In this paper, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in industrial Internet-of-Things (IIoT) is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the transmission data size. To support high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following greedy policy, while the phase shift of RIS can be optimized using the tensor based beamforming. Numerical results verify the superiority of the proposed algorithm.

翻译：本文研究了工业物联网中分布式可重构智能表面（RISs）辅助概率语义通信（PSC）的频谱高效通信与计算资源分配问题。在所考虑模型中，多个RIS被部署以服务多个用户，而PSC采用先计算后传输协议以减少传输数据量。为支持高速率传输，必须联合考虑语义压缩比、发射功率分配和分布式RIS部署。该联合通信与计算问题被建模为一个优化问题，其目标是在总发射功率、相移、RIS-用户关联和语义压缩比约束下最大化系统语义感知总传输速率。为解决该问题，提出了一种多对多匹配方案以处理RIS-用户关联子问题，采用贪心策略解决语义压缩比子问题，而RIS的相移可通过基于张量的波束成形进行优化。数值结果验证了所提算法的优越性。