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 re...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.展开更多
为了保证水下设备的长期稳定通信,提出了一种基于改进的快速边缘似然最大化的稀疏贝叶斯学习(sparse Bayesian learning based on improved fast marginal likelihood maximization, IFM-SBL)算法,对水声信道进行低复杂度、高性能的估...为了保证水下设备的长期稳定通信,提出了一种基于改进的快速边缘似然最大化的稀疏贝叶斯学习(sparse Bayesian learning based on improved fast marginal likelihood maximization, IFM-SBL)算法,对水声信道进行低复杂度、高性能的估计。特别是在低信噪比情况下,通过阈值去噪和离散傅里叶变换降噪,可以进一步提升算法的性能。仿真和海试结果表明,所提的IFM-SBL信道估计后的输出误码率与基于期望最大化的稀疏贝叶斯学习(sparse Bayesian learning based on expectation maximization, EM-SBL)算法相似,且验证了算法在低信噪比和快慢时变信道中都具有良好的鲁棒性。在运行速度方面,FM-SBL算法与IFM-SBL算法比EM-SBL算法提高了约90%,大大减少了信道估计时间。展开更多
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.
文摘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.
文摘为了保证水下设备的长期稳定通信,提出了一种基于改进的快速边缘似然最大化的稀疏贝叶斯学习(sparse Bayesian learning based on improved fast marginal likelihood maximization, IFM-SBL)算法,对水声信道进行低复杂度、高性能的估计。特别是在低信噪比情况下,通过阈值去噪和离散傅里叶变换降噪,可以进一步提升算法的性能。仿真和海试结果表明,所提的IFM-SBL信道估计后的输出误码率与基于期望最大化的稀疏贝叶斯学习(sparse Bayesian learning based on expectation maximization, EM-SBL)算法相似,且验证了算法在低信噪比和快慢时变信道中都具有良好的鲁棒性。在运行速度方面,FM-SBL算法与IFM-SBL算法比EM-SBL算法提高了约90%,大大减少了信道估计时间。
