Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era.
翻译:大多数语义通信系统都利用深层次学习模型的改进,但迄今为止,研究主要集中在建筑和模型改进上,但这种经过全面数据集和英格迪频道反应培训的模型对于每个测试实例来说都不大可能是最佳的。由于模型能力的限制以及不完善的优化和概括,这种学习的模型将是不最优化的,特别是在测试数据分布或频道反应不同于培训阶段时,正如实践中可能发生的情况那样。为了解决这一难题,我们在本文件中提出了一个新的语义通信模式,利用深层次学习模型的属性过于合适。我们的模式在部署之后可以更新,这在传输率扭曲性(RD)性能方面可能进一步带来巨大收益。这个新系统被称为适应性静度通信(ASC)。在我们ASC系统中,无线传输流的成分既包括源数据的语义描述,也包括我们经调整的解码模型参数。具体地说,我们把概念过于极端的语义的语义化通信模式概念,在设计系统升级过程中提出一个数据系统大幅升级的代号,即整个数据系统升级的代号是整个数据损失率。