Soft-Output Joint Channel Estimation and Data Detection using Deep Unfolding

TL;DR

This paper introduces a deep unfolding-based joint channel estimation and data detection algorithm for MU-MIMO systems that outperforms existing methods with fewer iterations by providing soft-output information.

Contribution

It presents a novel deep unfolding approach with a hyper-network for joint channel estimation and data detection in MU-MIMO systems, improving efficiency and accuracy.

Findings

Outperforms state-of-the-art algorithms in MU-MIMO detection.

Achieves better performance with as few as 10 iterations.

Effectively generates soft-output information for coded systems.

Abstract

We propose a novel soft-output joint channel estimation and data detection (JED) algorithm for multiuser (MU) multiple-input multiple-output (MIMO) wireless communication systems. Our algorithm approximately solves a maximum a-posteriori JED optimization problem using deep unfolding and generates soft-output information for the transmitted bits in every iteration. The parameters of the unfolded algorithm are computed by a hyper-network that is trained with a binary cross entropy (BCE) loss. We evaluate the performance of our algorithm in a coded MU-MIMO system with 8 basestation antennas and 4 user equipments and compare it to state-of-the-art algorithms separate channel estimation from soft-output data detection. Our results demonstrate that our JED algorithm outperforms such data detectors with as few as 10 iterations.