郑一,王承祥,冯瑞,等. 6G超大规模MIMO信道测量与容量优化评估[J]. 电波科学学报,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2024026
      引用本文: 郑一,王承祥,冯瑞,等. 6G超大规模MIMO信道测量与容量优化评估[J]. 电波科学学报,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2024026
      ZHENG Y, WANG C - X, FENG R, et al. Channel measurements and capacity optimization evaluation for 6G ultra-massive MIMO[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2024026
      Citation: ZHENG Y, WANG C - X, FENG R, et al. Channel measurements and capacity optimization evaluation for 6G ultra-massive MIMO[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2024026

      6G超大规模MIMO信道测量与容量优化评估

      Channel measurements and capacity optimization evaluation for 6G ultra-massive MIMO

      • 摘要: 为了研究6G超大规模多输入多输出(multiple-input multiple-output, MIMO)系统性能,本文进行了5.3 GHz频段的单用户和多用户超大规模MIMO信道测量实验,分析了超大规模MIMO单用户和多用户信道容量。在发送端配置单用户和多用户天线,接收端配置128×8的超大规模MIMO天线阵列,进行城市环境上行链路的信道测量实验。通过系统校准和测量数据处理,得到真实的信道冲激响应。根据上下行链路互易性,通过上行链路的信道矩阵得到下行链路的信道矩阵。采用基于均方误差的最小化方法将信道容量优化问题转化为凸优化问题,通过优化预编码矩阵得到下行链路最大的信道容量。通过分析下行链路的单用户信道容量,发现视距环境的信道容量小于非视距环境下的信道容量;通过分析下行链路的多用户信道容量,发现当用户数从单用户增加到4用户时,信道容量也随之增加;此外,根据测量数据指导优化算法设计预编码矩阵,可以带来信道容量的提升。

         

        Abstract: The single-user and multi-user ultra-massive multiple-input multiple-output (MIMO) channel measurements at 5.3 GHz band and channel capacity analysis are carried out in this paper. The purpose of the paper is to study the system performance of sixth-generation (6G) ultra-massive MIMO. The transmitter is equipped with single-user or multi-user antennas, the receiver is equipped with the 128×8 ultra-massive MIMO antenna array for channel measurements in urban environments for the uplink. The channel impulse response is obtained by the system calibration and measurement data processing. According to the reciprocity for the uplink and downlink, the channel matrix for the downlink is obtained from the channel matrix for the uplink. Based on mean square error minimization method, the channel capacity optimization problem is transformed into a convex optimization problem. The maximum channel capacity for the downlink can be obtained by optimizing the precoding matrix. The single-user channel capacity in line-of-sight (LoS) environments is smaller than that in non-line-of-sight (NLoS) environments of the downlink. When the number of users increases from single user to 4 users, the multi-user channel capacity of the downlink will increase with the increase of users. In addition, the channel capacity can be improved by using optimization algorithm to design the precoding matrix by the measurement data.

         

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