Generative-Adversarial-Network Enabled Signal Detection for Communication Systems With Unknown Channel Models

• Published in: IEEE journal on selected areas in communications Vol. 39; no. 1; pp. 47 - 60
• Main Authors: Sun, Li, Wang, Yuwei, Swindlehurst, A. Lee, Tang, Xiao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Channel estimation; Channel models; Communication; Communications systems; deep learning; Detectors; generative adversarial network; Generative adversarial networks; Machine learning; Mathematical models; Optical communication; Receivers; Signal detection; Transition probabilities; Underwater communication; Viterbi algorithm; Viterbi algorithm detectors
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2020.3036954

The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater optical communications, etc, where the underlying channel models are highly complex or completely unknown. ViterbiNet, developed in the prior literature, is a promising framework to cope with this challenge, where deep learning (DL) techniques are combined with the Viterbi Algorithm to enable near-optimal signal detection without CSI. This paper offers a non-trivial variation of ViterbiNet based on generative adversarial networks (GAN). Specifically, a novel architecture using GAN is designed to directly learn the channel transition probability (CTP) from receiver observations, which is the only part of the Viterbi algorithm that is channel-dependent. With the learned CTP, the classical Viterbi algorithm can be implemented without modifications. To make the proposed architecture applicable to time-varying channels, we further develop two methods to fine-tune the learned CTP online. In the first method, pilots within each frame are exploited to update the CTP learning network; In the second method, a decision-directed approach is devised to generate training data in real-time, which is utilized to re-train the learning network. By combining these two approaches, the receiver is able to track the dynamic channel conditions without being trained from scratch. Numerical simulations demonstrate the superiority of the proposed design compared to existing methods.