大规模MIMO系统中联合最强用户冲突解决的最大似然信道估计方案  

ML Channel Estimation Scheme Combining Strongest-User Conflict Resolution in Massive MIMO Systems

作　　者：吴军[1] 陈斌 康国良 Wu Jun;Chen Bin;Kang Guoliang(College of Information Engineering, Jiangxi University of Science and Technology, Jiangxi Ganzhou 341000)

机构地区：[1]江西理工大学信息工程学院,江西赣州341000

出　　处：《科技广场》2017年第12期99-105,共7页Science Mosaic

摘　　要：在多小区大规模多输入多输出(MIMO)系统中,针对各小区复用相同导频序列进行信道估计存在有导频污染问题,提出了一种大规模MIMO系统中联合最强用户冲突解决的最大似然(ML)信道估计算法。该方法联合最强用户冲突解决(SUCR)算法和循环前缀的最大似然信道估计算法,先在小区用最强用户冲突解决方案减轻用户之间的竞用相同导频的干扰,减少竞争导频的用户数,提高系统的成功接入用户数数量,然后再用循环前缀的最大似然的信道估计算法,对处理后的导频序列的信道进行信道估计,以获得较为准确的信道状态信息。仿真结果表明,提出的算法对比分析传统ML算法和MMSE算法能够得到更好的归一化均方误差和系统能量效率,并且可以较明显地减轻导频污染对信道估计的影响。In a multi-cell large-scale multiple-input multiple-output（MIMO） system, pilot pollution exists in the channel estimation for multiplexing the same pilot sequences in each cell. This paper then proposes a Maximum Likelihood（ML） channel estimation algorithm of joint strongest user conflict resolution in a massive MIMO system. This method combines the strongest user conflict resolution（SUCR） algorithm and the maximum-likelihood channel estimation algorithm of cyclic prefix. It first uses the strongest user conflict resolution solution in the cell to mitigate the interference between users competing for the same pilot, and then reduces the content of competing pilots. The number of users increases the number of successful access users in the system, and then it uses the maximum likelihood channel estimation algorithm of the cyclic prefix to perform channel estimation on the channels of the processed pilot sequences to obtain more accurate channel state information. The simulation results show that the proposed algorithm,compared to the traditional ML algorithm and the MMSE algorithm, can obtain better normalized mean square error and system energy efficiency and can significantly reduce the impact of pilot pollution on channel estimation.

关 键 词：大规模MIMO 信道估计 和速率 信道状态信息 

分 类 号：TN911[电子电信—通信与信息系统]