science and Technology Major Project "Research of IMT-Advanced Wireless Transmission Technology (No.2008ZX03003-004)" and "Research of IMT-Advanced Cross-layer Optimization Technology (No.2010ZX03003-001)".
The main contents of this dissertation are issued as follows:
In the handover algorithms for MBSFN, to solve the problem of the demanded radio resources for multicast services, the handover algorithm of minimizing the demanded radio resources for multicast services and the handover algorithm of load balancing for the multicast and unicast services are proposed. The system model of the demanded radio resources for multicast services is formulated by analyzing Signal to Interference plus Noise Ratio (SINR) of single frequency network (SFN) mode and point-to-point (PTP) mode, and the minimization problem of the demanded radio resources for multicast services is solved by the switch between the two transmission modes of SFN and PTP; then the system model of the demanded radio resources for the multicast and unicast services in a cell is formulated by considering the demanded radio resources for unicast services, and the minimization problem of the demanded radio resources for the maximum load cell is solved by the dynamic handover threshold. The simulation results show that the proposed handover algorithms can greatly reduce the demanded radio resources of multicast services to improve the spectrum utilization.
In the load balancing algorithms for MBSFN, to avoid the network congestion of the different demanded radio resources for multicast services, the load balancing algorithm in MBSFN areas and the load balancing algorithm between MBSFN areas are proposed. The system model of the demanded radio resources for a cell in MBSFN areas is formulated by analyzing the SINR of SFN mode and point-to-multipoint (PTM) mode, and the minimization problem of the demanded radio resources for the maximum load cell is solved by multicast QoS optimization; then the system model of the demanded radio resources for the maximum load cell in MBSFN is formulated by the two transmission modes of SFN and PTM, and the Simulated Annealing (SA) algorithm is adopted to solve the minimization problem of the maximum of the demanded radio resources for the maximum load cell in each MBSFN area. The simulation results show that the proposed load balancing algorithms can efficiently reduce the demanded radio resources of the maximum load cell for the better load balancing.
In the packet scheduling algorithms for MBSFN, to solve the tradeoff problem in the delay performance of real-time multimedia services and the average energy of mobile stations, the packet scheduling algorithm for guaranteeing the QoS requirements of real-time multicast services and the energy efficient packet scheduling algorithm for the real-time multicast and unicast services are proposed. The function of scheduling priority is defined by analyzing multiple utility factors, and the parallel scheduling is adopted to reduce the computation complexity; then the system model of the average energy of mobile stations is formulated by analyzing the energy for the transmission and states exchange of mobile stations, and the circulation and iteration algorithm is adopted to solve the minimization problem of the average energy of mobile stations. The simulation results show that the proposed packet scheduling algorithms can guarantee the minimum average energy of mobile stations with satisfying the delay performance of real-time multimedia services.
引文
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[1]M. Ylianttila, J. Makela and K. Pahlavan. Geolocation information and inter-technology handoff. Proceedings of IEEE ICC, Vol.3, PP.1573-1577, June.2000.
[2]H. P. Lin, R. T. Juang and D. B. Lin. Validation of an improved location-based handover algorithm using GSM measurement data. IEEE Transactions on Mobile Computing, Vol.4, No.5, PP.530-536,2005.
[3]A. Markopoulos, S. Kyriazakos, K. Tsagkaris, et al. Performance of cellular networks and mobile location-driven handover algorithms. Proceedings of IEEE VTC, Vol.4, PP.2430-2436, May.2004.
[4]R. Y. Kim, I. Jung, X. Y. Yang and C. C. Chou. Advanced handover schemes in IMT-advanced systems [WiMAX/LTE Update]. IEEE Communications Magazine, Vol.48, No.8, PP.78-85, Aug.2010.
[5]J. K. Seok and M. Gohar. Multicast handover agents for fast handover in wireless multicast networks. IEEE Communications Letters, Vol.14, No.7, PP.676-678,July.2010.
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[7]F. He and F. R. Wang. Position aware vertical handoff decision algorithm in heterogeneous wireless networks. Proceedings of IEEE WiCOM, PP.1-5, Sept.2008.
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[1]L. Du, J. Bigham and L. Cuthbert. Towards intelligent geographic load balancing for mobile cellular networks. IEEE Transactions on Systems, Man, and Cybernetics, Vol.33, No.4, PP.480-491,2003.
[2]C. Lee, H. Kang and T. Park. Dynamic sectorization of microcells for balanced traffic in cdma:genetic algorithms approach. IEEE Transactions on Vehicular Technology, Vol.51, No.1, PP.63-72,2002.
[3]S. Kyuho, C. Song and G. Veciana. Dynamic association for load balancing and interference avoidance in multi-cell networks. IEEE Transactions on Wireless Communicarions, Vol.8, No.7, PP.3566-3576,2009.
[4]A. F. AI Rawi, B. S. Sharif and C. C. Tsimenidis. User priority aware scheduling and dynamic resource allocation in orthogonal frequency division multiple access. IET Communicarions, Vol.5, No.7, PP.1006-1019,2011.
[5]R. Aggarwal, M. Assaad, C. E. Koksal and P. Schniter. Joint scheduling and resource allocation in the OFDMA downlink:utility maximization under imperfect channel-state information. IEEE Transactions on Signal Processing, Vol.59, No.11,PP.5589-5604,2011.
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[1]K. H. Kim, H. K. Kang and K. Kim. Providing quality of service in adaptive resource allocation for OFDMA systems. Proceedings of IEEE VTC, Vol.3, PP.1612-1615, May.2004.
[2]P. Svedman, S. K. Wilson and B. Ottersten. A QoS-aware proportional fair scheduler for opportunistic OFDM. Proceedings of IEEE VTC, PP.558-562, 2004.
[3]Z. Kong, Y. K. Kwok and J. Z. Wang. A low-complexity QoS-aware proportional fair multicarrier scheduling algorithm for OFDM systems. IEEE Transactions on Vehicular Technology, Vol.58, No.5, PP.2225-2235, June.2009.
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[5]J. Hajipour and V. C. M. Leung. Proportional fair scheduling in multi-carrier networks using channel predictions. Proceedings of IEEE ICC, PP.1-5, May.2010.
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[8]M. Cecchi, R. Fantacci, D. Marabissi and D. Tarchi. Adaptive scheduling algorithms for multimedia traffic in wireless OFDMA systems. Proceedings of IEEE GLOBECOM, PP.1-5,2008.
[9]G. C. Song and Y. Li. Utility-based resource allocation and scheduling in OFDM-based wireless broadband networks. IEEE Communications Magazine, Vol.43, No.12, PP.127-134, Dec.2005.
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[12]J. Shi, G. Fang, Y. Sun, J. Zhou, Z. Li and E. Dutkiewicz. Improving mobile station energy efficiency in IEEE 802.16e WMAN by burst scheduling. Proceedings of IEEE GLOBECOM, PP.1-5,2006.
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[1]M. Ylianttila, J. Makela and K. Pahlavan. Geolocation information and inter-technology handoff. Proceedings of IEEE ICC, Vol.3, PP.1573-1577, June.2000.
[2]H. P. Lin, R. T. Juang and D. B. Lin. Validation of an improved location-based handover algorithm using GSM measurement data. IEEE Transactions on Mobile Computing, Vol.4, No.5, PP.530-536,2005.
[3]A. Markopoulos, S. Kyriazakos, K. Tsagkaris, et al. Performance of cellular networks and mobile location-driven handover algorithms. Proceedings of IEEE VTC, Vol.4, PP.2430-2436, May.2004.
[4]R. Y. Kim, I. Jung, X. Y. Yang and C. C. Chou. Advanced handover schemes in IMT-advanced systems [WiMAX/LTE Update]. IEEE Communications Magazine, Vol.48, No.8, PP.78-85, Aug.2010.
[5]J. K. Seok and M. Gohar. Multicast handover agents for fast handover in wireless multicast networks. IEEE Communications Letters, Vol.14, No.7, PP.676-678,July.2010.
[6]J. H. Lee, S. Pack, T. Kwon and Y. Choi. Reducing handover delay by location management in mobile WiMAX multicast and broadcast services. IEEE Transactions on Vehicular Technology, Vol.60, No.2, PP.605-617, Feb.2011.
[7]F. He and F. R. Wang. Position aware vertical handoff decision algorithm in heterogeneous wireless networks. Proceedings of IEEE WiCOM, PP.1-5, Sept.2008.
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[9]N. Saxena and A. Roy. Novel framework for proactive handover with seamless multimedia over WLANs. IET Communications, Vol.5, No.9, PP.1204-1212, June.2011.
[10]L. Song, A. J. Liu and Y. F. Ma. Adaptive handoff algorithm for multi-beam GEO mobile satellite system. Proceedings of IEEE ICC, PP.1947-1951, May.2008.
[11]D. Triantafyllopoulou, N. Passas, A. Kaloxylos and L. Merakos. Coordinated handover initiation and cross-layer adaptation for mobile multimedia systems. IEEE Transactions on Multimedia, Vol.11, No.6, PP.1131-1139, Sept.2009.
[12]L. Rong, Q. Haddada and S. Elayoubi. Analytical analysis of the coverage of a MBSFN OFDMA network. Proceedings of IEEE GLOBECOM, PP.1-5, Dec.2008.
[1]L. Du, J. Bigham and L. Cuthbert. Towards intelligent geographic load balancing for mobile cellular networks. IEEE Transactions on Systems, Man, and Cybernetics, Vol.33, No.4, PP.480-491,2003.
[2]C. Lee, H. Kang and T. Park. Dynamic sectorization of microcells for balanced traffic in cdma:genetic algorithms approach. IEEE Transactions on Vehicular Technology, Vol.51, No.1, PP.63-72,2002.
[3]S. Kyuho, C. Song and G. Veciana. Dynamic association for load balancing and interference avoidance in multi-cell networks. IEEE Transactions on Wireless Communicarions, Vol.8, No.7, PP.3566-3576,2009.
[4]A. F. AI Rawi, B. S. Sharif and C. C. Tsimenidis. User priority aware scheduling and dynamic resource allocation in orthogonal frequency division multiple access. IET Communicarions, Vol.5, No.7, PP.1006-1019,2011.
[5]R. Aggarwal, M. Assaad, C. E. Koksal and P. Schniter. Joint scheduling and resource allocation in the OFDMA downlink:utility maximization under imperfect channel-state information. IEEE Transactions on Signal Processing, Vol.59, No.11,PP.5589-5604,2011.
[6]Hongseok Kim, G. de Veciana, X. Y. Yang and M. Venkatachalam. Alpha-optimal user association and cell load balancing in wireless networks. Proceedings of IEEE INFOCOM, PP.1-5, Mar.2010.
[7]N. Ghaboosi and A. Jamalipour. A cooperative cellular architecture with emphasis on traffic load balancing. Proceedings of IEEE WCNC, PP.1-6, Apr.2010.
[8]W. C. Xu, C. Q. Hua and A. P. Huang. A game theoretical approach for load balancing user association in 802.11 wireless networks. Proceedings of IEEE GLOBECOM, PP.1-5, Dec.2010.
[9]P. Bahl, M. T. Hajiaghayi, K. Jain, et al. Cell breathing in wireless LANs: algorithms and evaluation. IEEE Transactions on Mobile Computing, Vol.6, No.2, PP.164-178,2007.
[10]Y. Bejerano and S. J. Han. Cell breathing techniques for load balancing in wireless LANs. IEEE Transactions on Mobile Computing, Vol.8, No.6, PP.735-749,2009.
[11]V. D. Papoutsis and S. A. Kotsopoulos. Chunk-based resource allocation in multicast OFDMA systems with average BER constraint. IEEE Communicarions Letter, Vol.15, No.5, PP.551-553,2011.
[12]C. C. Ribeiro, S. L. Martins and I. Rosseti. Metaheuristics for Optimization Problems in Computer Communications. Computer Networks, Vol.30, No.4, PP.656-669,2007.
[1]K. H. Kim, H. K. Kang and K. Kim. Providing quality of service in adaptive resource allocation for OFDMA systems. Proceedings of IEEE VTC, Vol.3, PP.1612-1615, May.2004.
[2]P. Svedman, S. K. Wilson and B. Ottersten. A QoS-aware proportional fair scheduler for opportunistic OFDM. Proceedings of IEEE VTC, PP.558-562, 2004.
[3]Z. Kong, Y. K. Kwok and J. Z. Wang. A low-complexity QoS-aware proportional fair multicarrier scheduling algorithm for OFDM systems. IEEE Transactions on Vehicular Technology, Vol.58, No.5, PP.2225-2235, June.2009.
[4]Q. Li and R. Negi. Scheduling in wireless networks under uncertainties. Proceedings of IEEE ICC, PP.1-5, May.2010.
[5]J. Hajipour and V. C. M. Leung. Proportional fair scheduling in multi-carrier networks using channel predictions. Proceedings of IEEE ICC, PP.1-5, May.2010.
[6]L. Xiao, S. D. Zhou and Y. Yao. QoS-oriented scheduling algorithm for mobile multimedia in OFDM. Proceedings of IEEE PIMRC, Vol.1, PP.545-549, Sept.2003.
[7]Y. J. Wang, S. Z. Xu, F. Q. Liu, X. H. Wang, Y. Q. Qian and Y. L. Wang. A QoS-oriented cross-layer packet scheduling algorithm for a downlink wireless OFDMA system. Proceedings of IEEE CCWMSN, PP.793-796, Dec.2007.
[8]M. Cecchi, R. Fantacci, D. Marabissi and D. Tarchi. Adaptive scheduling algorithms for multimedia traffic in wireless OFDMA systems. Proceedings of IEEE GLOBECOM, PP.1-5,2008.
[9]G. C. Song and Y. Li. Utility-based resource allocation and scheduling in OFDM-based wireless broadband networks. IEEE Communications Magazine, Vol.43, No.12, PP.127-134, Dec.2005.
[10]X. L. Zhang, M. X. Tao and C. S. Ng. A generalized gradient scheduling algorithm in wireless networks for variable rate transmission. Proceedings of IEEE GLOBECOM, PP.3473-3477,2007.
[11]Z. X. Chen, K. Xu, F. Jiang, Y. Wang and P. Zhang. Utility based scheduling algorithm for multiple services per user in MIMO OFDM system. Proceedings of IEEE ICC, PP.4734-4738, May.2008.
[12]J. Shi, G. Fang, Y. Sun, J. Zhou, Z. Li and E. Dutkiewicz. Improving mobile station energy efficiency in IEEE 802.16e WMAN by burst scheduling. Proceedings of IEEE GLOBECOM, PP.1-5,2006.
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