End-to-End Optimization of Semantic Communication Systems: Joint Source-Channel-Host-Tasks

In images semantic communication, the efficacy of communication systems is intricately influenced by multiple factors, including images compression rates, semantic noise, channel noise, etc. These factors exhibit intricate interdependencies and pose formidable challenges when it comes to their quantitative assessment through mathematical formulations. Furthermore, semantic communication tailored to various image transmission tasks imposes different requirements on the system, necessitating a holistic consideration of these influential elements. This paper presents a method devised to optimize images semantic communication systems by joining the facets of Source-Channel-Host-Task (JSCHT). The primary objective of proposing this approach is to ensure the successful execution of images communication tasks. Initially, we propose an adaptive scheme for semantic compression. This scheme is proposed to tackle the intricate problem of optimally aligning semantic information with channel states. This scheme establishes a dynamic linkage between the source and the channel, adjusting the degree of semantic compression in response to signal-to-noise ratios (SNRs). As a consequence, the system's flexibility is heightened. Subsequently, we architect an end-to-end codec network grounded in deep learning (DL) principles to tackle the challenge of accommodating SNRs changes. The differentiable channel model is integrated into this framework as a network layer, actively participating in the processes of backpropagation and gradient computation. Through the implementation of end-to-end training by joint source-dynamic noise channel-host, system robustness undergoes pronounced enhancement. In addition, within the semantic communication environment for various image transmission tasks, an on-demand feature extraction module is incorporated to establish a contextual connection between the information source and task. The redundant components within an image will be removed based on different task objectives during the process of image transmission. The efficacy of the proposed approach is evaluated across three channel models varying in complexity. Simulation results underscore the system's capacity to accommodate fluctuations in SNR and its role in facilitating the optimal execution of images communication tasks.