摘要
随着用户对通信速率的要求日益增长,散射通信的通信容量亟待提升。大规模多输入多输出(MIMO)技术是提升容量的一种重要途径,本文研究基于大规模MIMO的对流层散射通信系统的信道估计问题。首先建立基于二维均匀方形天线阵列的大规模MIMO对流层散射信道模型,其次提出一种信道协方差矩阵估计算法对传统最小均方差(MMSE)信道估计算法进行改进,最后与最小二乘(LS)、传统MMSE算法和理想MMSE信道估计算法的准确度进行对比。仿真结果表明:在信噪比(SNR)为0~25 dB的情况下,传统的MMSE算法的准确度相较于LS算法的提升效果并不明显,与理想MMSE算法的准确度有一定差距;但改进MMSE信道估计算法的准确性优于传统MMSE算法,同等条件下NMSE相同时,其SNR可提升3~5 dB,并随着SNR的增大逐渐逼近理想MMSE算法。
With the increasing demand of users for communication speed, the communication capacity of tropospheric scattering communication needs to be improved. Massive multiple input multiple output(MIMO) technology is an important way to improve capacity. This paper studies the channel estimation problem of troposcatter communication system based on massive MIMO. Firstly, a massive MIMO troposcatter channel model based on two-dimensional uniform rectangular array is established. Secondly, a channel covariance matrix estimation algorithm is proposed to improve the traditional minimum mean square(MMSE) channel estimation algorithm. Finally, the accuracy of channel estimation algorithm is compared with that of least square(LS), traditional MMSE and ideal MMSE. The simulation results show that when the SNR is 0~25 dB, the accuracy of the traditional MMSE algorithm is not significantly improved compared with that of LS algorithm, and there is a certain gap between the accuracy of the ideal MMSE algorithm and that of the traditional MMSE algorithm. However, the accuracy of the improved MMSE channel estimation algorithm is better than that of the traditional MMSE algorithm. Under the same conditions, when the NMSE is the same, the SNR of the improved MMSE algorithm can be improved by 3~5 dB, and gradually approaches the ideal MMSE algorithm with the increase of SNR.
作者
史清林
刘丽哲
李行健
SHI Qing-lin;LIU Li-zhe;LI Xing-jian(The 54th Research Institute of China Electronics Technology Group Corporation,Shijiazhuang 050081,China)
出处
《计算机与现代化》
2022年第12期18-25,共8页
Computer and Modernization
基金
通信网信息传输与分发技术重点实验室基金资助项目(6142104210212)。
关键词
大规模MIMO
对流层散射
信道建模
信道估计
massive MIMO
tropospheric scattering
channel modeling
channel estimation
