摘要
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
出处
《哈尔滨工程大学学报(英文版)》
2026年第1期292-299,共8页
Journal of Marine Science and Application
基金
funding from the National Key Research and Development Program of China(No.2018YFE0110000)
the National Natural Science Foundation of China(No.11274259,No.11574258)
the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
