OFDMA系统中自适应资源分配算法研究
|
摘要
下一代无线通信系统需要支持高速率传输的无线多媒体业务,由于无线资源的稀缺性,如何合理有效利用无线资源是移动通信领域研究的重点。目前无线资源管理方面的研究主要包括资源控制、资源分配和资源调度三个方面。本文的研究重点是资源分配,通过改善OFDMA系统中的资源分配算法提高系统的性能。在下一代无线通信中,由于业务量的增大,能源的消耗也随之增加,因此需要提高系统效率降低整体能耗,实现绿色运营。本文从能效的角度考虑,通过自适应资源分配尽量最大化系统能效。
目前OFDMA系统自适应资源分配算法中,最优算法的复杂度很高。本文以最小化系统总功率为优化目标,提出了一种复杂度较低的子载波-比特同时分配次优解算法。本算法不限制每个用户的子载波数目和子载波集合,在分配子载波的同时分配比特。本算法首先将用户的最小传输速率需求作为用户的需求因子,需求因子越大则表示用户对子载波和比特的需求程度越大,按照用户需求因子的大小依次为每个用户分配1条子载波和1bit；然后再将用户最小传输速率需求与当前时刻传输速率之比作为用户的需求因子,该比值越大则需求程度越大,根据需求程度的高低依次为用户分配子载波和比特,直到所有用户的最小传输速率需求被满足或者所有子载波和比特被分配完毕为止。在分配过程中,用户的需求因子是动态变化的。通过MATLAB仿真将本文算法与BABS-RCG算法和BABS-ACG算法进行了对比,结果表明本文算法的系统总功率和算法复杂度均得到了一定改善。
此外,本文还研究了OFDMA系统中以能效为优化目标的自适应资源分配问题,采用了先子载波分配后比特分配的思想来实现最大化系统能效。由于以系统能效为优化目标的自适应比特-功率分配问题是一个非线性问题,不能通过用户之间的依次叠加来计算。因此,本文利用粒子群算法来解决自适应比特-功率分配问题。本算法中将多用户的比特分配方案作为粒子,通过计算系统能效来评价每个粒子的适应度,经过不断迭代寻找到最优的比特分配方案。通过MATLAB仿真,将本文算法与遗传算法进行了对比,结果表明粒子群算法的寻优能力和收敛性都要优于遗传算法,而且在用户的最小传输速率需求刚被满足时,系统的能效最大。
The next-generation of wireless communication systems need to support high data rate wireless multimedia services. Due to the scarcity of radio resource, It is the key research point in the field of mobile communications that how to use the radio resource efficiently. Currently, researches of radio resource management have mainly focus on three aspects, namely, resource control, resource allocation and resource scheduling. This paper aims at the problem of resource allocation and intending to increase the system performance through improving the algorithm of resource allocation in the orthogonal frequency division multiple access (OFDMA) systems. In the next-generation wireless communication systems, the consumption of energy is increasing with the increment of traffic, so it is important to improve the efficiency of the systems and decrease the overall energy consumption for green operation. This paper attempts to maximize the energy efficiency via executing adaptive resource allocation algorithm.
The current research works in adaptive resource allocation of OFDMA systems usually adopt optimal algorithm with high complexity. This paper proposes a low complexity sub-optimal algorithm which allocates the sub-carrier and bit simultaneously, and formulates the optimal goal as to minimize the overall transmit power. The proposed algorithm has no limit for the number of sub-carriers and the sub-carrier set of every user. Firstly, we introduce the minimum data rate requirement as the user demand factor. The bigger the user demand factor is, the larger the requirement degree of user for sub-carrier and bit is. We allocate one sub-carrier and one bit to each user according to the user demand factor. Then, we use the ratio of the minimum data rate requirement and the instant rate as the user demand factor. Similarly, the bigger the user demand factor is, the larger the requirement degree of user for sub-carrier and bit is. We allocate the sub-carriers and bits to users according the sequence of user demand factor, until the minimum data rate requirements of all users are satisfied or all sub-carrier and bits are exhausted. Note that the user demand factor is dynamic during the process of allocation. We compare the performance of the proposed algorithm with BABS-RCG algorithm and BABS-ACG algorithm by matlab simulation. Simulation results indicate that the system power and the computational complexity of the proposed algorithm are significantly improved.
In addition, this paper addresses the adaptive resource allocation problem based on optimization of energy efficiency. We introduce the idea of sub-carrier allocation firstly and bit allocation secondly to maximize the energy efficiency. Since the problem of adaptive bit-power allocation with the goal of energy efficiency optimization is a nonlinear problem, it is impossible to solve the problem by ordinal iterative addition among users. This paper uses the Particle Swarm Optimization (PSO) to solve the adaptive bit-power allocation problem. We adopt the bit allocation scheme of multi-user as particle, and evaluate the fitness degree of every particle by computing the energy efficiency of system. In order to obtain optimal bit allocation scheme, we execute continuous iteration. We compare the performance of the proposed algorithm with Genetic Algorithm (GA) by matlab simulation. Simulation results show that both the seeking optimization ability and the convergence performance of PSO are better than GA. Furthermore, it will reach the optimization point of energy efficiency only when the data rate requirements of each user are just meet.
目前OFDMA系统自适应资源分配算法中,最优算法的复杂度很高。本文以最小化系统总功率为优化目标,提出了一种复杂度较低的子载波-比特同时分配次优解算法。本算法不限制每个用户的子载波数目和子载波集合,在分配子载波的同时分配比特。本算法首先将用户的最小传输速率需求作为用户的需求因子,需求因子越大则表示用户对子载波和比特的需求程度越大,按照用户需求因子的大小依次为每个用户分配1条子载波和1bit；然后再将用户最小传输速率需求与当前时刻传输速率之比作为用户的需求因子,该比值越大则需求程度越大,根据需求程度的高低依次为用户分配子载波和比特,直到所有用户的最小传输速率需求被满足或者所有子载波和比特被分配完毕为止。在分配过程中,用户的需求因子是动态变化的。通过MATLAB仿真将本文算法与BABS-RCG算法和BABS-ACG算法进行了对比,结果表明本文算法的系统总功率和算法复杂度均得到了一定改善。
此外,本文还研究了OFDMA系统中以能效为优化目标的自适应资源分配问题,采用了先子载波分配后比特分配的思想来实现最大化系统能效。由于以系统能效为优化目标的自适应比特-功率分配问题是一个非线性问题,不能通过用户之间的依次叠加来计算。因此,本文利用粒子群算法来解决自适应比特-功率分配问题。本算法中将多用户的比特分配方案作为粒子,通过计算系统能效来评价每个粒子的适应度,经过不断迭代寻找到最优的比特分配方案。通过MATLAB仿真,将本文算法与遗传算法进行了对比,结果表明粒子群算法的寻优能力和收敛性都要优于遗传算法,而且在用户的最小传输速率需求刚被满足时,系统的能效最大。
The next-generation of wireless communication systems need to support high data rate wireless multimedia services. Due to the scarcity of radio resource, It is the key research point in the field of mobile communications that how to use the radio resource efficiently. Currently, researches of radio resource management have mainly focus on three aspects, namely, resource control, resource allocation and resource scheduling. This paper aims at the problem of resource allocation and intending to increase the system performance through improving the algorithm of resource allocation in the orthogonal frequency division multiple access (OFDMA) systems. In the next-generation wireless communication systems, the consumption of energy is increasing with the increment of traffic, so it is important to improve the efficiency of the systems and decrease the overall energy consumption for green operation. This paper attempts to maximize the energy efficiency via executing adaptive resource allocation algorithm.
The current research works in adaptive resource allocation of OFDMA systems usually adopt optimal algorithm with high complexity. This paper proposes a low complexity sub-optimal algorithm which allocates the sub-carrier and bit simultaneously, and formulates the optimal goal as to minimize the overall transmit power. The proposed algorithm has no limit for the number of sub-carriers and the sub-carrier set of every user. Firstly, we introduce the minimum data rate requirement as the user demand factor. The bigger the user demand factor is, the larger the requirement degree of user for sub-carrier and bit is. We allocate one sub-carrier and one bit to each user according to the user demand factor. Then, we use the ratio of the minimum data rate requirement and the instant rate as the user demand factor. Similarly, the bigger the user demand factor is, the larger the requirement degree of user for sub-carrier and bit is. We allocate the sub-carriers and bits to users according the sequence of user demand factor, until the minimum data rate requirements of all users are satisfied or all sub-carrier and bits are exhausted. Note that the user demand factor is dynamic during the process of allocation. We compare the performance of the proposed algorithm with BABS-RCG algorithm and BABS-ACG algorithm by matlab simulation. Simulation results indicate that the system power and the computational complexity of the proposed algorithm are significantly improved.
In addition, this paper addresses the adaptive resource allocation problem based on optimization of energy efficiency. We introduce the idea of sub-carrier allocation firstly and bit allocation secondly to maximize the energy efficiency. Since the problem of adaptive bit-power allocation with the goal of energy efficiency optimization is a nonlinear problem, it is impossible to solve the problem by ordinal iterative addition among users. This paper uses the Particle Swarm Optimization (PSO) to solve the adaptive bit-power allocation problem. We adopt the bit allocation scheme of multi-user as particle, and evaluate the fitness degree of every particle by computing the energy efficiency of system. In order to obtain optimal bit allocation scheme, we execute continuous iteration. We compare the performance of the proposed algorithm with Genetic Algorithm (GA) by matlab simulation. Simulation results show that both the seeking optimization ability and the convergence performance of PSO are better than GA. Furthermore, it will reach the optimization point of energy efficiency only when the data rate requirements of each user are just meet.
引文
[1]蔡跃明,吴启晖,田华等.现代移动通信[M].北京：机械工业出版社,2010.
[2]周恩,张兴,吕召彪.下一代宽带无线通信OFDM与MIMO技术[M].北京：人民邮电出版社,2008.
[3]IEEE P802.16j/D1. Draft Standard for Local and Metropolitan Area Networks--Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems:Multihop Relay Specification [S], August 2007:1002-1007.
[4]R. Prasad. OFDM Wireless Multimedia Communications [M]. Artech House, 2000.
[5]C. Y. Wong, R.S. Cheng, K. B. Letaief, et al. Multiuser OFDM With Adaptive Subcarrier, Bit, and Power Allocation [J]. IEEE JSAC,1999,17:1747-1758.
[6]I. Kim, H. L. Lee, B. Kim, et al. On The Use of Linear Programming for Dynamic Subchannel and Bit Allocation in Multiuser OFDM [A]. IEEE Global Telecommunications Conference [C], San Antonio, Texas, USA,2001,6:3648-3652.
[7]L. Gao, S. G. Cui, F. F. Li. A Low-complexity Adaptive Subcarrier, Bit, and Power Allocation Algorithm for OFDMA Systems [A]. IEEE Global Telecommunications Conference [C], San Francisco, USA, 2006:1-6.
[8]D. Kivanc, G. Li, H. Liu. Computationally Efficient Bandwidth Allocation and Power Control for OFDMA [J]. IEEE TWC,2003,2(6): 1150-1158.
[9]G. D. Yu, Z. Y. Zhang, Y. Chen, et al. Subcarrier and Bit Allocation for OFDMA Systems with Proportional Fairness [A]. IEEE Wireless Communications and Networking Conference [C], Las Vegas, Nevada USA,2006,3:1717-1722.
[10]J. Tang, Y. Rahulamathavan, S. Lambotharan. Optimal Adaptive Bit Loading and Subcarrier Allocation Techniques for OFDM Based Cognitive Radio Systems [A]. IEEE International Conference on Communication Technology [C],2010:454-457.
[11]M. H. Le, R. Liyana-Pathirana. Unequal Error Protection Codes for Wavelet Image Transmission over WCDMA, AWGN and Rayleigh Fading Channels [A]. IEEE International Conference on Telecommunications [C], Tahiti, Papeete, French Polynesia, 2003,2:1140-1146.
[12]边广凤,周力OFDM多载波系统中的自适应技术研究[J].南昌航空工业学院院报,2006,20(3)：10-15.
[13]D. Hughers-Hartogs. Ensemble Modem Structure for Imperfect Transmission Media [J]. U. S. Patents Nos, 4 679 227, July 1987; 4 731 816, Mar. 1988; and 4 833 769, May 1989.
[14]佟学俭,罗涛OFDM移动通信技术原理与应用[M].北京：人民邮电出版社,2003.
[15]S. K. Lai, R. S. Cheng, K. B. Letaief, et al. Adaptive Trellis Coded MQAM and Power Optimization for OFDM Transmission [J]. IEEE VTC'99, 1999:290-294.
[16]S. K. Lai, R. S. Cheng, K. B. Letaief, et al. Adaptive Tracing of Optimal Bit and Power Allocation for OFDM System in Time-Varying Channels [J]. IEEE WCN'99,1999: 776-780.
[17]P. S. Chow, M. J. Cioffi, J. A. C. Bingham. A Practical Discrete Multitone Transceiver Loading Algorithm for Data Transmission over Spectrally Shaped Channels [J]. IEEE Transaction Communications,1995,43(234):773-775.
[18]R. F. H. Fischer, J. B. Huber. A New Loading Algorithm for Discrete Multitone Transmission [A]. IEEE Global Telecommunications Conference [C]. 1996,1:724-728.
[19]G. Munz, S. Pfletschinger, J. Speidel. An Efficient Waterfilling Algorithm for Multiple Access OFDM [A]. IEEE Global Telecommunications Conference [C], Taipei, Taiwan, 2002,1:681-685.
[20]J. Holland. Adaptive in Nature and Artificial Systems [M]. MIT Press,1975.
[21]J. Kennedy, R. C. Eberhart. Particle Swarm Optimization [A]. IEEE International Conference on Neural Networks [C],1995:1942-1948.
[22]R. C. Eberhart, J. Kennedy. A New Optimizer Using Particle Swarm Theory [A]. Proceeding of The Sixth International Symposium on Micromachine and Human Science [C], 1995:39-43.
[23]李晓磊.一种新型的智能优化方法——人工鱼群算法[D].浙江大学硕士学位论文,2003.
[24]李茂军,罗安,童调生.人工免疫算法及其应用研究[J].控制理论与应用,2004,21(2)：153-157.
[25]李大卫,王莉,王梦光.遗传算法在有时间床车路径问题上的应用[J].系统工程理论与实践,1999,19(8)：65-69.
[26]P. T. Leite, A. Carneiro, A. Carvaiho. Operation Planning Using Genetic Algorithm [J]. IEEE MPER, 2001,21(11):57.
[27]H. K. Wei, P. B. Wang, Z. M. Cai, et al. Application of Particle Swarm Optimization Method in Fractional Fourier Transform [A]. International Conference on Image Analysis and Signal Processing [C], Xiamen, China, 2010:442-445.
[28]Y. Yu, W. Y. Zhou. Resource Allocation for OFDMA System Base on Genetic Algorithm [A]. International Workshop on Cross Layer Design[C], Jinan, China, 2007:65-69.
[29]I. Ahmed, S. P. Majumder. Adaptive Resource Allocation Based on Modified Genetic Algorithm and Particle Swarm Optimization for Multiuser OFDM Systems [A]. International Conference on Electrical and Computer Engineering [C], Phuket, Thailand, 2008:211-216.
[30]毕晓君,崔巍巍.基于粒子群算法的OFDMA系统自适应子载波分配问题[J].哈尔滨工程大学学报,2010,31(8)：1100-1104.
[31]崔巍巍.基于智能化算法的OFDMA系统自适应资源分配算法[D].哈尔滨工业大学硕士学位论文,2010.
[32]程永明,江铭炎.基于改进人工鱼群算法的多用户OFDM系统自适应资源分配[J].计算机应用研究,2009,26(6)：2092-2094.
[33]雷鸣OFDM系统自适应资源分配算法研究[D].南昌航空大学硕士学位论文,2010.
[34]G. W. Miao, N. Himayat, G. Y. Li, et al. Energy-efficient Design in Wireless OFDMA [A]. International Conference on Communications [C], Beijing, China, 2008: 3307-3312.
[35]A. Zappone, G. Alfano, et al. Energy-efficient Non-cooperative Resource Allocation in Multi-Cell OFDMA Systems with Multiple Base Station Antenna [A]. IEEE Online Conference on Green Communications [C],2011:82-87.
[36]A. Akbari, R. Hoshyar, R. Tafazolli. Energy-efficient Resource Allocation in Wireless OFDMA Systems [A]. IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications [C], Istanbul, Turkey,2010:1731-1735.
[37]C. Y. Ho, C. Y. Huang. Energy Efficient Subcarrier-Power Allocation and Relay Selection Scheme for OFDMA-Based Cooperative Relay Networks [A]. IEEE International Conference on Communications [C], Kyoto, Japan, 2011:1-6.
[38]H. M. Kwon, T. G. Birdsall. Channel Capacity in Bits Per Joule [J]. IEEE JOE,1986, 11(1):97-99.
[39]郭梯云,杨家威,李建东.数字移动通信[M].北京：人民邮电出版社,2001.
[40]杨大成.现代移动通信中的先进技术[M].北京：机械工业出版社,2005.
[41]S. Weinstein, P. Ebert. Data Transmission by Frequency Division Multiplexing Using the Discrete Fourier Transform. Communications [J]. IEEE TCOM,1971,19(5):628-634.
[42]吴伟陵,牛凯.移动通信原理[M].北京：电子工业出版社,2006.
[43]汪裕民OFDM关键技术与应用[M].北京：机械工业出版社,2007.
[44]张弛OFDM的动态子载波功率分配算法[D].南京航空航天大学硕士学位论文,2006.
[45]樊昌信.通信原理(第5版)[M].北京：国防出版社,2006.
[46]J. Jang, K. B. Lee. Transmit Power Adaptation for Multiuser OFDM Systems [J]. IEEE JSAC,2003,21(2):171-178.
[47]严蔚敏,吴伟民.数据结构(C语言版)[M].北京：清华大学出版社,2009.
[48]纪震,廖惠连,吴青华.粒子群算法及其应用[M].北京：科学出版社,2009
[49]Y. Shi, R. C. Eberhart. Empirical Study of Particle Swarm Optimization [A]. Proceedings of the 1999 Congress on Evolutionary Computation [C], Washington, USA. 1999:1945-1950.
[50]R. C. Eberhart, Y. Shi. Tracking and Optimizing Dynamic Systems with Particle Swarms [A]. Proceedings of the 2001 Congress on Evolutionary Computation [C], San Francisco, USA,2001:94-100.
[51]陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权重递减策略研究[J].西安交通大学学报,2006,40(1)：53-56.
[52]陈国良,王熙法,庄镇泉等.遗传算法及其应用[M].北京：人民邮电出版社,1996.
[2]周恩,张兴,吕召彪.下一代宽带无线通信OFDM与MIMO技术[M].北京：人民邮电出版社,2008.
[3]IEEE P802.16j/D1. Draft Standard for Local and Metropolitan Area Networks--Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems:Multihop Relay Specification [S], August 2007:1002-1007.
[4]R. Prasad. OFDM Wireless Multimedia Communications [M]. Artech House, 2000.
[5]C. Y. Wong, R.S. Cheng, K. B. Letaief, et al. Multiuser OFDM With Adaptive Subcarrier, Bit, and Power Allocation [J]. IEEE JSAC,1999,17:1747-1758.
[6]I. Kim, H. L. Lee, B. Kim, et al. On The Use of Linear Programming for Dynamic Subchannel and Bit Allocation in Multiuser OFDM [A]. IEEE Global Telecommunications Conference [C], San Antonio, Texas, USA,2001,6:3648-3652.
[7]L. Gao, S. G. Cui, F. F. Li. A Low-complexity Adaptive Subcarrier, Bit, and Power Allocation Algorithm for OFDMA Systems [A]. IEEE Global Telecommunications Conference [C], San Francisco, USA, 2006:1-6.
[8]D. Kivanc, G. Li, H. Liu. Computationally Efficient Bandwidth Allocation and Power Control for OFDMA [J]. IEEE TWC,2003,2(6): 1150-1158.
[9]G. D. Yu, Z. Y. Zhang, Y. Chen, et al. Subcarrier and Bit Allocation for OFDMA Systems with Proportional Fairness [A]. IEEE Wireless Communications and Networking Conference [C], Las Vegas, Nevada USA,2006,3:1717-1722.
[10]J. Tang, Y. Rahulamathavan, S. Lambotharan. Optimal Adaptive Bit Loading and Subcarrier Allocation Techniques for OFDM Based Cognitive Radio Systems [A]. IEEE International Conference on Communication Technology [C],2010:454-457.
[11]M. H. Le, R. Liyana-Pathirana. Unequal Error Protection Codes for Wavelet Image Transmission over WCDMA, AWGN and Rayleigh Fading Channels [A]. IEEE International Conference on Telecommunications [C], Tahiti, Papeete, French Polynesia, 2003,2:1140-1146.
[12]边广凤,周力OFDM多载波系统中的自适应技术研究[J].南昌航空工业学院院报,2006,20(3)：10-15.
[13]D. Hughers-Hartogs. Ensemble Modem Structure for Imperfect Transmission Media [J]. U. S. Patents Nos, 4 679 227, July 1987; 4 731 816, Mar. 1988; and 4 833 769, May 1989.
[14]佟学俭,罗涛OFDM移动通信技术原理与应用[M].北京：人民邮电出版社,2003.
[15]S. K. Lai, R. S. Cheng, K. B. Letaief, et al. Adaptive Trellis Coded MQAM and Power Optimization for OFDM Transmission [J]. IEEE VTC'99, 1999:290-294.
[16]S. K. Lai, R. S. Cheng, K. B. Letaief, et al. Adaptive Tracing of Optimal Bit and Power Allocation for OFDM System in Time-Varying Channels [J]. IEEE WCN'99,1999: 776-780.
[17]P. S. Chow, M. J. Cioffi, J. A. C. Bingham. A Practical Discrete Multitone Transceiver Loading Algorithm for Data Transmission over Spectrally Shaped Channels [J]. IEEE Transaction Communications,1995,43(234):773-775.
[18]R. F. H. Fischer, J. B. Huber. A New Loading Algorithm for Discrete Multitone Transmission [A]. IEEE Global Telecommunications Conference [C]. 1996,1:724-728.
[19]G. Munz, S. Pfletschinger, J. Speidel. An Efficient Waterfilling Algorithm for Multiple Access OFDM [A]. IEEE Global Telecommunications Conference [C], Taipei, Taiwan, 2002,1:681-685.
[20]J. Holland. Adaptive in Nature and Artificial Systems [M]. MIT Press,1975.
[21]J. Kennedy, R. C. Eberhart. Particle Swarm Optimization [A]. IEEE International Conference on Neural Networks [C],1995:1942-1948.
[22]R. C. Eberhart, J. Kennedy. A New Optimizer Using Particle Swarm Theory [A]. Proceeding of The Sixth International Symposium on Micromachine and Human Science [C], 1995:39-43.
[23]李晓磊.一种新型的智能优化方法——人工鱼群算法[D].浙江大学硕士学位论文,2003.
[24]李茂军,罗安,童调生.人工免疫算法及其应用研究[J].控制理论与应用,2004,21(2)：153-157.
[25]李大卫,王莉,王梦光.遗传算法在有时间床车路径问题上的应用[J].系统工程理论与实践,1999,19(8)：65-69.
[26]P. T. Leite, A. Carneiro, A. Carvaiho. Operation Planning Using Genetic Algorithm [J]. IEEE MPER, 2001,21(11):57.
[27]H. K. Wei, P. B. Wang, Z. M. Cai, et al. Application of Particle Swarm Optimization Method in Fractional Fourier Transform [A]. International Conference on Image Analysis and Signal Processing [C], Xiamen, China, 2010:442-445.
[28]Y. Yu, W. Y. Zhou. Resource Allocation for OFDMA System Base on Genetic Algorithm [A]. International Workshop on Cross Layer Design[C], Jinan, China, 2007:65-69.
[29]I. Ahmed, S. P. Majumder. Adaptive Resource Allocation Based on Modified Genetic Algorithm and Particle Swarm Optimization for Multiuser OFDM Systems [A]. International Conference on Electrical and Computer Engineering [C], Phuket, Thailand, 2008:211-216.
[30]毕晓君,崔巍巍.基于粒子群算法的OFDMA系统自适应子载波分配问题[J].哈尔滨工程大学学报,2010,31(8)：1100-1104.
[31]崔巍巍.基于智能化算法的OFDMA系统自适应资源分配算法[D].哈尔滨工业大学硕士学位论文,2010.
[32]程永明,江铭炎.基于改进人工鱼群算法的多用户OFDM系统自适应资源分配[J].计算机应用研究,2009,26(6)：2092-2094.
[33]雷鸣OFDM系统自适应资源分配算法研究[D].南昌航空大学硕士学位论文,2010.
[34]G. W. Miao, N. Himayat, G. Y. Li, et al. Energy-efficient Design in Wireless OFDMA [A]. International Conference on Communications [C], Beijing, China, 2008: 3307-3312.
[35]A. Zappone, G. Alfano, et al. Energy-efficient Non-cooperative Resource Allocation in Multi-Cell OFDMA Systems with Multiple Base Station Antenna [A]. IEEE Online Conference on Green Communications [C],2011:82-87.
[36]A. Akbari, R. Hoshyar, R. Tafazolli. Energy-efficient Resource Allocation in Wireless OFDMA Systems [A]. IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications [C], Istanbul, Turkey,2010:1731-1735.
[37]C. Y. Ho, C. Y. Huang. Energy Efficient Subcarrier-Power Allocation and Relay Selection Scheme for OFDMA-Based Cooperative Relay Networks [A]. IEEE International Conference on Communications [C], Kyoto, Japan, 2011:1-6.
[38]H. M. Kwon, T. G. Birdsall. Channel Capacity in Bits Per Joule [J]. IEEE JOE,1986, 11(1):97-99.
[39]郭梯云,杨家威,李建东.数字移动通信[M].北京：人民邮电出版社,2001.
[40]杨大成.现代移动通信中的先进技术[M].北京：机械工业出版社,2005.
[41]S. Weinstein, P. Ebert. Data Transmission by Frequency Division Multiplexing Using the Discrete Fourier Transform. Communications [J]. IEEE TCOM,1971,19(5):628-634.
[42]吴伟陵,牛凯.移动通信原理[M].北京：电子工业出版社,2006.
[43]汪裕民OFDM关键技术与应用[M].北京：机械工业出版社,2007.
[44]张弛OFDM的动态子载波功率分配算法[D].南京航空航天大学硕士学位论文,2006.
[45]樊昌信.通信原理(第5版)[M].北京：国防出版社,2006.
[46]J. Jang, K. B. Lee. Transmit Power Adaptation for Multiuser OFDM Systems [J]. IEEE JSAC,2003,21(2):171-178.
[47]严蔚敏,吴伟民.数据结构(C语言版)[M].北京：清华大学出版社,2009.
[48]纪震,廖惠连,吴青华.粒子群算法及其应用[M].北京：科学出版社,2009
[49]Y. Shi, R. C. Eberhart. Empirical Study of Particle Swarm Optimization [A]. Proceedings of the 1999 Congress on Evolutionary Computation [C], Washington, USA. 1999:1945-1950.
[50]R. C. Eberhart, Y. Shi. Tracking and Optimizing Dynamic Systems with Particle Swarms [A]. Proceedings of the 2001 Congress on Evolutionary Computation [C], San Francisco, USA,2001:94-100.
[51]陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权重递减策略研究[J].西安交通大学学报,2006,40(1)：53-56.
[52]陈国良,王熙法,庄镇泉等.遗传算法及其应用[M].北京：人民邮电出版社,1996.