ViterbiNet-Based Signal Detection for OTFS System

• Published in: IEEE communications letters Vol. 27; no. 3; pp. 881 - 885
• Main Authors: Gong, Yi, Li, Qingyu, Meng, Fanke, Li, Xinru, Xu, Zhan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Detectors; Long short term memory; model-driven; Neural networks; OTFS; Signal detection; Symbols; Training; Viterbi algorithm; Viterbi algorithm detectors; ViterbiNet; Wireless communication; Wireless communication systems
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2023.3237719

Recently, orthogonal time frequency space (OTFS) was presented to combat high Doppler shifts in wireless communication systems. Most studies on OTFS signal detection require channel state information (CSI). However, it is quite difficult to describe the channel model mathematically for some communication systems. This letter proposes a low-complexity ViterbiNet-based OTFS signal detection algorithm. A neural network (NN) is used in the ViterbiNet to replace the log-likelihood calculation that requires CSI in the Viterbi algorithm. Therefore, the proposed ViterbiNet-based scheme can perform signal detection without CSI in OTFS systems. Meanwhile, since it is a model-driven network, the proposed ViterbiNet-based scheme requires only a small size NN and a small amount of training data to achieve great performance. Moreover, the softplus function is utilized as the activation function, which smoothen the training of the ViterbiNet. Through experiments, simulation results prove the performance of the presented ViterbiNet-based OTFS signal detection algorithm.