Optimizing Cognitive Radio Networks for Spectrum Efficiency, Power Consumption, and Human Exposure
A novel cloud-based architecture and multi-objective optimization algorithm for cognitive radio networks that reduces power consumption by 27.5%, global exposure by 34.3%, and spectrum usage by 34.5% compared to traditional cognitive radio networks.
Optimizing TV White Space and LTE Networks for Energy Efficiency in Suburban and Rural Scenarios
TVWS networks have an energy efficiency 9-12 times higher than LTE networks in suburban and rural scenarios.
Stability Analysis and Coverage Extension Limits of Interacting Wireless Repeater Networks
The maximum amplification gain that wireless repeaters can use without causing destructive positive feedback is restricted by the sum of inter-repeater channel amplitude gains, rather than the sum of path losses.
5G NR V2X 사이드링크에서 충돌 완화를 위한 협력적 자원 할당 연구
5G NR V2X 사이드링크 통신에서 숨겨진 노드 문제로 인한 패킷 충돌을 완화하기 위해 노드 간 자원 할당 정보 공유를 통한 협력적 자원 할당 방법을 제안한다.
Federated Learning with Dynamically Adjusted Learning Rates for Resource-Constrained Wireless Networks
The proposed FLARE framework allows participating devices to dynamically adjust their individual learning rates and local training iterations based on their instantaneous computing powers, mitigating the impact of device and data heterogeneity in wireless federated learning.
Optimal Heterogeneous Client Sampling for Federated Learning over Wireless Networks
The authors design an adaptive client sampling algorithm for federated learning over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
LR-FHSS-Sim: An Open-Source Discrete-Event Simulator for Evaluating LR-FHSS Networks
LR-FHSS-Sim is an open-source discrete-event simulator written in Python that enables flexible and extensible modeling of LR-FHSS networks for various research and development purposes.
Optimal Error Bounds for Classification and Regression of Inhomogeneous Data in Wireless Networks
Under ergodic-type conditions on the data distributions, optimal error bounds can be achieved for classification and regression tasks using universal rules, with applications in wireless networks.
Fine-Grained Performance Analysis of Joint Communication and Sensing Networks Using the SIR Meta Distribution
The meta distribution of the signal-to-interference ratio (SIR) provides a fine-grained quantification of individual user or radar performance in joint communication and sensing (JCAS) wireless networks.
Optimizing Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks
The core message of this paper is to propose an adaptive decentralized federated learning (DFL) framework that optimizes the number of local training rounds across diverse devices with varying resource budgets, in order to enhance the model performance while considering energy and latency constraints.
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