Parametric Sparse Channel Estimation Using Long Short-Term Memory for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) communications will play an important role in 5G and 6G communication systems as a means to support extremely high data rates. One main bottleneck of the mmWave communication is the severe signal attenuation caused by the foliage loss, atmospheric absorption, body and hand losses in the mmWave band. To compensate for the severe path loss, multiple-input-multiple-output (MIMO) antenna array-based beamforming has been widely used. Since the beams should be aligned with the signal propagation paths to get the most of beamforming gain, acquisition of accurate channel knowledge, i.e., channel estimation, is the key to the success of mmWave MIMO systems. In this paper, we propose a new type of deep learning (DL)-based parametric channel estimation technique. In our work, DL figures out the direct mapping between the received pilot signal and the sparse channel parameters characterizing the angular domain channel. By exploiting the long short-term memory (LSTM) as a main deep neural network (DNN) engine, we extract the temporally correlated features of time-varying channel parameters and make a fast yet accurate estimation with relatively small pilot overhead. From the numerical experiments, we show that the proposed scheme is effective in estimating the mmWave MIMO channel in various mmWave downlink environments.