We study downlink (DL) channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in a time-division duplex. The users must know their effective channel gains to decode their received DL data signals. A common approach is to use the mean value as the estimate, motivated by channel hardening, but this is associated with a substantial performance loss in non-isotropic scattering environments. We propose two novel estimation methods. The first method is model-aided and utilizes asymptotic arguments to identify a connection between the effective channel gain and the average received power during a coherence block. The second one is a deep-learning-based approach that uses a neural network to identify a mapping between the available information and the effective channel gain. We compare the proposed methods against other benchmarks in terms of normalized mean-squared error and spectral efficiency (SE). The proposed methods provide substantial improvements, with the learning-based solution being the best of the considered estimators.
翻译:我们在一个在时间差异下方运行的多细胞多投入多输出(MIMO)系统中研究下链接(DL)频道估计。用户必须知道他们的有效渠道收益,以解码他们收到的DL数据信号。一个共同的方法是使用中值作为估计数,由频道硬化驱动,但这与非地球散布环境中的显著性能损失有关。我们建议两种新的估计方法。第一个方法是模型辅助的,并使用无药可治的参数来确定有效渠道收益与统一区块中平均获得的功率之间的联系。第二个方法是基于深层次的学习方法,利用神经网络确定现有信息与有效渠道收益之间的绘图。我们比较了拟议方法与其他基准在非地球分布环境中的正常平均差错和光谱效率(SE)方面进行的其他基准。拟议方法提供了重大改进,而基于学习的解决方案是被考虑的估算者的最佳方法。