Channel Prediction and Estimation Based on Machine and Deep Learning Techniques: A Review

Keshav Mishra; Rovin Tiwari

Abstract

Channel prediction estimation in the high- speed mobile environment is a hot issue for 3D massive multiple input multiple output (MIMO) millimeter-wave (mmWave) system. In this environment, the channel has fast time-varying and non-stationary characteristics. And its time-domain correlation coefficient is a time- varying parameter, which makes it difficult for traditional channel estimation methods to capture the channel variations over time and achieve ideal channel estimation performance. A known signal, referred to as a pilot signal, is sent from the transmitter to the receiver, which then receives it in order to estimate the channel. The received pilot signal is used by the receiver to determine the channel's properties, including the signal's phase and amplitude. The investigation of the massive multi-input multi- output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Artificial intelligence based machine and deep learning techniques are capable of predicting and estimate the wireless channel more efficiently. This paper review of the different channel estimation techniques based on ML & DL algorithms.

Abstract

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