基于多标签分类算法的多输入多输出智能接收机模型

传统无线通信系统由发射机和接收机组成，待传输的信息经过信道编码、调制、成型后通过天线发射出去。由于信道衰落、噪声和干扰等因素的影响，到达接收机的信号会存在较严重的失真，接收机需要从失真的信号中尽可能地恢复出原始信息。为解决此问题，提出基于多标签分类神经网络的多输入多输出（MIMO）智能接收机模型。该模型利用深度神经网络（DNN）替代接收机从信号到信息之间的整个信息恢复环节，并采用多标签分类算法代替多个二分类器实现多个比特的信息流恢复，而训练数据集为包含二进制相移键控（BPSK）与正交相移键控（QPSK）两种调制方式以及汉明编码与循环编码两种方式的正交信号。实验结果表明在噪声、瑞利衰落、干扰等情况下，使用传统Alamouti译码方法的接收机误码率（BER）为1E-3时，智能接收机已经实现了BER为0的恢复信息；在保持BER性能相同时，所提多标签分类算法比对比模型的多个二分类器算法在每个批次的训练时间上减少了约4 min。

Multi-input multi-output intelligent receiver model based on multi-label classification algorithm

The traditional wireless communication system is composed of transmitters and receivers. The information to be transmitted is transmitted through antenna after channel coding， modulation， and shaping. Due to the influence of factors such as channel fading， noise， and interference， signals arriving at the receiver will have serious distortion， and the receiver needs to recover original information from distorted signals as much as possible. To solve this problem， a Multi-Input Multi-Output （MIMO） intelligent receiver model based on multi-label classification neural network was proposed. In this model， Deep Neural Network （DNN） was used to replace the entire information recovery link of receiver from signals to information， and multi-label classification algorithm was used to replace multiple binary classifiers to achieve multi-bit information flow recovery. The training dataset has quadrature signals that contains two modulation modes including Binary Phase Shift Keying （BPSK） and Quadrature Phase Shift Keying （QPSK） as well as two coding modes of Hamming coding and cyclic coding. Experimental results show that under conditions such as noise， Rayleigh fading， and interference， when the Bit Error Rate （BER） of receiver using the traditional Alamouti decoding method is 1E-3， the intelligent receiver realizes the recovered information with the BER of 0. While maintaining the same BER performance， the proposed multi-label classification algorithm reduces the training time of each batch by about 4 min compared with the multiple binary classifier algorithms of the comparison model.