Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis

TLDR

Semantic communication promises to surpass the Shannon limit and enable future 6G applications, yet current work focuses on high‑performance algorithms while overlooking energy efficiency, image‑retrieval constraints, and user‑personality‑aware encoding, hindering widespread adoption. The study targets UAV image‑sensing task‑oriented semantic communications, proposing an energy‑efficient framework with a triple‑based scene graph and a game‑theoretic multi‑user resource allocation to mitigate fading channel effects. The mechanism comprises an energy‑efficient task‑oriented framework using a triple‑based scene graph, a personalized semantic encoder driven by user interests, and a game‑theoretic multi‑user resource allocation to counter dynamic fading. Numerical experiments on real datasets show that the proposed framework markedly improves personalization, anti‑interference, and overall communication quality in semantic communications.

Abstract

Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e.g., smart healthcare. In this paper, we focus on UAV image-sensing-driven task-oriented semantic communications scenarios. The majority of existing work has focused on designing advanced algorithms for high-performance semantic communication. However, the challenges, such as energy-hungry and efficiency-limited image retrieval manner, and semantic encoding without considering user personality, have not been explored yet. These challenges have hindered the widespread adoption of semantic communication. To address the above challenges, at the semantic level, we first design an energy-efficient task-oriented semantic communication framework with a triple-based scene graph for image information. We then design a new personalized semantic encoder based on user interests to meet the requirements of personalized saliency. Moreover, at the communication level, we study the effects of dynamic wireless fading channel on semantic transmission mathematically and thus design an optimal multi-user resource allocation scheme by using game theory. Numerical results based on real-world datasets clearly indicate that the proposed framework and schemes significantly enhance the personalization and anti-interference performance of semantic communication, and are also efficient to improve the communication quality of semantic communication services.