Design of Intelligent Interpretation Network for Underwater Acoustic Communication Receiver with Multiple Signal Modulations - Lanjun LIU; Zining CHENG; Jialin CHEN; Ming LI; Honghao LIU (水下无人系统学报, 2025)
An intelligent interpretation network design scheme for underwater acoustic communication receivers with multiple signal modulations was proposed to meet the requirements of channel adaptive underwater acoustic high-quality communication in complex application scenarios. It supported four signal modulation methods including orthogonal frequency division multiplexing(OFDM), single carrier modulation(SCM), multi carrier frequency domain spread spectrum(MC-FDSS), and single carrier with time domain spread spectrum(SC-TDSS). Intelligent interpretation modules based on fully-connected deep neural network(FC-DNN) and long short-term memory(LSTM) networks were used to replace traditional channel estimation and channel equalization modules. A deep learning network structure that facilitated parallel expansion was designed for non-spread spectrum and spread spectrum signal modulation methods. Network training and testing were conducted based on five typical time-varying channel models. The test results show that the two designed intelligent interpretation networks have significantly improved system performance compared to traditional least squares(LS) estimation + zero-forcing(ZF) equalization and LS estimation + minimum mean squared error(MMSE) channel estimation equalization methods. Under a signal-to-noise ratio of 5 dB, the system error rates of OFDM and SCM non-spread spectrum signal modulation methods are reduced by about 10 times and 100 times, respectively. Under a signal-to-noise ratio of −5 dB, the system error rates of MC-FDSS and SC-TDSS spread spectrum signal modulation methods are reduced by about 100 times and 1 000 times, respectively. The system performance of the two designed intelligent interpretation networks is comparable, and they both have good generalization performance. The computational complexity of the intelligent interpretation network based on FC-DNN is relatively low.