Matching-Theory-Based Cooperative D2D Semantic Content Sharing
Zhi Ji, Dan Wu, Xinxin Shen, Xinrong Guan · IEEE Internet of Things Journal · 2025
Device-to-Device (D2D) content sharing supports real-time applications but still faces challenges of large data and limited resources. With the growing computing capabilities of terminal devices, semantic content sharing has emerged as a promising solution. In this paper, a cooperative transmission D2D semantic content sharing based on probabilistic graphs is investigated to enhance user quality of experience (QoE). Specifically, D2D is divided into requesters and helpers. It is noteworthy that if both matching entities have knowledge bases, smaller-sized semantic information can be obtained by further compressing the semantic data. To encourage cooperation between D2D, we design utility functions for helpers and requesters, and formulate an optimization problem to maximize system utility by optimizing compression ratio, transmit power, and D2D pairing. In order to solve this problem, we introduce a matching game framework. First, we use an Nelder-Mead (NM)-based heuristic algorithm to solve the optimization problem for the compression ratio and transmit power. Then, a distributed cooperative semantic content sharing matching algorithm is proposed to achieve one-to-one matching, ultimately resulting in a stable strategy. The simulation results validate the optimality and convergence of the proposed algorithm. Compared to classical distributed algorithms, the proposed algorithm improves QoE performance by over 4.8%.