蓝绿激光气-海跨介质通信信道特征动态学习传输方案OA

Air-sea cross-medium communication is a crucial technology for enabling high-speed air-sea cross-domain interconnection.Owing to its excellent oceanic penetration characteristics,the blue-green light band(450～550 nm)has emerged as an ideal transmission window for underwater wireless optical communication links,providing vital support for high-speed cross-medium communication.However,subjected to the nonlinear coupling effects of the atmosphere,random sea surface waves,and seawater scattering and absorption,the cross-medium channel exhibits significant non-stationarity and strong time-varying characteristics.In such rapidly time-varying channel environments,traditional fixed coding and modulation strategies,which rely on quasi-static channel assumptions,suffer from degraded system error performance due to mismatches with the actual channel conditions.To address this issue,this paper proposes a transmission scheme based on dynamic channel feature learning for air-sea cross-medium communication.By constructing a closed-loop feedback mecha-nism of"periodic probing—dynamic learning—reconstruction",the coding structure can be dynamically optimized in re-sponse to varying sea states.Specifically,the transmitter periodically transmits pre-designed pilot sequences.The receiver utilizes these pilot signals for channel estimation,compiles real-time statistics on the instantaneous characteristics of the cur-rent channel,and subsequently generates a polar code construction scheme that optimally matches the prevailing sea condi-tions.This construction information is then fed back to the transmitter via a feedback link.Based on this information,the transmitter executes code reconstruction and applies adaptive polar coding to the information sequence to be transmitted.This closed-loop feedback process operates periodically,ensuring that the code reconstruction closely tracks the non-station-ary variations of the cross-medium time-varying channel,thereby overcoming the mismatch between fixed coding strategies and the actual physical channel.To verify the performance of the proposed system,a cross-medium communication channel model encompassing the atmosphere,the air-sea interface,and the seawater medium was established.Three typical water types—Jerlov ⅠB,Jerlov Ⅱ,and Jerlov Ⅲ—were selected to simulate oceanic environments with weak to strong absorption and scattering effects,respectively.Simulation results demonstrate that the proposed dynamic channel feature learning strat-egy can effectively adapt to variations in the seawater medium.Compared to uncoded systems,applying polar coding under the same Jerlov water type yields a significant coding gain,with the bit error rate(BER)markedly reduced.Furthermore,compared to the fixed polar code construction strategy adopted in the 3GPP-5G standard,the proposed dynamic channel fea-ture learning strategy achieves substantial performance improvements across all three Jerlov water conditions.Notably,the performance gain increases as water turbidity rises and channel conditions deteriorate.These results validate that adaptive coded transmission based on dynamic channel feature learning can perceive and adapt to the dynamic changes of the seawa-ter medium,providing a solid theoretical foundation and technical support for the development of highly reliable air-sea cross-medium adaptive communication systems.