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Found 12 papers, 0 papers with code
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
To tackle this problem, Split Federated Learning is utilized, where clients upload their intermediate model training outcomes to a cloud server for collaborative server-client model training.
Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN).
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design
Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse.
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing
This theoretical bound is decomposed into two components, including the variance of the global gradient and the quadratic bias that can be minimized by optimizing the communication resources, and quantization/noise parameters.
Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Prediction
For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands.
FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems
data, users' heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud.
Hybrid Relay-Reflecting Intelligent Surface-Aided Wireless Communications: Opportunities, Challenges, and Future Perspectives
In this paper, we provide an overview of a hybrid relay-reflecting intelligent surface (HR-RIS) architecture, in which only a few elements are active and connected to power amplifiers and radio frequency chains.
Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium.
Information Theory Information Theory
Energy Efficiency Maximization in RIS-Aided Cell-Free Network with Limited Backhaul
Integrating the reconfigurable intelligent surface in a cell-free (RIS-CF) network is an effective solution to improve the capacity and coverage of future wireless systems with low cost and power consumption.
Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks
To address this problem, we develop two efficient unsupervised machine learning (ML) based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system.
UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device's requirement and the UAV's limited storage capacity.
A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing
This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multipleoutput (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources.
