Deep Learning-Based Channel Estimation for Massive MIMO Systems

In this letter, we propose a deep learning (DL)-based channel estimation scheme for the massive multiple-input multiple-output (MIMO) system. Unlike existing studies, we develop the channel estimation scheme for the case that the pilot length is smaller than the number of transmit antennas. The proposed scheme takes a two-stage estimation process: 1) a DL-based pilot-aided channel estimation and 2) a DL-based data-aided channel estimation. In the first stage, the pilot itself and the channel estimator are jointly designed by using both a two-layer neural network (TNN) and a deep neural network (DNN). In the second stage, the accuracy of channel estimation is further enhanced by using another DNN in an iterative manner. The simulation results demonstrate that the proposed channel estimation scheme has much better performance than the conventional channel estimation scheme. We also derive a useful insight into the optimal pilot length given the number of transmit antennas.

I. Introduction

Multiple-input multiple-output (MIMO) with large-scale antenna arrays, so-called the massive MIMO, is one of the most promising techniques to increase the data rate and to maintain the high communication reliability for future wireless systems [1], [2]. In the massive MIMO system, a large scale antenna array is deployed typically at the base station (BS) to provide a considerable antenna gain, and this antenna gain highly depends on the channel estimation accuracy. Thus, obtaining accurate channel estimation is very important to ensure such benefits of the massive MIMO technique.