Semantic Communication with Probability Graph: A Joint Communication and Computation Design

TL;DR

This paper introduces a probability graph-based semantic compression system that reduces data size by sharing background knowledge, enabling efficient joint communication and computation resource management in wireless networks.

Contribution

It proposes a novel semantic compression method using probability graphs and addresses joint resource allocation for minimizing energy consumption.

Findings

Effective semantic data compression demonstrated in simulations

Significant reduction in communication resource usage

Joint optimization improves overall network efficiency

Abstract

In this paper, we present a probability graph-based semantic information compression system for scenarios where the base station (BS) and the user share common background knowledge. We employ probability graphs to represent the shared knowledge between the communicating parties. During the transmission of specific text data, the BS first extracts semantic information from the text, which is represented by a knowledge graph. Subsequently, the BS omits certain relational information based on the shared probability graph to reduce the data size. Upon receiving the compressed semantic data, the user can automatically restore missing information using the shared probability graph and predefined rules. This approach brings additional computational resource consumption while effectively reducing communication resource consumption. Considering the limitations of wireless resources, we address the problem of joint communication and computation resource allocation design, aiming at minimizing the total communication and computation energy consumption of the network while adhering to latency, transmit power, and semantic constraints. Simulation results demonstrate the effectiveness of the proposed system.