Adaptive Channel Estimation Based on Multidirectional Structure in Delay-Doppler Domain for Underwater Acoustic OTFS System

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 17; p. 3157
• Main Authors: Shi, Wentao, Jin, Mingqi, Lianyou Jing, Tu, Nan, He, Chengbing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Acoustics; Adaptation; Adaptive algorithms; adaptive channel estimation; Adaptive systems; Algorithms; Channels; Deep learning; denoising strategy; Doppler effect; Fourier transforms; improved proportionate normalized least mean squares (IPNLMS); Invariants; Least mean squares; Least mean squares algorithm; Methods; moving average (MA) filter; Noise reduction; orthogonal time frequency space (OTFS); Pilots; Sparsity; Underwater; underwater acoustic communications; Underwater acoustics; Underwater communication; Underwater structures
• ISSN: 2072-4292
• DOI: 10.3390/rs16173157

Time-varying underwater acoustic (UWA) channels are the key challenge of underwater acoustic communication (UAC). Although UAC exhibits time-variance characteristics significantly in time domains, its delay-Doppler (DD) domain representation tends to be time-invariant. Orthogonal time–frequency space (OTFS) modulation has recently been proposed and has acquired widespread interest due to its excellent performance over time-varying channels. In the UWA OTFS system, the novel DD domain channel estimation algorithm that employs a multidirectional adaptive moving average scheme is proposed. Specifically, the proposed scheme is cascaded by a channel estimator and moving average filter. The channel estimator can be employed to estimate the time-invariant channel of the DD domain multidirectionally, improving proportionate normalized least mean squares (IPNLMS). Meanwhile, the moving average filter is used to reduce the output noise of the IPNLMS. The performance of the proposed method is verified by simulation experiments and real-world lake experiments. The results demonstrate that the proposed channel estimation method can outperform those of benchmark algorithms.