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 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…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Cognitive Computing and Networks · Robotics and Automated Systems
