Search Results for author:
Found 7 papers, 0 papers with code
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks
In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated.
Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach
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.
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach
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.
Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks
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.
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks
In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs).
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
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.
Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC
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.
