Search Results for author: Fu-Chun Zheng

Found 7 papers, 0 papers with code

Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC

no code implementations25 May 2023 Kang Li, Pengcheng Zhu, Yan Wang, Fu-Chun Zheng, Xiaohu You

With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements.

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

no code implementations11 Jul 2022 Yuhong Liu, Changyang She, Yi Zhong, Wibowo Hardjawana, Fu-Chun Zheng, Branka Vucetic

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks.

Graph Neural Network

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks

no code implementations23 Jun 2022 Yunwei Tao, Yanxiang Jiang, Fu-Chun Zheng, Pengcheng Zhu, Dusit Niyato, Xiaohu You

To utilize the computing resources of other fog access points (F-APs) and to reduce the communications overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning.

Federated Learning

Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach

no code implementations13 Jun 2022 Zhiheng Wang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You

Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users.

Federated Learning

Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

no code implementations13 Jun 2022 Lingling Zhang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You

In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs.

Federated Learning Reinforcement Learning (RL)

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