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

Zhouxiang Zhao, Zhaohui Yang, Chongwen Huang, Li Wei, Qianqian Yang, Caijun Zhong, Wei Xu, Zhaoyang Zhang

TL;DR

The paper tackles spectral-efficient joint communication and computation for distributed RISs aided probabilistic semantic communication in IIoT by formulating a semantic-aware sum-rate objective that includes RIS-user association, phase shifts, semantic compression ratios, and computation budgets. It introduces a solution framework combining many-to-many RIS-user matching, greedy semantic compression optimization, tensor-based phase beamforming, and convex power control in an alternating optimization loop. Key contributions include a scalable RIS-user association method, a practical greedy scheme for allocating semantic compression, and closed-form or tensor-based beamforming strategies that accommodate different multi-user/multi-RIS scenarios, with numerical results showing superiority over conventional centralized RIS setups. The approach demonstrates significant gains in semantic-aware rate, enabling efficient, computation-aware PSC for large-scale IIoT deployments with distributed RISs.

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.

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

TL;DR

The paper tackles spectral-efficient joint communication and computation for distributed RISs aided probabilistic semantic communication in IIoT by formulating a semantic-aware sum-rate objective that includes RIS-user association, phase shifts, semantic compression ratios, and computation budgets. It introduces a solution framework combining many-to-many RIS-user matching, greedy semantic compression optimization, tensor-based phase beamforming, and convex power control in an alternating optimization loop. Key contributions include a scalable RIS-user association method, a practical greedy scheme for allocating semantic compression, and closed-form or tensor-based beamforming strategies that accommodate different multi-user/multi-RIS scenarios, with numerical results showing superiority over conventional centralized RIS setups. The approach demonstrates significant gains in semantic-aware rate, enabling efficient, computation-aware PSC for large-scale IIoT deployments with distributed RISs.

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.
Paper Structure (16 sections, 41 equations, 17 figures, 2 algorithms)

This paper contains 16 sections, 41 equations, 17 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel Model
  4. Transmission Model
  5. Semantic Computation and Transmission Models
  6. Problem Formulation
  7. Algorithm Design
  8. Many-to-Many Matching
  9. Semantic Compression Ratio Optimization
  10. Transmit and Phase Beamforming Design
  11. One RIS Servers Multiple Users
  12. Multiple RISs Serve One User
  13. Power Control
  14. Overall Algorithm
  15. Simulation Results
  16. ...and 1 more sections

Figures (17)

  • Figure 1: A wireless communication system with distributed RISs, one BS, and multiple users.
  • Figure 2: The compute-then-transmit protocol for probabilistic semantic communication systems.
  • Figure 3: Illustration of the computation overhead versus the semantic compression ratio.
  • Figure 4: Three kinds of RIS distributions. Distributed RISs scheme 1 is colored in green, distributed RISs scheme 2 is colored in blue, and central RIS is colored in red.
  • Figure 5: The user distribution case 1 around the central RIS.
  • ...and 12 more figures