Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

Zhao, Hai; Yang, Cui; XU, Yalu; Ji, Fei; Wen, Miaowen; Chen, Yankun

doi:10.1109/tvt.2022.3230143

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Although MMSE equalizer overcomes this problem by considering the signal-to-noise ratio (SNR), it is hard to estimate the statistical values of the noise in real underwater environments including non-Gaussian and colored noise [20,21]. To further optimize the equalizer, deep learning (DL) and neural network (NN) have been developed [22][23][24][25][26]. With enough samples, a DL-based receiver can statistically learn to detect the symbols from the channel and other interference.…”

Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach

Zhou,

Wang,

Feng

et al. 2023

Remote Sensing

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The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.

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