Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System

TLDR

Massive MIMO promises higher network capacity, yet its high computational complexity and complex spatial structures hinder effective channel exploitation. This work proposes a deep‑learning framework that jointly performs channel and DOA estimation for massive MIMO systems. A deep neural network is trained offline on simulated data and then applied online to learn channel statistics and spatial structures, enabling super‑resolution channel and DOA estimation directly in the angle domain without extra complexity. Simulations demonstrate that the proposed scheme outperforms conventional methods in both channel and DOA estimation and remains robust across diverse scenarios.

Abstract

The recent concept of massive multiple-input multiple-output (MIMO) can significantly improve the capacity of the communication network, and it has been regarded as a promising technology for the next-generation wireless communications. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring great difficulties to exploit the characteristics of the channel and sparsity of these multi-antennas systems. To address this problem, in this paper, we focus on channel estimation and direction-of-arrival (DOA) estimation, and a novel framework that integrates the massive MIMO into deep learning is proposed. To realize end-to-end performance, a deep neural network (DNN) is employed to conduct offline learning and online learning procedures, which is effective to learn the statistics of the wireless channel and the spatial structures in the angle domain. Concretely, the DNN is first trained by simulated data in different channel conditions with the aids of the offline learning, and then corresponding output data can be obtained based on current input data during online learning process. In order to realize super-resolution channel estimation and DOA estimation, two algorithms based on the deep learning are developed, in which the DOA can be estimated in the angle domain without additional complexity directly. Furthermore, simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.