Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications
<|reference_start|>Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications: Semantic communications (SC) is an emerging communication paradigm in which wireless devices can send only relevant information from a source of data while relying on computing resources to regenerate missing data points. However, the design of a multi-user SC system becomes more challenging because of the computing and communication overhead required for coordination. Existing solutions for learning the semantic language and performing resource allocation often fail to capture the computing and communication tradeoffs involved in multiuser SC. To address this gap, a novel framework for decentralized computing and communication resource allocation in multiuser SC systems is proposed. The challenge of efficiently allocating communication and computing resources (for reasoning) in a decentralized manner to maximize the quality of task experience for the end users is addressed through the application of Stackelberg hyper game theory. Leveraging the concept of second-level hyper games, novel analytical formulations are developed to model misperceptions of the users about each other's communication and control strategies. Further, equilibrium analysis of the learned resource allocation protocols examines the convergence of the computing and communication strategies to a local Stackelberg equilibria, considering misperceptions. Simulation results show that the proposed Stackelberg hyper game results in efficient usage of communication and computing resources while maintaining a high quality of experience for the users compared to state-of-the-art that does not account for the misperceptions.<|reference_end|>
@article{thomas2024hypergame,
    title={Hypergame Theory for Decentralized Resource Allocation in Multi-user
  Semantic Communications},
    author={Christo Kurisummoottil Thomas, Walid Saad},
    journal={arXiv preprint arXiv:2409.17985},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17985},
    primaryClass={cs.IT cs.LG math.IT}
}