Abstract

The latest research for applying deep learning in wireless communications gives several opportunities to reduce complex signal processing. The channel estimation is important to study the nature of the varying channel and to calculate channel state information (CSI) value which is utilized at the receiver to nullify the interference which occurs during multipath transmission. In the current article, considering the massive Multiple Input Multiple Output (MIMO) channel model, a DL approach is developed with a fully connected neural network (NN) architecture which is used to estimate the channel with minimum error. The proposed DL architecture uses an openly available channel dataset. Further, using generated pilot symbols of lengths 2 and 4, the performance of DL-based Fully connected NN (DL-FCNN) is analyzed to estimate the channel in uplink massive MIMO communication. The obtained results demonstrate that the channel estimation performance was calculated in terms of normalized mean square error((NMSE) for different values of SNR added at receiver base station (BS) to the signals over the range of BS antennas. Also, the channel estimation error over a large number of BS antennas for massive MIMO scenarios is observed, and it is observed that the NMSE reduces with a greater number of antennas. Hence, it can be inferred that the DL models will be the future for most physical layer signal processing techniques such as channel estimation, modulation detection, etc. within massive MIMO networks.

摘要

在无线通信中应用深度学习的最新研究为减少复杂信号处理提供了多种机会。信道估计对于研究变化信道的性质以及计算信道状态信息（CSI）值非常重要，该值在接收器处用于消除多径传输期间发生的干扰。在本文中，考虑到大规模多输入多输出 (MIMO) 信道模型，开发了一种采用全连接神经网络 (NN) 架构的深度学习方法，用于以最小误差估计信道。所提出的深度学习架构使用公开可用的通道数据集。进一步地，使用生成的长度为2和4的导频符号，分析了基于深度学习的全连接神经网络（DL-FCNN）的性能，以估计上行链路大规模 MIMO 通信中的信道。获得的结果表明，对于在接收基站 (BS) 处添加到 BS 天线范围内的信号的不同 SNR 值，信道估计性能是根据归一化均方误差 ((NMSE) 计算的。此外，信道估计观察到大规模 MIMO 场景中大量 BS 天线的误差，并且观察到 NMSE 随着天线数量的增加而降低。因此，可以推断 DL 模型将成为大多数物理层信号处理的未来大规模 MIMO 网络中的信道估计、调制检测等技术。观察到大规模 MIMO 场景中大量 BS 天线上的信道估计误差，并且观察到 NMSE 随着天线数量的增加而降低。因此，可以推断，DL 模型将成为大规模 MIMO 网络中大多数物理层信号处理技术（例如信道估计、调制检测等）的未来。观察到大规模 MIMO 场景中大量 BS 天线上的信道估计误差，并且观察到 NMSE 随着天线数量的增加而降低。因此，可以推断，DL 模型将成为大规模 MIMO 网络中大多数物理层信号处理技术（例如信道估计、调制检测等）的未来。