A Joint Communication and Computation Design for Distributed RISs Assisted Probabilistic Semantic Communication in IIoT

TL;DR

This paper proposes a joint optimization framework for distributed RIS-assisted probabilistic semantic communication in IIoT, enhancing spectral efficiency and resource allocation through novel algorithms and tensor-based beamforming.

Contribution

It introduces a comprehensive joint design for communication and computation in RIS-assisted semantic communication, including a matching scheme and greedy policy for optimization.

Findings

Proposed algorithm outperforms existing methods in spectral efficiency.

Joint optimization improves resource utilization in IIoT scenarios.

Tensor-based beamforming enhances phase shift optimization accuracy.

Abstract

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.