Abstract
We consider a signal detection problem by using deep learning techniques in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel. There exist some suboptimal detectors such as the near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector in the channel. However, the NML detectors require an exponentially increasing complexity as the number of transmit antennas increases. More seriously, without the channel state information (CSI) of the source-relay (SR) link, there is no detector that can achieve good performance even at high complexity. In this paper, we propose a deep learning approach to the NML (DL-NML) detector that achieves good performance with low complexity regardless of whether the CSI of the SR link is known or not at the destination. The DL-NML detector can detect signals in changing channels after a single training by using randomly generated channels. Furthermore, we propose a linear detector and a semidefinite relaxation approach to the NML detector to compare with the DL-NML detector in performance and complexity. The complexity analysis and simulation results validate the superiority of the proposed DL-NML detector.
Highlights
In wireless communications, deep fading often causes a failure in reliable data transmission
The deep learning approach to the NML (DL-NML) detector is proposed by applying the deep unfolding approach under various conditions of the knowledge of the SR channel when the maximum likelihood (ML) detection is applied at the relay
DETECTORS AT THE RELAY In previous sections, we proposed several detectors at the destination (DetD) when the ML detector is applied at the relay
Summary
Deep fading often causes a failure in reliable data transmission. Referring to the ZF detector, a linear detector of ZF with maximum ratio combining (MRC) (ZFwMRC) was proposed in the MIMO DF relay channel when the relay detects signals correctly [9]. This algorithm cannot achieve good performance for the relay with errors similar to the MD detection. The detection and channel decoding problems have been investigated using powerful deep learning tools in the channel decoding related to belief propagation [16], [17], signal detection in MIMO systems [18], [19], and signal detection in chemical communications [20], [21]. Embedding the existing mathematical methods into black-box-like deep neural networks improves the accuracy and reduces complexity
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