Flexible Semantic-Aware Resource Allocation: Serving More Users Through Similarity Range Constraints
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 compared to state of the art methods based on quality of experience-aware SemCom methods.
Cite
@article{arxiv.2504.20939,
title = {Flexible Semantic-Aware Resource Allocation: Serving More Users Through Similarity Range Constraints},
author = {Nasrin Gholami and Neda Moghim and Behrouz Shahgholi Ghahfarokhi and Pouyan Salavati and Christo Kurisummoottil Thomas and Sachin Shetty and Tahereh Rahmati},
journal= {arXiv preprint arXiv:2504.20939},
year = {2025}
}