Semantic Communication in Multiteam Dynamic Games: A Mean Field Perspective

• 成果类型: Article
• 署名作者: Aggarwal, Shubham; Zaman, Muhammad Aneeq Uz; Bastopcu, Melih; Basar, Tamer
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; Ihsan Dogramaci Bilkent University; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3583625
• 发表日期: 2026
• 关键词: systems

摘要

Coordinating communication and control is a key component in the stability and performance of networked multiagent systems. While single user networked control systems have gained a lot of attention within this domain, in this work, we address the more challenging problem of large population multiteam dynamic games. In particular, each team constitutes two decision-makers (namely, the sensor and the controller) who coordinate over a shared network to control a dynamically evolving state of interest under costs on both actuation and sensing/communication. Due to the shared nature of the wireless channel, the overall cost of each team depends on other teams' policies, thereby leading to a noncooperative game setup. Due to the presence of a large number of teams, we compute approximate decentralized Nash equilibrium policies for each team using the paradigm of (extended) mean-field games, which is governed by 1) the mean traffic flowing over the channel, and 2) the value of information at the sensor, which highlights the semantic nature of the ensuing communication. In the process, we compute optimal controller policies and approximately optimal sensor policies for each representative team of the mean-field system to alleviate the problem of general noncontractivity of the mean-field fixed point operator associated with the finite cardinality of the sensor action space. Consequently, we also prove the & varepsilon;-Nash property of the mean-field equilibrium solution which essentially characterizes how well the solution derived using mean-field analysis performs on the finite-team system. Finally, we provide extensive numerical simulations, which corroborate the theoretical findings and lead to additional insights on the properties of the results presented.