Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems

TL;DR

This paper introduces a deep neural network approach to calibrate UL/DL channels in massive MIMO systems, effectively handling nonlinear hardware effects and limited training data, achieving performance comparable to traditional linear methods.

Contribution

The paper presents a novel deep learning-based calibration method that models nonlinear UL/DL channel relationships in massive MIMO systems, outperforming traditional linear assumptions.

Findings

Achieves calibration performance comparable to conventional linear methods.

Demonstrates robustness under nonlinear hardware conditions.

Maintains effectiveness with limited training data.

Abstract

One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.