Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection

TL;DR

This paper introduces a unified AI-based receiver architecture that jointly optimizes channel estimation and MIMO detection using neural networks, achieving near-optimal performance with end-to-end training.

Contribution

It proposes a novel integrated AI receiver combining CNN-based channel estimation and GNN-based detection, optimized via end-to-end training for improved MIMO system performance.

Findings

QRMNet achieves BLER close to baseline detectors.

End-to-end training simplifies receiver design.

Unified architecture enhances detection robustness.

Abstract

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial intelligence (AI) for receiver design. However, problem remains on how to unify the CE and MIMODet by optimizing AI's structure to achieve near optimal detection performance such as widely considered QR with M-algorithm (QRM) that can perform close to the maximum likelihood (ML) detector. In this paper, we propose an AI receiver that connects CE and MIMODet as an unified architecture. As a merit, CE and MIMODet only adopt structural input features and conventional neural networks (NN) to perform end-to-end (E2E) training offline. Numerical results show that, by adopting a simple super-resolution based convolutional neural network (SRCNN) as channel estimator and domain knowledge enhanced graphical neural network (GNN) as detector, the proposed QRM enhanced GNN receiver (QRMNet) achieves comparable block error rate (BLER) performance to near-optimal baseline detectors.