Two-step Machine Learning Approach for Channel Estimation with Mixed Resolution RF Chains

TL;DR

This paper presents a two-step machine learning-based channel estimation method for massive MIMO systems with mixed-resolution RF chains, improving efficiency and performance in 5G networks.

Contribution

It introduces a novel combination of cGAN and LSTM neural networks for uplink channel estimation in constrained RF hardware, outperforming traditional methods.

Findings

cGAN accurately predicts channels from limited RF data

LSTM extracts phase information effectively

Method is competitive with Unitary tensor-ESPRIT in complex scenarios

Abstract

Massive MIMO is one of the main features of 5G mobile radio systems. However, it often leads to high cost, size and power consumption. To overcome these issues, the use of constrained radio frequency (RF) frontends has been proposed, as well as novel precoders, e.g., a multi-antenna, greedy, iterative and quantized precoding algorithm (MAGIQ). Nevertheless, the best performance of MAGIQ assumes accurate channel knowledge per antenna element, for example, from uplink sounding reference signals. In this context, we propose an efficient uplink channel estimator by applying machine learning (ML) algorithms. In a first step a conditional generative adversarial network (cGAN) predicts the radio channels from a limited set of full resolution RF chains to the rest of the low resolution RF chain antenna elements. A long-short term memory (LSTM) neural network extracts further phase information from the low resolution RF chain antenna elements. Our results indicate that our proposed approach is competitive with traditional Unitary tensor-ESPRIT in scenarios with various closely spaced multipath components (MPCs).