时变信道下多小区多用户分布式大规模MIMO系统上行可达速率分析

Analysis of Uplink Achievable Rate in Multi-Cell Multi-User Distributed Massive MIMO System with Time-varying Channels

  • 摘要: 针对多小区多用户分布式大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)上行系统,考虑移动环境下信道时变特性,并结合多小区导频污染和信道估计误差条件,分析这类因素对系统可达速率的性能影响。采用一阶高斯马尔科夫过程对时变信道进行建模,以时间相关性系数为时变信道参量描述信道系数随时间变化的快慢程度。当基站采用最大比合并(Maximum Ratio Combining,MRC)接收机时,利用Jensen不等式、随机矩阵理论和Gamma随机变量的各阶矩,推导得出了包含导频污染、信道估计误差和信道时变参量的可达速率解析表达式。基于此,分析得出在多小区分布式大规模MIMO系统中,时变信道参量只会影响系统的可达速率绝对值,而不会影响发射功率缩放律。更重要的是,当不考虑发射功率缩放时,随总天线数增加,可达速率将不受时变信道的影响,而只由导频污染所决定,这表明该系统对时变信道具有良好的鲁棒性。最后,利用蒙特卡洛数值仿真验证了所得出的结论的正确性和有效性。

     

    Abstract: By jointly considering the time-varying characteristic in mobile channel scenario, the pilot contamination phenomenon, and the channel estimation error, we analyze the impacts of the involved three factors for the achievable rate performance in the multi-cell multi-user distributed massive multiple-input multiple-output (MIMO) uplink system. We employ the Gauss-Markov process to model the time-varying channel, which includes the temporal correlation coefficient as an important parameter to describe how the channel coefficient varies as the time flows. Using Jensen’s inequality, the random matrix theory, and the moments of random variable with Gamma distribution, we derive the analytical expression of the uplink achievable rate including pilot contamination, channel estimation error, and the temporal correlation coefficient, when the maximum ratio combining (MRC) receiver is utilized at the base station. Based on this, we conclude that the temporal correlation coefficient only influences the absolute value of the achievable rate without affecting the transmit power scaling law. What’s more, without considering the power scaling, the achievable rate is only determined by the pilot contamination as the total number of the antennas becomes large, while the effect of the time-varying channel asymptotically vanishes, which indicates that the involved system is robust to the time-varying channel. Finally, Monte Carlo simulations validate the correctness and effectiveness of the deduced conclusions.

     

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