论文标题
Demodnet：使用卷积神经网络从硬信息中学习软解调
DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network
论文作者
论文摘要
软解码是传统通信接收器的基本模块。它将接收到的符号转换为软位，即对数可能比率（LLRS）。但是，在非理想的白色高斯噪声（AWGN）通道中，很难准确计算LLR。在这封信中，我们基于具有可变输入和输出长度的完全卷积神经网络提出了一个解调器Demodnet。我们使用硬位信息来训练Demodnet，并根据训练有素的Demodnet的输出层提出对数概率比（LPR），以实现软解调。仿真结果表明，在AWGN渠道下，硬重调和Demodnet的软解调的性能非常接近传统方法。在三个非理想的通道方案中，即存在频率偏差，加性通用高斯噪声（AGGN）通道和瑞利褪色通道，使用Demodnet获得的软信息LPR的传道解码性能优于使用理想的AWGN假设下计算的精确LLR来解码的性能。
Soft demodulation is a basic module of traditional communication receivers. It converts received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the nonideal additive white Gaussian noise (AWGN) channel, it is difficult to accurately calculate the LLR. In this letter, we propose a demodulator, DemodNet, based on a fully convolutional neural network with variable input and output length. We use hard bit information to train the DemodNet, and we propose log probability ratio (LPR) based on the output layer of the trained DemodNet to realize soft demodulation. The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods. In three non-ideal channel scenarios, i.e., the presence of frequency deviation, additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading channel, the performance of channel decoding using the soft information LPR obtained by DemodNet is better than the performance of decoding using the exact LLR calculated under the ideal AWGN assumption.
