An Information-Theoretic Metric for Semantic Value of Spatiotemporal Information

2026-06-25

AI Summary

This study addresses the challenge of quantifying the value of task-oriented semantic information in wireless communications, where spectrum resources are limited and existing approaches struggle to account for complex spatiotemporal correlations. To this end, the paper proposes a Semantic Value of Information (SVoI) framework grounded in mutual information, which measures the reduction in uncertainty about unknown system states when leveraging historical semantic observations. This work establishes, for the first time, a unified metric that jointly incorporates semantic content, spatiotemporal dependencies, information timeliness, and channel conditions. Under a Gaussian Markov model, closed-form expressions for SVoI and its upper and lower bounds are derived, and the impacts of separable versus coupled spatiotemporal structures on semantic value are analyzed. Both theoretical analysis and simulations validate the efficacy of the proposed framework, offering an optimizable objective function for semantic-aware communication systems.

Abstract

With the explosive growth of network scale and data volume, wireless communication is facing an increasingly severe limitation of spectrum resources. Semantic communication has emerged as a promising paradigm to break the bandwidth bottleneck by transmitting significant task-oriented semantic information rather than raw data. In practical real-time wireless applications, semantics of information exhibit diverse spatial and temporal correlations depending on intrinsic dynamics of source and extrinsic dynamics of environment. Motivated by this observation, this paper develops a novel information-theoretic metric to quantify the semantic value of spatiotemporal information. Specifically, a semantic value of information (SVoI) framework is proposed based on the mutual information, which characterises the reduction in uncertainty when predicting an unknown system state using past semantic spatiotemporal correlated observations. Focusing on general Gaussian Markov models, closed-form expressions of the SVoI are derived. Effects of both separable and coupled spatiotemporal correlations on SVoI are further investigated analytically. Numerical simulations are conducted to validate the theoretical analysis of SVoI and its bounds. The proposed SVoI metric jointly captures the impact of semantic spatiotemporal correlation of source, timeliness of information, and channel conditions, which could serve as an effective optimisation objective for the design of next-generation semantic-aware communication systems.

Problem

• semantic communication
• spatiotemporal correlation
• semantic value of information
• spectrum efficiency
• information theory

Innovation

• Semantic Value of Information
• Spatiotemporal Correlation
• Information Theory
• Semantic Communication
• Gaussian Markov Models