A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems

Abstract

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the “beam squint” effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the “beam squint” effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

“…Additionally, an approximate message-passing network employing learned denoising techniques has been proposed for the estimation of mmWave communication systems featuring lens antenna arrays, effectively finding and eliminating noise to refine channel estimation [24]. Hybrid precoding vast MIMO systems operating at the millimetre wave do not incorporate channel estimates into these methods [25]. In addition, a beamforming design method based on deep learning develops a neural network-trained beamformer that exhibits optimal spectrum efficacy [26].…”