大规模MIMO系统中基于迫零预编码的能效优化算法

doi:10.19678/j.issn.1000-3428.0047326

计算机工程 ›› 2018, Vol. 44 ›› Issue (7): 121-125,130. doi: 10.19678/j.issn.1000-3428.0047326

大规模MIMO系统中基于迫零预编码的能效优化算法

李民政^1,2,3,李艳峰¹

1.桂林电子科技大学计算机与信息安全学院,广西桂林 541004; 2.广西信息科学实验中心,广西桂林 541004; 3.广西可信软件重点实验室,广西桂林 541004

收稿日期:2017-05-23 出版日期:2018-07-15 发布日期:2018-07-15
作者简介:李民政(1972—),男,教授、博士,主研方向为编码技术、模式识别;李艳峰,硕士研究生。
基金资助:
国家自然科学基金(61362007);广西信息科学实验中心项目(YB1407);广西可信软件重点实验室基金(KX201414)。

Energy Efficiency Optimal Algorithm Based on Zero Forcing Precoding in Massive MIMO System

LI Minzheng^1,2,3,LI Yanfeng¹

1.School of Computer and Information Security,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China; 2.Guangxi Information Science Research Center,Guilin,Guangxi 541004,China; 3.Guangxi Key Lab of Trusted Software,Guilin,Guangxi 541004,China

Received:2017-05-23 Online:2018-07-15 Published:2018-07-15

摘要/Abstract

摘要：

在大规模多输入多输出无线通信系统中,为提高能效的利用率,提出一种用于优化下行链路能效的功率分配算法。基于迫零预编码思想推导出速率与用户总功率的解析式。在满足用户最小数据速率要求和基站天线最大发射功率约束的条件下,建立能效优化模型,该模型同时考虑大尺度衰落和阴影衰落的实际功耗。通过对分式规划问题性质的探讨,将原始的约束非凸分式最优化问题转换成减式形式,进而利用拉格朗日定理给出低复杂度的功率分配方案。仿真结果表明,与基于标准干扰函数的算法相比,该算法的系统能效提高明显。

关键词: 大规模多输入多输出天线系统, 下行链路, 能效, 资源分配, 迫零预编码

Abstract:

A power allocation algorithm for downlink energy efficiency optimization is proposed in Massive Multiple Input Multiple Output(Massive MIMO) wireless communication system to improve the utilization rate of energy efficiency.Based on the idea of Zero Forcing(ZF) precoding,the analytic expression of rate and total power of the user is deduced.The energy efficiency optimization model is established under the condition of satisfying the minimum data rate requirement of the user and the maximum transmit power constraint of the base station antenna.The model considers the practical power consumption of both large scale fading and shadow fading.According to the discuss of the properties of fractional programming,the original constraint nonconvex fractional optimization problem is transformed into a subtractive form.Using the Lagrangian theorem,a low complexity power allocation scheme is proposed.Simulation results show that the proposed algorithm can significantly improve the energy efficiency of the system compared with the algorithm based on the standard interference function.

Key words: Massive Multiple Input Multiple Output(Massive MIMO) antenna system, downlink, energy efficiency, resource allocation, Zero Forcing(ZF) precoding

中图分类号:

TP92

李民政,李艳峰. 大规模MIMO系统中基于迫零预编码的能效优化算法[J]. 计算机工程, 2018, 44(7): 121-125,130.

LI Minzheng,LI Yanfeng. Energy Efficiency Optimal Algorithm Based on Zero Forcing Precoding in Massive MIMO System[J]. Computer Engineering, 2018, 44(7): 121-125,130.

https://www.ecice06.com/CN/Y2018/V44/I7/121

参考文献

［1］BUZZI S,CHIH L I,KLEIN T E,et al.A survey of energy-efficient techniques for 5G networks and challenges ahead［J］.IEEE Journal on Selected Areas in Communications,2016,34(4):697-709.
［2］BJORNSON E,LARSSON E G,DEBBAH M.Optimizing multi-cell massive MIMO for spectral efficiency:how many users should be scheduled?［C］//Proceedings of IEEE Global Conference on Signal and Information Processing.Washington D.C.,USA:IEEE Press,2014:612-616.
［3］NITHYA A,ARUNACHALAPERUMAL C,ARUN C.Intelligent beam forming to improve the spectral efficiency in MIMO system［C］//Proceedings of International Conference on Information Communication and Embedded Systems.Washington D.C.,USA:IEEE Press,2015:1-4.
［4］KIM H,LEE S R,SONG C,et al.Optimal power allocation for energy efficiency maximization in distributed antenna systems［J］.IEEE Transactions on Communications,2015,63(2):5769-5773.
［5］ZHENG Zhengguang,DAN Lilin,GONG Saidang,et al.Energy-efficient resource allocation for downlink OFDMA systems［C］//Proceedings of IEEE International Conference on Communications Workshops.Washington D.C.,USA:IEEE Press,2013:391- 395.
［6］YUAN Jingya,LI Xiaohui,HEI Yongqiang,et al.Low complexity energy optimization algorithm for massive MIMO systems［J］.China Communications,2015,12(s1):74-82.
［7］RUSEK F,PERSSON D,LAU B K,et al.Scaling up MIMO:opportunities and challenges with very large arrays［J］.IEEE Signal Processing Magazine,2012,30(1):40-60.
［8］NGO H Q,LARSSON E G,MARZETTA T L.Energy and spectral efficiency of very large multiuser MIMO systems［J］.IEEE Transactions on Communications,2013,61(4):1436-1449.
［9］ZHAO Long,ZHAO Hui,HU Fanglong,et al.Energy efficient power allocation algorithm for downlink massive MIMO with MRT precoding［C］//Proceedings of the 78th Vehicular Technology Conference.Washington D.C.,USA:IEEE Press,2013:1-5.
［10］YING Hu,XU Daofeng,JI Baofeng,et al.Energy-efficiency of very large multi-user MIMO systems［C］//Proceedings of International Conference on Wireless Communications and Signal Processing.Washington D.C.,USA:IEEE Press,2012:1-5.
(下转第130页) (上接第125页) ［11］江爱珍,曾桂根.Massive MIMO上行系统能效资源分配算法［J］.计算机技术与发展,2016,26(10):200-204.
［12］胡莹,冀保峰,黄永明,等.基于上行多用户大规模MIMO系统能效优化算法［J］.通信学报,2015,36(1):72-78.
［13］王毅,林艳,李春国,等.基于能效优化的大规模MIMOFDD下行系统导频和功率资源分配算法［J］.电子与信息学报,2017,39(1):16-23.
［14］ZHANG Jiadian,JIANG Yanxiang,LI Peng,et al.Energy efficient power allocation in massive MIMO systems based on standard interference function［C］//Proceedings of Vehicular Technology Conference.Washington D.C.,USA:IEEE Press,2016:1-6.
［15］ISHEDEN C,CHONG Z,JORSWIECK E,et al.
Framework for link-level energy efficiency optimization with informed transmitter［J］.IEEE Transactions on Wireless Communications,2012,11(8):2946-2957.
［16］DINKELBACH W.On nonlinear fractional program-ming［J］.Management Science,1967,13(7):492-498.
［17］BOYD S,VANDENBERGHE L,FAYBUSOVICH L.Convex optimization［J］.IEEE Transactions on Automatic Control,2006,51(11):1859.
［18］陈玉磊,季新生,黄开枝,等.多用户OFDM系统物理层安全资源分配方案［J］.计算机工程,2013,39(8):156-160.
［19］RDENAS R G,LPEZ M L,VERASTEGUI D.Extensions of Dinkelbach’s algorithm for solving non-linear fractional programming problems［J］.TOP,1999,7(1):33-70.

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