Joint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks
Xingqiu He, Chaoqun You, Zihan Chen, Yao Sun, Dongzhu Liu, Tony Q. S. Quek, Yue Gao

TL;DR
This paper proposes an adaptive semantic communication framework for 5G networks that dynamically adjusts DNN-based transceivers to optimize system utility considering energy and latency constraints.
Contribution
It introduces a novel joint user association and resource allocation method for adaptive semantic communication, addressing user heterogeneity and system efficiency.
Findings
Outperforms existing baselines in simulations.
Efficient polynomial-time algorithm for joint optimization.
Demonstrates feasibility of dynamic adjustment of semantic transceivers.
Abstract
Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing the volume of transmitted data. In existing implementations, the semantic transceiver is typically pre-trained for a specific task and uniformly adopted by all users. However, due to user heterogeneity in computational and communication capabilities, employing a single, fixed semantic transceiver may degrade the coding efficiency and transmission robustness. To address this issue, we first demonstrate the feasibility of dynamically adjusting the computational and communication overhead of DNN-based semantic transceivers, enabling a more flexible paradigm referred to as Adaptive Semantic Communication (ASC). Building on this concept, we formulate a joint user association and resource allocation problem for ASC…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Age of Information Optimization
