mpNet: variable depth unfolded neural network for massive MIMO channel estimation

TL;DR

mpNet is an adaptive, unsupervised unfolded neural network designed for efficient massive MIMO channel estimation, capable of online training and automatic adjustment to varying SNR conditions, improving accuracy and resilience.

Contribution

The paper introduces mpNet, a novel unfolded neural network that adapts in real-time for massive MIMO channel estimation without requiring offline training.

Findings

Achieves near-perfect channel estimation accuracy in realistic millimeter wave channels

Automatically adapts its depth based on SNR levels

Enables incident detection and environment adaptation

Abstract

Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes challenging for a large number of antennas. Using a physical model allows to ease the problem by injecting a priori information based on the physics of propagation. However, such a model rests on simplifying assumptions and requires to know precisely the configuration of the system, which is unrealistic in practice.In this paper we present mpNet, an unfolded neural network specifically designed for massive MIMO channel estimation. It is trained online in an unsupervised way. Moreover, mpNet is computationally efficient and automatically adapts its depth to the signal-to-noise ratio (SNR). The method we propose adds flexibility to physical channel models by allowing a base station (BS) to automatically correct its channel estimation algorithm based on incoming data, without the need for a separate offline training phase.It is applied to realistic millimeter wave channels and shows great performance, achieving a channel estimation error almost as low as one would get with a perfectly calibrated system. It also allows incident detection and automatic correction, making the BS resilient and able to automatically adapt to changes in its environment.