Resource Allocation of Multi-Satellite Beam Hopping Technology Based on System Capacity Maximization by Qing SHU, Xiaoning ZHANG, Zesong FEI

“…Unlike medium and high earth orbit satellites, LEO satellites have the characteristics of small path loss, low propagation delay and high deployment fl exibility.LEO satellites are widely used in real time services, global internet of things, emergency communication and other fi elds.In order to solved the problems of large diff erences in service demand distribution of terrestrial users and low resource utilization in LEO satellite communication systems, a resource allocation algorithm of LEO multi-satellite beam hopping technology based on convex optimization was proposed, which aimed to maximized the system capacity.For multisatellite beam hopping scenarios in LEO satellite communication system, the beam-hopping technology could fl exibly allocated system resources accorded to user needs.Considered the factor of co-channel interference, the service region division scheme based on load balancing and the inter-satellite resource allocation priority scheme was proposed.In order to improved system capacity and resource utilization, it jointly designed a time and power resource allocation algorithm based on convex optimization.The simulation results showed that the proposed scheme could eff ectively reduced co-channel interference and realized the ondemand allocation of system capacity without causing waste of resources.…”
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Joint scheduling and resource allocation based on reinforcement learning in integrated access and backhaul networks by Joeun Kim, Youngil Jeon, Junhwan Lee, Moon-Sik Lee, Taesoo Kwon

“…Because of the inherent nature of IAB, access and backhaul links share the same resource pool causing cross-link interference. To address this challenge, this paper investigates an algorithm based on reinforcement learning (RL) for joint scheduling and resource allocation (RA) problem, aiming to mitigate interference and enhance user data rates. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. …”
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Robust QoT Assured Resource Allocation in Shared Backup Path Protection Based EONs by Venkatesh Chebolu, Sadananda Behera, Goutam Das

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Opportunistic spectrum sharing protocol and optimal resource allocation based on OFDM cooperation relaying by Wei-dang LU, Xuan-li WU, Xue-jun SHA, Nai-tong ZHANG

“…An opportunistic spectrum sharing protocol based on OFDM cooperation relaying was proposed.When the primary user experiences weak channel conditions,it could not achieve its target rate.If the cognitive user could help the primary user achieve its target rate,it accessed the pectrum of the primary user in the cooperation way.In this access way,the cognitive user used a fraction of the subcarriers to amplify-and-forward the signal of the primary user,to help the primary user achieve its target rate.And uses the remained subcarriers to transmit its own signal.The resource allocation was analyzed in this cooperation way.An optimal resource allocation algorithm based on dual ethod was proposed,which maximized the rate of the cognitive user while g anteeing the primary user achieve its target rate.If the cognitive user could not help the primary user achieve its target rate,to make use the spectrum efficiently,the cognitive user accessed the spectrum of the primary user in the non-cooperation way,and uses the whole accessed spectrum to transmit its own signal.The simulation demonstrates the efficiency of the proposed spectrum sharing protocol as well as its benefit to both primary and cognitive user.…”
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Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO by Qusay Alghazali, Husam Al-Amaireh, Tibor Cinkler

“…This paper introduces a comprehensive framework for reducing energy consumption in MEC environments by leveraging advanced optimization techniques and energy-efficient resource allocation algorithms. We propose a novel approach that dynamically adjusts the computational resources based on the current network load and the type of services requested, thus minimizing unnecessary energy consumption. …”
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Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk by Changxiao Zhao, Wei Zhang, Fangzheng Dong, Jun Dai, Lei Dong

“…The resource allocation method is evaluated according to the fault propagation risk model, and a heuristic algorithm is applied to optimize the resource allocation method of the IMA system. …”
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Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce by Yi Xie, Heng-Qing Ye, Wenbin Zhu

“…In cross-border e-commerce, effective marketing resource allocation is crucial due to the complexity introduced by diverse product categories, regional differences, and competition among category managers. …”
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Game Theory Based Energy-Aware Uplink Resource Allocation in OFDMA Femtocell Networks by Jun Zhao, Wei Zheng, Xiangming Wen, Xiaoli Chu, Haijun Zhang, Zhaoming Lu

“…Simulation results show that the proposed algorithm can improve user utilities significantly, compared with existing power control and subchannel allocation algorithms.…”
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Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…This paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. The study uses real-world data from 51 public hospitals across Southern and Northern Italy, ensuring a representative sample of diverse healthcare structures. …”
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RL-MAB-Based Resource Allocation for Efficient Bandwidth Utilization in Industrial IoT Networks by Dongyeong Seo, Kidong Nam, Kwansoo Jung

“…The experimental performance evaluation shows that the proposed scheme improves network performance in terms of resource efficiency, scalability, energy efficiency, and mobility support compared to the typical resource allocation and scheduling schemes.…”
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Energy-efficient device selection and resource allocation for edge-driven hierarchical federated learning by Yongwen Liu, Dongqing Liu, Lyes Khoukhi, Bo Wang, Lei Zhang, Yaoli Xu

“…However, existing frameworks often struggle with energy efficiency, adaptability to dynamic conditions, and the complex interplay between device selection and resource allocation. In this paper, we propose an energy-efficient hierarchical federated learning framework that jointly optimizes IoT device selection, local resource allocation, and base station resource allocation across a three-layer architecture. …”
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Distributed RL-Based Resource Allocation and Task Offloading for Vehicular Edge of Things Computing by Ghada Afifi, Bassem Mokhtar

“…However, VEoTC frameworks face several challenges in effectively localizing and allocating the distributed resources and offloading tasks successfully due to the high mobility of vehicles and fluctuating user densities. …”
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Cost Minimization with Project Crashing: Comparison of the Traditional Solution and Genetic Algorithm Approach by Sadik Yıgıt, Semih Caglayan

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Research on the Capacity Allocation Optimization of Island Micro-grid Including Wind Power, Tidal Current Energy and Energy Storage by PANG Jiameng, ZANG Xiaodi

“…The three-objective optimization model uses the multi-objective particle swarm optimization algorithm to solve the established model, and obtains different capacity allocation schemes. …”
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Decentralized and Joint Resource Allocation, Beamforming, and Beamcombining for 5G Networks With Heterogeneous MARL by Ala'a Al-Habashna, Jon Menard, Gabriel Wainer, Gary Boudreau

“…Our results show that the proposed paradigm can successfully optimize the task of joint resource allocation, beamforming, and combining. Furthermore, we provide a comparative analysis of the three different implementations, highlighting noteworthy insights into the strengths and limitations of fully distributed algorithms, such as IQL, in comparison to algorithms employing the Centralized Training with Decentralized Execution (CTDE) framework, exemplified by QTRAN and MADDPG.…”
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Joint Optimization of UAV Placement and Resource Allocation in FDMA Wireless-Powered Sensor Networks by Omid Abachian Ghasemi, Mehdi Chehel Amirani

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Dynamic Security-Aware Resource Allocation in Quantum Key Distribution-Enabled Optical Networks by Vimal Bhatia, Adolph Kasegenya, Bowen Chen

“…This research addresses the issues of high blocking probabilities (BPs) and the proper utilization of quantum resources in varying network loads by introducing a novel heuristic approach, termed dynamic security-aware quantum resource allocation (D-SQRA), designed for dynamic resource allocation in QKD-enabled optical networks. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…Two novel scheduling algorithms, Dynamic Renewable Energy Allocation (DREA) and Energy-Aware Priority Scheduling (EAPS), enhance energy efficiency. …”
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Energy-saving computation offloading scheme based on Sarsa algorithm in industrial internet of things by Jun SUN, Shangweikang ZHAO

“…In order to reduce the total energy consumption of industrial internet of things systems with deadline requirements, a computing offloading scheme based on Sarsa algorithm was proposed.Based on the characteristics of Sarsa algorithm, the energy consumption optimization problem was coupled.The Sarsa algorithm iterated the external state value function, selected the appropriate edge computing server to offload the computing task, and then the particle swarm optimization algorithm solved the resource allocation problem.Finally, the environmental information was updated for the next round of algorithm iteration until the algorithm met the end conditions.The simulation results show that this scheme has faster convergence speed and effectively reduces the system energy consumption.…”
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