Fair Resource Allocation for Probabilistic Semantic Communication in IIoT

TL;DR

This paper addresses optimizing resource allocation in probabilistic semantic communication for IIoT, balancing data compression, power constraints, and computational overhead to improve minimum data rates.

Contribution

It introduces a joint optimization framework for semantic compression and power allocation in IIoT, proposing two algorithms to enhance communication efficiency under constraints.

Findings

Proposed algorithms effectively improve minimum user rates.

Semantic compression reduces data size significantly.

Algorithms outperform baseline methods in simulations.

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.