Deep Learning Detection Networks in MIMO Decode-Forward Relay Channels
Xianglan Jin, Hyoung-Nam Kim

TL;DR
This paper introduces deep learning-based detection networks for MIMO decode-forward relay channels that achieve near-optimal performance with significantly reduced complexity, adaptable to changing channel conditions after a single training.
Contribution
The paper proposes NML-based detection networks using deep learning for MIMO relay channels, reducing complexity and enabling detection in dynamic channels with minimal training.
Findings
NMLDNs outperform traditional detectors in complexity and performance.
Detection networks adapt to changing channels after a single training.
Simulation results validate the effectiveness of the proposed methods.
Abstract
In this paper, we consider signal detection algorithms in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel with one source, one relay, and one destination. The existing suboptimal near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector achieve excellent performance with instantaneous channel state information (CSI) of the source-relay (SR) link and with statistical CSI of the SR link, respectively. However, the NML detectors require an exponentially increasing complexity as the number of transmit antennas increases. Using deep learning algorithms, NML-based detection networks (NMLDNs) are proposed with and without the CSI of the SR link at the destination. The NMLDNs detect signals in changing channels after a single training using a large number of randomly distributed channels. The detection networks…
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Taxonomy
TopicsCooperative Communication and Network Coding · Wireless Communication Security Techniques · Advanced Wireless Communication Technologies
