Deep Learning for MIMO Communications

Abstract

Recently, deep learning (DL) is becoming a key feature of next-generation multiple-input multiple-output (MIMO) transceiver design with learning and inference capabilities embedded in the network, which achieves greatly enhanced system performance. Popular topics include end-to-end (E2E) learning for transceiver design, deep reinforcement learning (DRL) for communications, and model-driven deep unfolding techniques. In particular, E2E learning treats the communication system design as an E2E data reconstruction task that seeks to jointly optimize transceiver components, so that encoding and decoding are fostered by the learned weights of deep neural networks (DNN). E2E learning can be employed to solve various problems in MIMO communications, such as channel state information (CSI) feedback, beamforming, signal detection, and channel estimation. Moreover, DRL has been widely applied to solve high-dimensional non-convex optimization problems in designing the transceiver. However, these DNNs generally suffer from an inability to be interpreted or generalized, and they often lack performance guarantees. To overcome such drawbacks, substantial researches have proposed to unfold the iterations of an iterative optimization algorithm into a layer-wise structure analogous to a DNN. Inspired by the great potential of these DL methods, it is important to investigate AI-empowered transceivers for future MIMO systems.