Adaptive Underwater Acoustic OFDM Modulation Method Based on Improved Deep Reinforcement Learning

Abstract

High environmental noise, narrow bandwidth, low carrier frequency, large transmission delay and other factors restrict the data transmission rate in underwater acoustic communication. To this end, improving communication quality and efficiency through adaptive adjustment is necessary for underwater acoustic communication system because of the restrict factor. The proposed method uses Deep Reinforcement Learning (DRL) to select the optimal transportation parameters and maximize the transmission efficiency with. The adaptive communication parameter setting is mapped into a Markov decision process with an unknown state transition probability. The system returns channel information including Channel State Information (CSI) and Signal Noise Ratio (SNR). We validate our estimation and adaptation techniques using the well-known simulator SWellEx_96 in varying underwater environments. Our results show that, for many practical cases, a dynamic adjustment of the prefix length, number of sub-carriers, filter length, and over-sampling factor may enable the utilization of OFDM modulations in underwater communications.