We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 – 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channel coding such as Better Portable Graphics (BPG) and Low-Density Parity-Check (LDPC) schemes. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation.

中文翻译：

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用于无线多径衰落信道的 OFDM 引导的深度联合信源信道编码

我们研究了多径衰落信道上无线图像传输的联合源信道编码 (JSCC)。受最近基于深度学习的 JSCC 和基于模型的学习方法的启发，我们将自动编码器与正交频分复用 (OFDM) 相结合以应对多径衰落。所提出的编码器和解码器使用卷积神经网络 (CNN) 并将源图像直接映射到复值基带样本以进行 OFDM 传输。多径信道和 OFDM 由不可训练（确定性）但可区分的层表示，因此可以对系统进行端到端训练。此外，我们的 JSCC 解码器进一步结合了显式信道估计、均衡和额外的子网来提高性能。所提出的方法展示了 2。与采用最先进但分离源和通道编码的传统方案（例如更好的便携式图形 (BPG) 和低密度奇偶校验 (LDPC) 方案）相比，等效图像质量的 SNR 增益为 5 – 4 dB。当系统结合信道状态信息 (CSI) 反馈时，性能会进一步提高。所提出的方案对训练和评估中使用的信道模型的 OFDM 信号削波和参数不匹配具有鲁棒性。