Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection
arxiv(2024)
摘要
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.
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