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强大的表征能力，学习型数据传输方案已展现出优于传统信源和信道编码方法的性能。然而，目前研究主要集中于面向静态目标领域的架构与模型改进。尽管取得了成功，这类学习模型仍受限于模型容量不足、优化不完善及泛化能力有限等问题，尤其在测试数据分布或信道响应与模型训练时采用的环境存在差异（这在现实场景中很可能发生）的情况下表现欠佳。针对这一挑战，我们提出一种新颖的在线学习联合信源信道编码方法，该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，提出一系列易于实现的方案，使编解码模型或表征适应单个数据或信道状态实例，从而在带宽率失真性能上带来显著提升。所提方法可在不牺牲解码速度的前提下，实现网络中所有参数的通信高效自适应。在持续变化的源数据及无线信道环境上的实验（包括用户研究）表明，我们的方法在有效性和效率上均优于现有最先进工程化传输方案（VVC结合5G LDPC编码传输）。