Flexible Semantic-Aware Resource Allocation: Serving More Users Through Similarity Range Constraints

2025-04-29

AI Summary

To address the trade-off between semantic reliability and service capacity in multi-user semantic communications, this paper proposes a semantic-aware flexible resource allocation mechanism. The method models inter-user semantic disparity as a quantifiable similarity-range constraint—first introduced in the literature—and formulates a novel resource allocation paradigm aimed at maximizing semantic satisfaction, thereby transcending conventional bit-level or QoE-driven optimization frameworks. A non-convex mixed-integer nonlinear programming (MINLP) model is established, and an efficient solution framework is developed by synergistically decomposing the problem and applying geometric programming (GP). Simulation results demonstrate that the proposed approach achieves up to a 17.1% improvement in user semantic satisfaction over state-of-the-art methods, while maintaining high semantic transmission reliability. This enhancement significantly boosts both service capacity and fairness in multi-user scenarios.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Satisfiability
• Multiagent Systems: Mechanism Design

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Semantic communication (SemCom) aims to enhance the resource efficiency of next-generation networks by transmitting the underlying meaning of messages, focusing on information relevant to the end user. Existing literature on SemCom primarily emphasizes learning the encoder and decoder through end-to-end deep learning frameworks, with the objective of minimizing a task-specific semantic loss function. Beyond its influence on the physical and application layer design, semantic variability across users in multi-user systems enables the design of resource allocation schemes that incorporate user-specific semantic requirements. To this end, emph{a semantic-aware resource allocation} scheme is proposed with the objective of maximizing transmission and semantic reliability, ultimately increasing the number of users whose semantic requirements are met. The resulting resource allocation problem is a non-convex mixed-integer nonlinear program (MINLP), which is known to be NP-hard. To make the problem tractable, it is decomposed into a set of sub-problems, each of which is efficiently solved via geometric programming techniques. Finally, simulations demonstrate that the proposed method improves user satisfaction by up to $17.1%$ compared to state of the art methods based on quality of experience-aware SemCom methods.

Problem

• Enhancing resource efficiency in semantic communication networks
• Maximizing transmission and semantic reliability for users
• Solving non-convex MINLP via geometric programming decomposition

Innovation

• Semantic-aware resource allocation for multi-user systems
• Decomposition into sub-problems via geometric programming
• Enhancing user satisfaction by 17.1% over benchmarks