Abstract：As non-orthogonal multiple access technology can achieve higher system throughput,spectrum efficiency and energy efficiency than traditional orthogonal access technology,it has become a research hotspot of 5G multiple access technology.In this paper,a power allocation strategy based on Improved Particle Swarm Optimization (IPSO) is proposed to optimize the energy efficiency of NOMA downlink system.The Standard Particle Swarm Optimization (SPSO) is improved in three aspects,and the IPSO algorithm is used to solve the objective function to maximize the energy efficiency of the system.The simulation results show that at the optimal power allocation point,the IPSO algorithm can significantly improve the energy efficiency of the system.
[1] Islam S M R,Avazov N,Dobre O A,et al.Power-domain non-orthogonal multiple access (NOMA) in 5G systems:Potentials and challenges[J].IEEE Communications Surveys & Tutorials,2016,19(2):721-742.
[2] Al-Imari M,Xiao P,Imran M A,et al.Uplink non-orthogonal multiple access for 5G wireless networks[A].The 11th International Symposium on Wireless Communications Systems (ISWCS)[C].USA:IEEE,2014.781-785.
[3] Islam S M R,Avazov N,Dobre O A,et al.Power-domain non-orthogonal multiple access (NOMA) in 5G systems:Potentials and challenges[J].IEEE Communications Surveys & Tutorials,2016,19(2):721-742.
[4] Sun Y,Ng D W K,Ding Z,et al.Optimal joint power and subcarrier allocation for full-duplex multicarrier non-orthogonal multiple access systems[J].IEEE Transactions on Communications,2017,65(3):1077-1091.
[5] Lei L,Yuan D,Ho C K,et al.Power and channel allocation for non-orthogonal multiple access in 5G systems:Tractability and computation[J].IEEE Transactions on Wireless Communications,2016,15(12):8580-8594.
[6] Kazmi S M A,Tran N H,Saad W,et al.Mode selection and resource allocation in device-to-device communications:A matching game approach[J].IEEE Transactions on Mobile Computing,2017,16(11):3126-3141.
[7] Parida P,Das S S.Power allocation in OFDM based NOMA systems:A DC programming approach[A].IEEE Globecom Workshops (GC Wkshps)[C].USA:IEEE,2014.1026-1031.
[8] Kim J B,Lee I H.Capacity analysis of cooperative relaying systems using non-orthogonal multiple access[J].IEEE Communications Letters,2015,19(11):1949-1952.
[9] Hsiung C,Huang R,Zhou Y,et al.Dynamic user pairing and power allocation for throughput maximization in NOMA systems[A].IEEE International Conference on Communications Workshops (ICC Workshops)[C].USA:IEEE,2019.1-6.
[10] Saito Y,Benjebbour A,Kishiyama Y,et al.System-level performance evaluation of downlink non-orthogonal multiple access (NOMA)[A].The 24th IEEE Annual International Symposium on Personal,Indoor,and Mobile Radio Communications (PIMRC)[C].USA:IEEE,2013.611-615.
[11] 蒋继胜,朱晓荣.H2H与M2M共存场景下的上行资源分配算法[J].电子学报,2018,46(5):1259-1264. JIANG Ji-sheng,ZHU Xiao-rong.An uplink resource allocation algorithm under the scenario of coexistence of H2H & M2M based on knapsack model[J].Acta Electronica Sinica,2018,46(5):1259-1264.(in Chinese)
[12] Fang F,Zhang H,Cheng J,et al.Energy-efficient resource allocation for downlink non-orthogonal multiple access network[J].IEEE Transactions on Communications,2016,64(9):3722-3732.
[13] Wang J,Xu H,Fan L,et al.Energy-efficient joint power and bandwidth allocation for NOMA systems[J].IEEE Communications Letters,2018,22(4):780-783.
[14] Muhammed A J,Ma Z,Diamantoulakis P D,et al.Energy-efficient resource allocation in multicarrier NOMA systems with fairness[J].IEEE Transactions on Communications,2019,67(12):8639-8654.
[15] Vien Q T,Le T A,Barn B,et al.Optimising energy efficiency of non-orthogonal multiple access for wireless backhaul in heterogeneous cloud radio access network[J].IET Communications,2016,10(18):2516-2524.
[16] Zhou F,Wu Y,Hu R Q,et al.Energy-efficient NOMA enabled heterogeneous cloud radio access networks[J].IEEE Network,2018,32(2):152-160.
[17] Kennedy J.Particle swarm optimization[A].Encyclopedia of Machine Learning[M].Boston,MA:Springer,2010.760-766.
[18] Shi Y,Eberhart R.A modified particle swarm optimizer[A].IEEE International Conference on Evolutionary Computation Proceedings[C].USA:IEEE,1998.69-73.
[19] Yu Y F,Li G,Xu C.An improved particle swarm optimization algorithm[J].Applied Mechanics and Materials,2013,401:1328-1335.
[20] 孙兰兰,王晓超.求解高维函数优化的动态粒子群算法[J].计算机工程与应用,2011,47(27):36-37,51.
[21] 邓先礼,魏波,曾辉,桂凌,夏学文.基于多种群的自适应迁移PSO算法[J].电子学报,2018,46(8):1858-1865. DENG Xian-li,WEI Bo,ZENG Hui,GUI Ling,XIA Xue-wen.A multi-population based self-adaptive migration PSO[J].Acta Electronica Sinica,2018,46(8):1858-1865.(in Chinese)
[22] 孙辉,邓志诚,赵嘉,王晖,谢海华.混合均值中心反向学习粒子群优化算法[J].电子学报,2019,47(9):1809-1818. SUN Hui,DENG Zhi-cheng,ZHAO Jia,WANG Hui,XIE Hai-hua.Hybrid mean center opposition-based learning particle swarm optimization[J].Acta Electronica Sinica,2019,47(9):1809-1818.(in Chinese)
[23] Chang Z,Hou X,Guo X,et al.Energy efficient resource allocation for secure OFDMA relay systems with eavesdropper[A].IEEE International Conference on Communications (ICC)[C].USA:IEEE,2016.1-7.
[24] Zhu J,Wang J,Huang Y,et al.On optimal power allocation for downlink non-orthogonal multiple access systems[J].IEEE Journal on Selected Areas in Communications,2017,35(12):2744-2757.
[25] Bansal J C,Singh P K,Saraswat M,et al.Inertia weight strategies in particle swarm optimization[A].The Third World Congress on Nature and Biologically Inspired Computing[C].USA:IEEE,2011.633-640.
[26] Zhang L,Tang Y,Hua C,et al.A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques[J].Applied Soft Computing,2015,28:138-149.
[27] Ardizzon G,Cavazzini G,Pavesi G.Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms[J].Information Sciences,2015,299:337-378.
2020, Vol. 48 
