多用户MIMO中继系统包含直传链路的联合预编码
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摘要
针对现有的联合预编码算法忽略多用户之间的直传链路而导致通信性能下降的问题,本文以最小和均方误差为设计准则,提出了一种发射端的所有用户集中优化的联合预编码算法。首先,由于MSMSE优化问题的非凸性,将单流信号输入的多用户等效为一个多流信号输入的基站进行优化处理,其中基站的预编码矩阵由所有发射端的用户的子预编码矩阵构成,原非凸的MSMSE优化问题被转化为发射端用户的子预编码矩阵和中继收发矩阵2个子优化问题分别进行处理。其次,根据最小均方误差接收滤波理论,直接求解出接收滤波矩阵的优化表达式。发射端用户的子预编码矩阵的子优化问题通过求解条件获得,中继收发矩阵的子优化问题被转化成一个标准的半正定问题进行优化处理。最后,发射端用户的子预编码矩阵、中继收发矩阵和接收滤波矩阵联合迭代至收敛,得到优化后的预编码矩阵。实验仿真结果表明:提出的联合预编码算法在系统和均方误差性能和误码率性能上都有明显的提升,并且仿真进一步验证了算法有快速的收敛速度。
To solve the problem of existing precoding algorithms that fail to improve communication performance because they ignore the direct links between multiple users,we design a joint precoding algorithm based on minimum sum mean-square-error( MSMSE) design criteria. In this algorithm,all users at the transmitter are optimized in a centralized manner. First,as the MSMSE optimization problem is non-convex,all multiple users of a single-stream signal input are equivalent to a base station of multi-stream signal input for optimization,in which the precoding matrix of the base station consists of sub-precoding matrixes of all the transmitting users. The original problem is transformed into two sub-optimization problems to be solved: transmitter user' s sub-precoding matrix and relay transmitter/receiver matrix. Second,according to the minimum mean-square-error receiving filter theory,expressions of the receiving filter matrixes are derived directly. The optimal expressions of the transmitter sub-precoding matrices are solved under Karush-Kuhn-Tucker conditions. To optimize the relay transmitter/receiver matrix,we transform the optimization problem into a standard semi-definite programming problem. Finally,the transmitter subprecoding matrices,relay amplifying matrix,and receiving filter matrixes are optimized iteratively,and the solution is obtained after the optimization of the algorithm. Numerical results show that the proposed algorithm offers good performance in sum mean-square-error and bit-error rate. In addition,the algorithm exhibits fast convergence.
To solve the problem of existing precoding algorithms that fail to improve communication performance because they ignore the direct links between multiple users,we design a joint precoding algorithm based on minimum sum mean-square-error( MSMSE) design criteria. In this algorithm,all users at the transmitter are optimized in a centralized manner. First,as the MSMSE optimization problem is non-convex,all multiple users of a single-stream signal input are equivalent to a base station of multi-stream signal input for optimization,in which the precoding matrix of the base station consists of sub-precoding matrixes of all the transmitting users. The original problem is transformed into two sub-optimization problems to be solved: transmitter user' s sub-precoding matrix and relay transmitter/receiver matrix. Second,according to the minimum mean-square-error receiving filter theory,expressions of the receiving filter matrixes are derived directly. The optimal expressions of the transmitter sub-precoding matrices are solved under Karush-Kuhn-Tucker conditions. To optimize the relay transmitter/receiver matrix,we transform the optimization problem into a standard semi-definite programming problem. Finally,the transmitter subprecoding matrices,relay amplifying matrix,and receiving filter matrixes are optimized iteratively,and the solution is obtained after the optimization of the algorithm. Numerical results show that the proposed algorithm offers good performance in sum mean-square-error and bit-error rate. In addition,the algorithm exhibits fast convergence.
引文
[1]SHEKHAR,SINGHAL A,SHARMA R,et al.Study of analysis of multiple input and multiple outputs(MIMO)technology in wireless communication[C]//Proceedings of2016 International Conference on Micro-Electronics and Telecommunication Engineering(ICMETE).Ghaziabad,India:IEEE,2016:658-662.
[2]SETIAWAN D P,ZHAO Huaan.Performance analysis of hybrid AF and DF protocol for relay Networks[C]//Proceedings of 2017 International Conference on Control,Electronics,Renewable Energy and Communications(IC-CEREC).Yogyakarta,Indonesia:IEEE,2017:207-211.
[3]KHANDAKER M R A,RONG Yue.Joint transceiver optimization for multiuser MIMO relay communication systems[J].IEEE transactions on signal processing,2012,60(11):5977-5986.
[4]SHEN Hui,LI Bin,TAO Meixia,et al.MSE-based transceiver designs for the MIMO interference channel[J].IEEEtransactions on wireless communications,2010,9(11):3480-3489.
[5]LIU Jun,LIU Zhi,QIU Zhengding.Joint MMSE transceiver design for multiuser non-regenerative MIMO relay downlink systems[C]//Proceedings of the 7th International Wireless Communications and Mobile Computing Conference.Istanbul,Turkey:IEEE,2011:877-882.
[6]SHIN J,MOON J,AHN J.MMSE-based filter design for multi-user peer-to-peer MIMO amplify-and-forward relay systems[C]//Proceedings of 2013 IEEE International Conference on Communications.Budapest,Hungary:IEEE,2013:5505-5510.
[7]HE Zhiqiang,JIANG Weipeng,RONG Yue.Robust design for amplify-and-forward MIMO relay systems with direct link and imperfect channel information[J].IEEE transactions on wireless communications,2015,14(1):353-363.
[8]HE Zhiqiang,ZHANG Jinnian,LIU Wanning,et al.New results on transceiver design for two-hop amplify-and-forward MIMO relay systems with direct link[J].IEEE transactions on signal processing,2016,64(20):5232-5241.
[9]WAN Haibin,CHEN Wen.Joint source and relay design for multiuser MIMO nonregenerative relay networks with direct links[J].IEEE transactions on vehicular technology,2012,61(6):2871-2876.
[10]SUN Qi,LI Lihua.Weighted sum rate maximization for downlink multiuser relay network with direct link[J].Wireless personal communications,2014,75(1):369-384.
[11]MEHANA A H,NOSRATINIA A.MMSE receive filtering for precoded MIMO systems[C]//Proceedings of 2013Asilomar Conference on Signals,Systems and Computers.Pacific Grove,CA,USA:IEEE,2013:2057-2061.
[12]BOYD S,VANDENBERGHE L.Convex optimization[M].Cambridge:Cambridge University Press,2004.
[13]ZHANG Xianda.Matrix analysis and applications[M].Cambridge:Cambridge University Press,2017:71-100.
[14]GRANT M,BOYD S.CVX:Matlab software for disciplined convex programming[EB/OL].http://stanford.edu/boyd/cvx.
[15]LOBO M S,VANDENBERGHE L,BOYD S,et al.Applications of second-order cone programming[J].Linear algebra and its applications,1998,284(1/2/3):193-228.
[2]SETIAWAN D P,ZHAO Huaan.Performance analysis of hybrid AF and DF protocol for relay Networks[C]//Proceedings of 2017 International Conference on Control,Electronics,Renewable Energy and Communications(IC-CEREC).Yogyakarta,Indonesia:IEEE,2017:207-211.
[3]KHANDAKER M R A,RONG Yue.Joint transceiver optimization for multiuser MIMO relay communication systems[J].IEEE transactions on signal processing,2012,60(11):5977-5986.
[4]SHEN Hui,LI Bin,TAO Meixia,et al.MSE-based transceiver designs for the MIMO interference channel[J].IEEEtransactions on wireless communications,2010,9(11):3480-3489.
[5]LIU Jun,LIU Zhi,QIU Zhengding.Joint MMSE transceiver design for multiuser non-regenerative MIMO relay downlink systems[C]//Proceedings of the 7th International Wireless Communications and Mobile Computing Conference.Istanbul,Turkey:IEEE,2011:877-882.
[6]SHIN J,MOON J,AHN J.MMSE-based filter design for multi-user peer-to-peer MIMO amplify-and-forward relay systems[C]//Proceedings of 2013 IEEE International Conference on Communications.Budapest,Hungary:IEEE,2013:5505-5510.
[7]HE Zhiqiang,JIANG Weipeng,RONG Yue.Robust design for amplify-and-forward MIMO relay systems with direct link and imperfect channel information[J].IEEE transactions on wireless communications,2015,14(1):353-363.
[8]HE Zhiqiang,ZHANG Jinnian,LIU Wanning,et al.New results on transceiver design for two-hop amplify-and-forward MIMO relay systems with direct link[J].IEEE transactions on signal processing,2016,64(20):5232-5241.
[9]WAN Haibin,CHEN Wen.Joint source and relay design for multiuser MIMO nonregenerative relay networks with direct links[J].IEEE transactions on vehicular technology,2012,61(6):2871-2876.
[10]SUN Qi,LI Lihua.Weighted sum rate maximization for downlink multiuser relay network with direct link[J].Wireless personal communications,2014,75(1):369-384.
[11]MEHANA A H,NOSRATINIA A.MMSE receive filtering for precoded MIMO systems[C]//Proceedings of 2013Asilomar Conference on Signals,Systems and Computers.Pacific Grove,CA,USA:IEEE,2013:2057-2061.
[12]BOYD S,VANDENBERGHE L.Convex optimization[M].Cambridge:Cambridge University Press,2004.
[13]ZHANG Xianda.Matrix analysis and applications[M].Cambridge:Cambridge University Press,2017:71-100.
[14]GRANT M,BOYD S.CVX:Matlab software for disciplined convex programming[EB/OL].http://stanford.edu/boyd/cvx.
[15]LOBO M S,VANDENBERGHE L,BOYD S,et al.Applications of second-order cone programming[J].Linear algebra and its applications,1998,284(1/2/3):193-228.