We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training.
翻译:我们提出了一个基于深度学习的联合源源码(JSCC)计划,用于在多路退位频道和非线性信号剪切的多路退位频道上进行无线图像传输。拟议的编码器和解码器使用共振神经网络(CNN),并将源图像直接映射为具有复杂价值的基带样本,用于正心频率分多路传输(OFDM) 。拟议的模型驱动机学习方法消除了对不同源和通道编码的需要,同时结合了一个离子数据解位,以对付多路退位通道。端到端的JSC通信系统将可训练的CNN层与代表多路信道模型和DM信号处理区块的不锈但不同的层结合起来。我们的结果表明,通过将DM基带处理区块纳入机器学习框架,从而极大地提高了总体性能。我们的方法超越了使用最新但独立的源码和频道编码(如BPG和LDPC)的常规方案。此外,我们的方法在各种DM条件下对非线性、但又具有很强的信号性模型,无法将DM在各种频道中使用的DDDD中进行匹配。