activity
20232026
most citedJoint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2026

Secure Semantic Communications via AI Defenses: Fundamentals, Solutions, and Future Directions

Lan Zhang, Chengsi Liang, Zeming Zhuang +4

Semantic communication (SemCom) redefines wireless communication from reproducing symbols to transmitting task-relevant semantics. However, this AI-native architecture also introdu…

eess.SY2025

Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks

Le Xia, Yao Sun, Haijian Sun +3

Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus natural…

cs.NI2024

Hybrid Semantic/Bit Communication Based Networking Problem Optimization

Le Xia, Yao Sun, Dusit Niyato +3

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where t…

eess.SP2024

S-RAN: Semantic-Aware Radio Access Networks

Yao Sun, Lan Zhang, Linke Guo +3

Semantic communication (SemCom) has been a transformative paradigm, emphasizing the precise exchange of meaningful information over traditional bit-level transmissions. However, ex…

cs.IT2024

Improving Channel Resilience for Task-Oriented Semantic Communications: A Unified Information Bottleneck Approach

Shuai Lyu, Yao Sun, Linke Guo +4

Task-oriented semantic communications (TSC) enhance radio resource efficiency by transmitting task-relevant semantic information. However, current research often overlooks the inhe…

cs.NI2023★ 1 cited

Joint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks

Xingqiu He, Chaoqun You, Zihan Chen +4

Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing…