We investigate the sum capacity of Block Diagonalization precoding Multiple Input Mul-tiple Output Broadcast Channels(BD MIMO BC) with imperfect Channel State Information(CSI) at the base station.Since it is difficult...We investigate the sum capacity of Block Diagonalization precoding Multiple Input Mul-tiple Output Broadcast Channels(BD MIMO BC) with imperfect Channel State Information(CSI) at the base station.Since it is difficult to obtain the exact expression,a lower and an upper bounds of the sum capacity under Gaussian channel estimation errors are drived instead.Analyses show that the gap between two bounds is considerably tight at all Signal to Noise Ratio(SNR) region.From the lower bound of the sum capacity,we can see that the multiplexing gain tends to be zero at high SNR region,which indicates that the BD MIMO BC system with channel estimation errors is interference-limited at high SNR.展开更多
User selection is necessary for multiuser multiple-input multiple-output(MIMO) downlink systems with block diagonalization(BD) due to the limited free spatial transmit dimensions.The pure user selection algorithms can...User selection is necessary for multiuser multiple-input multiple-output(MIMO) downlink systems with block diagonalization(BD) due to the limited free spatial transmit dimensions.The pure user selection algorithms can be improved by performing receive antenna selection(RAS) to increase sum rate.In this paper,a joint user and antenna selection algorithm,which performs user selection for sum rate maximization in the first stage and then performs antenna selection in the second stage,is proposed.The antenna selection process alternately drops one antenna with the poorest channel quality based on maximum determinant ranking(MDR) from the users selected during the first stage and activates one antenna with the maximum norm of projected channel from the remaining users.Simulation results show that the proposed algorithm significantly outperforms the algorithm only performing user selection as well as the algorithm combining user selection with MDR receive antenna selection in terms of sum rate.展开更多
基金Supported by Chinese 863 Program (2006AA01Z268)the National Natural Science Foundation of China (No. 60496311)
文摘We investigate the sum capacity of Block Diagonalization precoding Multiple Input Mul-tiple Output Broadcast Channels(BD MIMO BC) with imperfect Channel State Information(CSI) at the base station.Since it is difficult to obtain the exact expression,a lower and an upper bounds of the sum capacity under Gaussian channel estimation errors are drived instead.Analyses show that the gap between two bounds is considerably tight at all Signal to Noise Ratio(SNR) region.From the lower bound of the sum capacity,we can see that the multiplexing gain tends to be zero at high SNR region,which indicates that the BD MIMO BC system with channel estimation errors is interference-limited at high SNR.
基金the National Science and Technology Major Project (No.2009ZX03002-003)
文摘User selection is necessary for multiuser multiple-input multiple-output(MIMO) downlink systems with block diagonalization(BD) due to the limited free spatial transmit dimensions.The pure user selection algorithms can be improved by performing receive antenna selection(RAS) to increase sum rate.In this paper,a joint user and antenna selection algorithm,which performs user selection for sum rate maximization in the first stage and then performs antenna selection in the second stage,is proposed.The antenna selection process alternately drops one antenna with the poorest channel quality based on maximum determinant ranking(MDR) from the users selected during the first stage and activates one antenna with the maximum norm of projected channel from the remaining users.Simulation results show that the proposed algorithm significantly outperforms the algorithm only performing user selection as well as the algorithm combining user selection with MDR receive antenna selection in terms of sum rate.
文摘在多用户MIMO系统下行链路中,块对角化(Block diagonalization,BD)预编码算法的和速率性能要优于匹配滤波算法(Matched filter,MF)和迫零算法(Zero-forcing,ZF)。然而,传统的BD算法利用矩阵分解来构造除当前用户的其他所有用户信道的零空间,需要O(N2)浮点运算次数(Float point operations,FLOPs)。当基站的天线数N趋向于大规模时,BD算法计算复杂度巨大。本文提出一种基于投影子方法构造其他用户合成信道的零空间的BD算法,该算法仅需O(N)FLOPs。仿真表明:同传统的BD算法相比,本文所提出的低复杂度BD算法显著地降低了实现复杂度,而和速率性能损失微小,仍然优于MF和ZF,并且当N趋向于大规模时,它的和速率性能趋向于传统的BD算法和SVD算法。
