融合多头自注意力的AeroMACS自适应调制编码算法

AeroMACS Adaptive Modulation Coding Algorithm Combining Multi-headed Self-Attention

航空移动机场通信系统(AeroMACS)因传输速率高、安全性好等优点成为机场、空地通信网络的重要组成部分。针对飞机起飞、着陆高速移动阶段的信道快速时变导致信道状态信息(CSI)过时和多普勒频移大等引起通信质量恶化的问题,利用多头自注意力机制,提出了基于Transformer神经网络的信道预测方法。根据实时预测的信噪比(SNR)来调整AeroMACS的WiMAX和5G双模的调制编码方案(MCS)。仿真结果表明,与其他3种人工智能方法相比,所提出的基于Transformer网络的信道预测方法能够达到较高的准确率,并提升了系统的总吞吐量,对缓解信道参数过时现状和提高系统通信性能具有良好的促进作用。

With the advantages of high transmission rate and great security,the Aeronautical Mobile Airport Communications System(AeroMACS)has become an important part of the airport and air-ground communication network.Aiming at the problems of outdated Channel State Information(CSI)caused by fast time-varying channel during the high-speed movement phase of aircraft take-off and landing,and worsening of communication quality caused by large Doppler frequency shifts,a channel prediction method based on transformer neural-network is proposed by using a multi-head self-attention mechanism.Modulation Coding Scheme(MCS)for WiMAX and 5G dual-mode of AeroMACS is adjusted according to the Signal-to-Noise Ratio(SNR)predicted in real time.Simulation results show that,compared with the other three artificial intelligence methods,the proposed transformer network-based channel prediction method achieves higher accuracy and enhances the total throughput of the system,which can effectively cope with the problem of outdated CSI and improve the system communication performance.