The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model's overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the bandwidth ratio-distortion performance. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Our experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

翻译：新兴的语义通信领域正推动端到端数据传输的研究。通过利用深度学习模型的强大表示能力，学习型数据传输方案已展现出比传统信源和信道编码方法更优越的性能。然而，目前的研究主要集中于针对静态目标域的架构与模型改进。尽管这些学习型模型取得了成功，但由于模型容量限制、优化不完善及泛化能力不足，在测试数据分布或信道响应与模型训练时所采用的条件不同（这在现实世界中很可能发生）的情况下，它们仍非最优方案。为解决这一问题，我们提出了一种新颖的在线学习联合信源信道编码方法，利用深度学习模型的过拟合特性。具体而言，我们在部署后以轻量级在线方式更新现成的预训练模型，以适应源数据与环境域中的分布偏移。我们将过拟合概念推向极致，提出了一系列易于实现的方案，使编解码模型或表示适应于单个数据或信道状态实例，从而在带宽比-失真性能方面带来显著增益。所提方法能够在不牺牲解码速度的情况下，实现网络中所有参数的高效通信适配。我们在持续变化的源数据和无线信道环境上进行的实验（包括用户研究）表明，我们的方法有效且高效，其性能优于现有的最先进工程化传输方案（VVC结合5G LDPC编码传输）。