基于自编码器的动态协作中继系统

Dynamic cooperative relay system based on autoencoder

鉴于大多数现有端到端自编码器(AE)仅适用于点对点的通信场景,提出一种基于AE的动态协作通信系统,将基于深度学习的AE扩展到多点通信系统。构建了3个神经网络子系统,分别用于学习发送端、中继节点和接收端的最佳编码、传输和解码,通过三者的联合训练达到多点通信系统的最佳传输性能。其中,发送端和接收端使用一维卷积层进行信号特征的提取及学习,中继节点通过引入密集层和一维卷积层,支持放大转发(AF)和解码转发(DF)两种经典的中继协作方式。仿真实验表明,在加性高斯白噪声以及瑞利衰落信道条件下,提出的模型采用两种不同的协作方式,其误码性能均优于单一点到点通信系统,验证了系统方案的可行性和有效性。此外,该系统支持动态的节点拓扑结构,在无需额外训练的条件下,本系统支持中继节点数量实时变化。

Considering that most existing end-to-end Autoencoders(AE)are only suitable for point-to-point communication scenarios,this paper proposes a dynamic collaborative communication system based on AE,extending the AE based on deep learning to multi-point communication systems.Three neural network subsystems are constructed,each for learning the optimal encoding,transmission,and decoding at the transmitter,relay node,and receiver,respectively,with joint training of the three to achieve the best transmission performance of the multi-point communication system.Among them,the transmitter and receiver use one-dimensional convolutional layers for signal feature extraction and learning,while the relay node supports two classic relay cooperation methods,Amplify-and-Forward(AF)and Decode-and-Forward(DF),by introducing dense layers and one-dimensional convolutional layers.Simulation experiments show that under the conditions of additive white Gaussian noise and Rayleigh fading channels,the proposed model,using two different cooperation methods,has better error performance than a single point-to-point communication system,verifying the feasibility and effectiveness of the system scheme.In addition,the system supports dynamic node topologies,and without the need for additional training,this system supports real-time changes in the number of relay nodes.