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Fair Resource Allocation for Probabilistic Semantic Communication in IIoT

Siyun Liang, Zhouxiang Zhao, Chen Zhu, Zhaohui Yang, Yinchao Yang, Mohammad Shikh-Bahaei, Zhaoyang Zhang

TL;DR

This paper tackles fair resource allocation in probabilistic semantic communication (PSCom) for IIoT by jointly considering transmission and computation costs. A multi-user uplink PSCom model is developed where semantic compression, guided by local probability graphs and a shared probability graph with the BS, yields an equivalent rate \(R_n = \frac{B}{\eta_n}\log_2\left(1+\frac{p_n^{t} h_n}{\sigma^2}\right)\) with computation cost \(p_n^c = g_n(\eta_n) p_0\); the objective is to maximize the minimum \(R_n\) under a total power constraint \(\sum_n (p_n^t+p_n^c) \le P^{max}\). Two suboptimal algorithms are proposed: Method-1 uses rate-equality assumptions and a bisection over the target rate \tau with a beta-based initial power allocation, while Method-2 fixes the computation-load endpoints and performs a \tau-bisection with closed-form transmission power; both ensure feasibility with respect to the power budget. Simulations show both methods outperform baselines (equal power and non-semantic transmission), with Method-2 offering marginal gains at higher complexity, validating the effectiveness of balancing computation and transmission in PSCom for fair multi-user performance. The work demonstrates practical-path decisions for deploying semantic compression in IIoT while maintaining fairness and energy efficiency.

Abstract

In this paper, the problem of minimum rate maximization for probabilistic semantic communication (PSCom) in industrial Internet of Things (IIoT) is investigated. In the considered model, users employ semantic information extraction techniques to compress the original data before sending it to the base station (BS). During this semantic compression process, knowledge graphs are employed to represent the semantic information, and the probability graph sharing between users and the BS is utilized to further compress the knowledge graph. The semantic compression process can significantly reduce the transmitted data size, but it inevitably introduces additional computation overhead. Considering the limited power budget of the user, we formulate a joint communication and computation optimization problem is formulated aiming to maximize the minimum equivalent rate among all users while meeting total power and semantic compression ratio constraints. To address this problem, two algorithms with different computational complexities are proposed to obtain suboptimal solutions. One algorithm is based on a prorate distribution of transmission power, while the other traverses the combinations of semantic compression ratios among all users. In both algorithms, bisection is employed in order to achieve the greatest minimum equivalent rate. The simulation results validate the effectiveness of the proposed algorithms.

Fair Resource Allocation for Probabilistic Semantic Communication in IIoT

TL;DR

This paper tackles fair resource allocation in probabilistic semantic communication (PSCom) for IIoT by jointly considering transmission and computation costs. A multi-user uplink PSCom model is developed where semantic compression, guided by local probability graphs and a shared probability graph with the BS, yields an equivalent rate \(R_n = \frac{B}{\eta_n}\log_2\left(1+\frac{p_n^{t} h_n}{\sigma^2}\right)\) with computation cost \(p_n^c = g_n(\eta_n) p_0\); the objective is to maximize the minimum under a total power constraint \(\sum_n (p_n^t+p_n^c) \le P^{max}\). Two suboptimal algorithms are proposed: Method-1 uses rate-equality assumptions and a bisection over the target rate \tau with a beta-based initial power allocation, while Method-2 fixes the computation-load endpoints and performs a \tau-bisection with closed-form transmission power; both ensure feasibility with respect to the power budget. Simulations show both methods outperform baselines (equal power and non-semantic transmission), with Method-2 offering marginal gains at higher complexity, validating the effectiveness of balancing computation and transmission in PSCom for fair multi-user performance. The work demonstrates practical-path decisions for deploying semantic compression in IIoT while maintaining fairness and energy efficiency.

Abstract

In this paper, the problem of minimum rate maximization for probabilistic semantic communication (PSCom) in industrial Internet of Things (IIoT) is investigated. In the considered model, users employ semantic information extraction techniques to compress the original data before sending it to the base station (BS). During this semantic compression process, knowledge graphs are employed to represent the semantic information, and the probability graph sharing between users and the BS is utilized to further compress the knowledge graph. The semantic compression process can significantly reduce the transmitted data size, but it inevitably introduces additional computation overhead. Considering the limited power budget of the user, we formulate a joint communication and computation optimization problem is formulated aiming to maximize the minimum equivalent rate among all users while meeting total power and semantic compression ratio constraints. To address this problem, two algorithms with different computational complexities are proposed to obtain suboptimal solutions. One algorithm is based on a prorate distribution of transmission power, while the other traverses the combinations of semantic compression ratios among all users. In both algorithms, bisection is employed in order to achieve the greatest minimum equivalent rate. The simulation results validate the effectiveness of the proposed algorithms.
Paper Structure (11 sections, 15 equations, 7 figures, 1 table, 2 algorithms)

This paper contains 11 sections, 15 equations, 7 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Semantic Communication Model
  4. Transmission Model
  5. Computation Model
  6. Problem Formulation
  7. Algorithm Design
  8. Method-1
  9. Method-2
  10. Simulation Results and Analysis
  11. Conclusion

Figures (7)

  • Figure 1: The considered PSCom network.
  • Figure 2: An illustration of a probability graph in the considered PSCom system.
  • Figure 3: An illustration of a knowledge graph.
  • Figure 4: Illustration of computation load vs. semantic compression ratio $\eta$.
  • Figure 5: Maximum power vs. Optimization target $\tau$
  • ...and 2 more figures