5 papers
Joint Beamforming Design and Port Selection in Fluid Antenna-Assisted Multi-Cell Networks: A Personalized Federated Learning Approach
Liwen Gao, Li Zheng, Xing Hao +2
This paper investigates joint beamforming and port selection in multi-cell fluid antenna-assisted (FAS) networks. In such networks, active beamforming and discrete FA port selectio…
Hierarchical Reinforcement Learning for Next Generation of Multi-AP Coordinated Spatial Reuse
Ziru Chen, Salvatore Talarico, Qing Xia +3
In next generation of Wi-Fi networks Multiple Access Point Coordination (MAPC) is poised to significantly enhance the network performance by enabling a set of Access Points (APs) t…
Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning
Chen Chen, Peiyuan Guan, Ziru Chen +3
Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and shortlived containers triggered by events, resulting in a reduction of mo…
Context-aware Container Orchestration in Serverless Edge Computing
Peiyuan Guan, Chen Chen, Ziru Chen +3
Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries o…
Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks
Ziru Chen, Zhou Ni, Peiyuan Guan +4
Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. Howe…