52 citations · 64 across the 15 of their papers we have counts for
7 papers · 1 filter
Rate Maximization for UAV-assisted ISAC System with Fluid Antennas
Xingtao Yang, Zhenghe Guo, Siyun Liang +3
This letter investigates the joint sensing problem between unmanned aerial vehicles (UAV) and base stations (BS) in integrated sensing and communication (ISAC) systems with fluid a…
Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission
Chen Zhu, Siyun Liang, Zhouxiang Zhao +4
Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission effic…
Fair Rate Maximization for Fluid Antenna Relay (FAR)-assisted Multi-user MISO Communications
Ruopeng Xu, Zhaohui Yang, Ting Zhang +3
In this paper, we investigate the problem of max-min rate maximization in fluid antenna relay (FAR)-assisted multi-user uplink multiple-input single-output (MISO) wireless systems,…
Rate Maximization for Fluid Antenna System Assisted Semantic Communication
Siyun Liang, Chen Zhu, Zhaohui Yang +4
In this paper, we investigate the problem of rate maximization in a fluid antenna system (FAS) assisted semantic communication system. In the considered model, a base station (BS)…
Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks
Chen Zhu, Kang Liang, Jianrong Bao +4
The advent of 6G networks demands unprecedented levels of intelligence, adaptability, and efficiency to address challenges such as ultra-high-speed data transmission, ultra-low lat…
Fair Resource Allocation in UAV-based Semantic Communication System with Fluid Antenna
Liang Siyun, Chen Zhu, Zhaohui Yang +4
In this paper, the problem of maximization of the minimum equivalent rate in a unmanned-aerial-vehicle (UAV)-based multi-user semantic communication system is investigated. In the…
Efficient Split Federated Learning for Large Language Models over Communication Networks
Kai Zhao, Zhaohui Yang, Ye Hu +3
Fine-tuning pre-trained large language models (LLMs) in a distributed manner poses significant challenges on resource-constrained edge networks. To address this challenge, we propo…