In this letter, based on deep joint source-channel coding (DeepJSCC), we propose a channel adaptive scheme based on entropy model and a subchannel matching method with entropy indication to minimize reconstruction distortion for wireless image transmission over orthogonal frequency division multiplexing (OFDM) channels. Specifically, after an image is compressed and packaged into several OFDM packets, the more critical OFDM packets are mapped to subchannels with higher quality based on estimated channel state information (CSI). In addition, after analyzing the effect of channel signal-to-noise ratio (CSNR) on the parameters of our network model, we achieve the adaptation of a single model to various CSNRs simply by adapting the training strategy, without the need to input CSNR into additionally introduced network. Extensive numerical experiments show that our method achieves state-of-the-art performance among existing DeepJSCC schemes over OFDM channels.

中文翻译：

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基于熵模型的 OFDM 信道无线自适应图像传输


在本文中，基于深度联合源-信道编码 （DeepJSCC），我们提出了一种基于熵模型的信道自适应方案和一种带有熵指示的子信道匹配方法，以最大限度地减少正交频分复用 （OFDM） 信道上无线图像传输的重建失真。具体来说，在将映像压缩并打包成多个 OFDM 数据包后，根据估计的信道状态信息 （CSI），更关键的 OFDM 数据包将映射到质量更高的子信道。此外，在分析了信道信噪比 （CSNR） 对网络模型参数的影响后，我们只需调整训练策略，即可实现单个模型对各种 CSNR 的适应，而无需将 CSNR 输入到额外引入的网络中。广泛的数值实验表明，我们的方法在 OFDM 信道上实现了现有 DeepJSCC 方案中最先进的性能。