Research On Underwater Acoustic Communication Technology Based On Unitary Space Time Modulation

Underwater acoustic channel is a time-space-frequency variable channel with severe multipath effect and significant Doppler frequency shift,which greatly limits the performance of the underwater acoustic communication system.Space-time coding for Multiple-Input Multiple-Output(MIMO)systems can effectively improve the performance of underwater acoustic communication.By processing the signal in the time and space domains,using spatial resources to provide redundancy to the signal or generate multiplexing,which can improve the anti-fading capability and data transmission efficiency of the system without increasing the transmit power and expanding the bandwidth.However,the excellent performance of traditional spacetime technology requires accurate Channel State Information(CSI),which is prone to the problems of wasted communication resources and unreliability in the hydroacoustic channel with fast fading and other characteristics.As a unique kind of space-time code,Unitary Space-Time Code(USTM),also known as unitary space-time modulation.Its ability to achieve reliable communication without relying on CSI,decoding only requires the stored unitary space-time constellation and the received signal matrix.This paper studies the use of unitary space-time modulation in underwater acoustic communication,and then studies the joint LDPC code and unitary space-time modulation technology,using joint coding modulation at the transmitting end and iterative decoding at the receiving end,and finally study the unitary space-time demodulation technique based on deep learning.The work of this paper is as follows:(1)Time-varying underwater acoustic channel model based on BELLHOP.According to the propagation characteristics of the underwater acoustic channel,focus on the Doppler effect and time variation.A time-varying underwater acoustic channel model based on the BELLHOP model is designed to reflect the time-varying characteristics of the underwater acoustic channel while taking into account the accuracy of the model.In order to verify the validity of the time-varying model,makes the simulation,and the theoretical analysis of various parameters in the simulation was also carried out to prove the validity of the model.(2)Research on underwater acoustic communication method based on USTM.In underwater acoustic communication,fast time-varying,multipath fading,and limited bandwidth channels make it difficult for traditional space-time signals to accurately obtain CSI,and sending training sequences to obtain CSI will occupy channel bandwidth.This paper proposes an underwater acoustic communication method based on USTM.The method does not depend on CSI,and can effectively utilize channel multipath,improve spectrum efficiency and power efficiency,reduce system overhead and information obsolescence due to channel estimation.In addition,an analytical proof is given by means of an equation derivation for the excellent performance of the unitary space-time modulation under high signal-to-noise ratio conditions.(3)Research on joint iterative W-LDPC-USTM method.Aiming at the problem of high bit error rate under low signal-to-noise ratio of the underwater acoustic communication system using USTM,combined with efficient channel coding technology,we designed a joint iterative decoding method W-LDPC-USTM suitable for underwater acoustic communication on the basis of(2),which introduces feedback between the LDPC decoder and USTM demapper.Simulations show that the W-LDPCUSTM method can operate in a heavily interfered underwater acoustic channel,providing a coding gain of about 17 d B compared to an uncoded USTM system and a performance gain of about 3 d B compared to a simple LDPC-USTM cascade method.(4)Research on unitary space-time signal demodulation method based on deep learning.In underwater acoustic communication,using maximum likelihood to demodulate unitary space-time signals has high complexity and poor adaptability to underwater acoustic channel.Based on the technical background of deep learning,considering the space-time characteristics of unitary space-time signal,this paper proposes a unitary space-time modulation signal demodulation method CNN-GRU based on deep learning by combining convolutional neural network and improved gated recurrent unit.Simulation experiments show that this method is more suitable for underwater acoustic channels and reduces the bit error rate of unitary space-time systems.