Energy Aware Optimal Resource Allocation in Backhaul Constraint Wireless Networks: A Two Base Stations Scenario by Yuan Gao, Peng Xue, Yi Li, Hongyi Yu, Xianfeng Wang, Shihai Gao

“…Simulations show that this allocation algorithm can improve the system capacity and energy efficiency significantly compared with the blind alternatives.…”
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Energy-Efficient Multi-Agent Deep Reinforcement Learning Task Offloading and Resource Allocation for UAV Edge Computing by Shu Xu, Qingjie Liu, Chengye Gong, Xupeng Wen

“…This paper proposes a novel multi-agent reinforcement learning framework, termed Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation (MATD3-TORA), to optimize task offloading and resource allocation in UAV-assisted MEC networks. …”
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Power-Efficient UAV Positioning and Resource Allocation in UAV-Assisted Wireless Networks for Video Streaming with Fairness Consideration by Zaheer Ahmed, Ayaz Ahmad, Muhammad Altaf, Mohammed Ahmed Hassan

“…The joint optimization includes power minimization, efficient resource allocation, i.e., transmit power and bandwidth, and efficient two-dimensional positioning of the UAV while meeting system constraints. …”
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Network Resource Allocation Method Based on Awareness–Prediction Joint Compensation for Low-Earth-Orbit Satellite Networks by Hang Di, Tao Dong, Zhihui Liu, Shuotong Wei, Qiwei Zhang, Dingyun Zhang

“…Furthermore, an efficient, accelerated alternating-direction method of multipliers (ADMM) resource allocation algorithm is proposed with the aim of maximizing the satisfaction of service resources requirements. …”
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GLOPS: A Hybrid Approach for Enhanced Scheduling in Cloud Computing Environments via Machine Learning-Based Process Prediction by Syed Abu Ammar Muhammad Zarif, Layla Kader Suma, Md. Sadat Rahman, Md. Motaharul Islam, Phuoc Hung Pham, Phong Dung Thi Nguyen, Nguyen Thanh Binh

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SPYDER: QoS-Aware Radio Resource Allocation in Multiuser ISAC-Capable C-V2X Networks by Syed Najaf Haider Shah, Aamir Ullah Khan, Christian Schneider, Joerg Robert

“…To counteract this, we employ sparse reconstruction algorithms within the compressed sensing framework, enhancing flexibility in TF resource allocation and providing high-resolution radar sensing despite uncoordinated resource selection. …”
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Mode selection and resource optimization for UAV-assisted cellular networks by Daquan FENG, Canjian ZHENG, Xiangqi KONG

“…The resource allocation and optimization scheme was studied in a coexistence scenario of unmanned aerial vehicle (UAV) and cellular communication network.To improve spectrum efficiency of the system, UAV users could reuse the cellular spectrum resources to access the network through full duplex or half duplex device-to-device technique.Additionally, a joint access control, mode selection, power control and resource allocation optimization problem was formulated to maximize the overall throughput of the network while ensuring quality of service requirements for both UAV users and ground cellular users.Specifically, the phase 1 method in the convex optimization was adopted for access control and feasibility check, and then the convex and concave procedure (CCCP) iterative algorithm was used to solve the power control problem for feasible UAV user pairs.By using this local optimum value, the original optimization problem can be simplified into a weighted maximization problem.Finally, the Kuhn-Munkres (KM) algorithm was used to match the optimal channel resources and obtain the global optimal throughput value of the system.Numerical results show that the proposed scheme can significantly improve the performance of system.…”
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Energy-Aware Task Allocation for Multi-Cloud Networks by Sambit Kumar Mishra, Sonali Mishra, Ahmed Alsayat, N Z Jhanjhi, Mamoona Humayun, Kshira Sagar Sahoo, Ashish Kr. Luhach

“…This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. …”
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Dual time scale network slicing algorithm based on D3QN for B5G multi-service scenarios by Geng CHEN, Shuhu QI, Fei SHEN, Qingtian ZENG

“…To effectively meet the differentiated quality of service (QoS) requirements of different slices, a dual time scale network slicing resource allocation algorithm based on dueling double DQN (D3QN) was proposed for B5G multi-service scenarios.The joint resource slicing and scheduling problem was formulated, with the weighted sum of the normalized spectral efficiency (SE) and the QoS of users indices of different slices as the optimization objective.On large time scale, the SDN controller used the D3QN algorithm to pre-allocate resources to different slices based on the resource requirements of each service, and then performed BS-level resource updating based on the load status of BS.On small time scale, the BS scheduled resources to end-users by using the round-robin scheduling algorithm.The simulation results show that the proposed algorithm has excellent performance in ensuring the QoS requirements of slice users, SE and system utility.Compared with the other 4 baseline algorithms, the system utility is improved by 3.22%, 3.81%, 7.48% and 21.14%, respectively.…”
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Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks by Apostolos Avranas, Philippe Ciblat, Marios Kountouris

“…Our method can, for instance, achieve with 13% less power and bandwidth resources the same user satisfaction rate as a myopic algorithm using knapsack optimization.…”
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A survey on AI algorithms applied in communication and computation in Internet of vehicles by Yu KANG, Yaqiong LIU, Tongyu ZHAO, Guochu SHOU

Subjects: “…communication resource allocation;communication security;computation offloading;communication-computation integration;AI algorithm;deep reinforcement learning…”
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A survey on AI algorithms applied in communication and computation in Internet of vehicles by Yu KANG, Yaqiong LIU, Tongyu ZHAO, Guochu SHOU

Subjects: “…communication resource allocation…”
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Optimal allocation of urban land space based on NSGA2 by Yi Guo, Chaoqin Bai, Peiwen Zhao

“…Meanwhile, the reverse iteration distance value of this algorithm was only 4.14%, which was 22.76% lower than the adaptive weighted genetic algorithm. …”
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Enterprise fission path optimization and dynamic capability construction based on the soft actor-critic algorithm by Hengsheng Gu

“…The experimental results show that the fission path optimized by deep reinforcement learning (DRL) markedly improves the resource allocation efficiency and market response speed by an average of 20.4% and 25.2%, respectively. …”
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Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice by Guolin SUN, Ruijie OU, Guisong LIU

“…Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.…”
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Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm by Roghayeh Mirataollahi olya, Seyed Ahmad Shayannia, Mohammad Mehdi movahedi

“…In this research, considering the high importance of these issues, the problems of scheduling and allocation of manpower in a real place are solved. To this end, the metaheuristic Tabu search algorithm is used with the aim of minimizing the duration of activity and the presence of all manpower. …”
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A FPGA Accelerator of Distributed A3C Algorithm with Optimal Resource Deployment by Fen Ge, Guohui Zhang, Ziyu Li, Fang Zhou

“…In addition, the resource wastage problem caused by the distributed training characteristics of A3C algorithms and the resource allocation problem affected by the imbalance between the computational amount of inference and training need to be carefully considered when designing accelerators. …”
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Educational resources recommendation algorithm based on GMF-MLP-NeuMF prediction model by Rui Wang, Qiusheng Cui, Yezhi Xiao, Jinfeng Du, Ruicheng Yue

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Algorithmic Approach of Majority Voting With Agents’ Inclusiveness for Facility Resource Matching by Peash Ranjan Saha, Salimur Choudhury, Kai Salomaa

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Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing by Bin LI, Sicong PENG, Zesong FEI

“…To address the dependence of traditional integrated sensing and communication network mode on ground infrastructure, the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption, signal blocking, and coverage blind spots in complex scenarios.Firstly, under the conditions of satisfying the user’s transmission power, radar estimation information rate and task offloading proportion limit, the system energy consumption was minimized by jointly optimizing UAV radar beamforming, computing resource allocation, task offloading, user transmission power, and UAV flight trajectory.Secondly, the non-convex optimization problem was reformulated as a Markov decision process, and the proximal policy optimization method based deep reinforcement learning was used to achieve the optimal solution.Simulation results show that the proposed algorithm has a faster training speed and can reduce the system energy consumption effectively while satisfying the sensing and computing delay requirements.…”
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