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Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A q-learning Approach

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IEEE ACCESS 2025年 13卷 24323-24335页

作者： Solaiman, Suhare Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia

The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every service has distinct quality of service (qoS) requirements, it is essential to ensure the seamless coexistence of these devices. The main difficulty is allocating network resources to maximize spectrum utilization while meeting mMTC's massive connectivity demands and offering URLLC's demand for ultra-reliable, low-latency communication. In this study, non-orthogonal multiple access (NOMA) network slicing is utilized to share radio resources among various services, thereby improving connectivity for large-scale device deployments. When these services exist in high-density NOMA environments characterized by high network congestion with radio resource sharing, the level of difficulty increases significantly. To address these issues, an optimization algorithm is proposed for subchannel assignment and power allocation in NOMA high-density networks for URLLC and mMTC devices. The solution adopts a q-learning algorithm to optimize decision-making processes and ensure efficient resource sharing between URLLC and mMTC devices, while satisfying their distinct qoS requirements. Extensive simulations demonstrate that the proposed algorithm is flexible and scalable in dynamic scenarios, outperforming random and exhaustive search algorithms in high-density NOMA networks in terms of sum rates. The sum rate of the proposed algorithm increased by approximately 23.44% compared to that of the exhaustive search algorithm.

关键词： Ultra reliable low latency communication Network slicing Resource management NOMA Heuristic algorithms quality of service q-learning Interference Dynamic scheduling Uplink q-learning algorithm URLLC mMTC

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An improved particle swarm optimization using q-learning and tangent flight strategy for the combined cooling, heating, and power system

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ELECTRICAL ENGINEERING 2025年 1-18页

作者： Zhang, Zhaojun Luo, Hongjie Tan, Simeng Zou, Kuansheng Zhou, Shengwu Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Land Surveying & Mapping Inst Shandong Prov Jinan 250102 Shandong Peoples R China

In this paper, the storage battery and photovoltaic generator set are integrated into the combined cooling, heating, and power (CCHP) system to reduce its operating cost. Four scenarios with or without storage battery are investigated for the CCHP system. qTPSO that combines the q-learning algorithm and tangent flight strategy is proposed for the four CCHP scenarios in spring and winter. The q-learning algorithm enables the population to utilize information from previous iterations, adaptively selecting the optimal speed and position update formula based on the environment and population state. The tangent flight strategy expands the search space of the population and improves the global search capability of the algorithm. Experimental results indicate that the qTPSO outperforms four other algorithms, achieving the lowest operating cost among the four CCHP scenarios. Additionally, the CCHP system with battery exhibits a lower operating cost. In spring, the operating cost of the CCHP system with battery is 8.2% lower than that the system without battery. In winter, the operating costs for CCHP systems with battery is 8.42% lower than that the system without battery.

关键词： Combined cooling heating and power Storage battery q-learning algorithm Tangent flight strategy

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learning to select operators in meta-heuristics: An integration of q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期304卷 1296-1330页

作者： Karimi-Mamaghan, Maryam Mohammadi, Mehrdad Pasdeloup, Bastien Meyer, Patrick IMT Atlantique Lab STICC UMR CNRS 6285 F-29238 Brest France

This paper aims at integrating machine learning techniques into meta-heuristics for solving combinato-rial optimization problems. Specifically, our study develops a novel efficient iterated greedy algorithm based on reinforcement learning. The main novelty of the proposed algorithm is its new perturbation mechanism, which incorporates q-learning to select appropriate perturbation operators during the search process. Through an application to the permutation flowshop scheduling problem, comprehensive com-putational experiments are conducted on a wide range of benchmark instances to evaluate the perfor-mance of the proposed algorithm. This evaluation is done against non-learning versions of the iterated greedy algorithm and seven state-of-the-art algorithms from the literature. The experimental results and statistical analyses show the better performance of the proposed algorithm in terms of optimality gaps, convergence rate, and computational overhead.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Iterated greedy meta-heuristic Reinforcement learning q-learning algorithm Permutation flowshop scheduling problem

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Relay selection algorithm based on social network combined with q-learning for vehicle D2D communication

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IET COMMUNICATIONS 2019年第20期13卷 3582-3587页

作者： qian, Hongzhi Yu, Jinming Hua, Licheng Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Ningbo Univ Fac Mech Engn & Mech Ningbo 315211 Zhejiang Peoples R China

A relay selection algorithm was proposed to improve a communication rate of D2D (device-to-device) users in-vehicle networking communication systems based on social network combined with q-learning. The scheme was divided into two steps. Firstly, a social threshold was introduced to filter the potential relay nodes to reduce the number of probing times based on the user's interest similarity in D2D communication network. Then, an optimal relay selection algorithm was proposed to maximise the total rate of D2D links based on a q-learning algorithm. This method can provide the optimal relay selection scheme to meet the requirements of vehicle networking communication. The simulation results showed that the proposed scheme could reduce the number of probing relays and improve the communication speed of the system on the basis of ensuring communication security.

关键词： cooperative communication radio networks relay networks (telecommunication) telecommunication security social networking (online) social network vehicle D2D communication communication rate social threshold potential relay nodes user D2D communication network optimal relay selection algorithm q-learning algorithm optimal relay selection scheme vehicle networking communication probing relays communication speed communication security

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q-learning whale optimization algorithm for test suite generation with constraints support

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NEURAL COMPUTING & APPLICATIONS 2023年第34期35卷 24069-24090页

作者： Hassan, Ali Abdullah Abdullah, Salwani Zamli, Kamal Z. Razali, Rozilawati Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Comp Pekan 26600 Pahang Malaysia Univ Airlangga Fac Sci & Technol Campus JI Dr H Soekamo C Surabaya 60115 Indonesia

This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (qWOA-EMC). Unlike WOA, qWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed qWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms.

关键词： Combinatorial testing Constrained software testing Meta-heuristic Test case generation Whale optimization algorithm Reinforcement learning q-learning algorithm

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q-learning-based simulated annealing algorithm for constrained engineering design problems

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NEURAL COMPUTING & APPLICATIONS 2020年第9期32卷 5147-5161页

作者： Samma, Hussein Mohamad-Saleh, Junita Suandi, Shahrel Azmin Lahasan, Badr Univ Sains Malaysia Sch Elect & Elect Engn Intelligent Biometr Grp Engn Campus Nibong Tebal 14300 Penang Malaysia Univ Aden Fac Educ Shabwa Dept Comp Programming Aden Yemen

Simulated annealing (SA) was recognized as an effective local search optimizer, and it showed a great success in many real-world optimization problems. However, it has slow convergence rate and its performance is widely affected by the settings of its parameters, namely the annealing factor and the mutation rate. To mitigate these limitations, this study presents an enhanced optimizer that integrates q-learning algorithm with SA in a single optimization model, named qLSA. In particular, the q-learning algorithm is embedded into SA to enhance its performances by controlling its parameters adaptively at run time. The main characteristics of q-learning are that it applies reward/penalty technique to keep track of the best performing values of these parameters, i.e., annealing factor and the mutation rate. To evaluate the effectiveness of the proposed qLSA algorithm, a total of seven constrained engineering design problems were used in this study. The outcomes show that qLSA was able to report a mean fitness value of 1.33 on cantilever beam design, 263.60 on three-bar truss design, 1.72 on welded beam design, 5905.42 on pressure vessel design, 0.0126 on compression coil spring design, 0.25 on multiple disk clutch brake design, and 2994.47 on speed reducer design problem. Further analysis was conducted by comparing qLSA with the state-of-the-art population optimization algorithms including PSO, GWO, CLPSO, harmony, and ABC. The reported results show that qLSA significantly (i.e., 95% confidence level) outperforms other studied algorithms.

关键词： Simulated annealing q-learning algorithm Constrained engineering design problems

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The q-learning obstacle avoidance algorithm based on EKF-SLAM for NAO autonomous walking under unknown environments

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ROBOTICS AND AUTONOMOUS SYSTEMS 2015年 72卷 29-36页

作者： Wen, Shuhuan Chen, Xiao Ma, Chunli Lam, H. K. Hua, Shaoyang Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Peoples R China Kings Coll London Dept Informat London WC2R 2LS England

The two important problems of SLAM and Path planning are often addressed independently. However, both are essential to achieve successfully autonomous navigation. In this paper, we aim to integrate the two attributes for application on a humanoid robot. The SLAM problem is solved with the EKF-SLAM algorithm whereas the path planning problem is tackled via q-learning. The proposed algorithm is implemented on a NAO equipped with a laser head. In order to differentiate different landmarks at one observation, we applied clustering algorithm on laser sensor data. A Fractional Order PI controller (FOPI) is also designed to minimize the motion deviation inherent in during NAO's walking behavior. The algorithm is tested in an indoor environment to assess its performance. We suggest that the new design can be reliably used for autonomous walking in an unknown environment. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Humanoid robot q-learning algorithm EKF-SLAM Path planning Clustering algorithm Laser sensor Autonomous navigation FOPI controller Obstacle avoidance Motion deviation

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Green fourth-party logistics network design under carbon cap-and-trade policy

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 282卷

作者： Zhang, Yuxin Huang, Min Wu, Yaoxin Cao, Zhiguang Lin, Yuan Zhang, Jie Wang, Xingwei Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Eindhoven Univ Technol Dept Informat Syst NL-5600 MB Eindhoven North Brabant Netherlands Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Liaoning Peoples R China

As carbon emissions become a significant issue worldwide, sustainability has emerged as a driving force in fourth-party logistics (4PL) network design. To ensure service quality, transportation time cannot be ignored. Considering the carbon cap-and-trade policy, a novel mixed-integer non-linear programming model is proposed to design a green 4PL network under the service time constraint. An equivalent reformulation is proposed to obtain the optimal solution for small-scale problems. For larger-scale problems, the q-learning based dynamic memetic particle swarm optimisation algorithm is proposed to adaptively select suitable parameters and local search strategies for each individual. Numerical experimental results demonstrate the effectiveness and efficiency of proposed algorithm. Furthermore, the influence of 4PL, credit price, carbon cap, service time, and different carbon policies on the network are investigated. Compared with traditional third-party logistics, 4PL has more advantages in terms of cost, carbon emissions, and customer service. The credit price has a greater influence oncost and carbon emissions compared to the cap. Emissions sensitivity shifts from the purchased credit price under lower caps to the sell price under higher caps. The influence of the capon emissions is driven by the prices of purchased and sold credits. The appropriate maximum allowable service time is crucial for cost and carbon emissions. Overall, the carbon cap-and-trade policy is beneficial to company economics and sustainable development.

关键词： Green fourth-party logistics network design Service time Memetic particle swarm optimisation q-learning algorithm Carbon cap-and-trade

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Reliability-based reinforcement learning driven maintenance policy optimization

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年

作者： Tanhaeean, Mehrab Ghaderi, Seyed Farid Sheikhalishahi, Mohammad Khoobani, Mohammad Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Toronto Metropolitan Univ Fac Engn & Architectural Sci Toronto ON Canada

The study presents a reinforcement learning (RL) method called the q-learning algorithm to determine the best maintenance policy for equipment. This involves an artificial intelligence agent making decisions and an environment representing the equipment. The agent creates maintenance policies and takes actions, while the environment determines state transitions and rewards based on the actions chosen. The optimization of the maintenance policy starts with predicting equipment reliability using sensor data. This prediction method combines Back Propagation Neural Network (BPNN) algorithms with the Boxing Match algorithm (BMA), an evolutionary meta-heuristic algorithm. A novel weight update strategy enhances the performance of artificial neural networks in reliability prediction. This integrated model, BMA-BPNN, aims to improve the accuracy of forecasted reliability. The study involves forecasting equipment reliability to determine critical levels, incorporating cost and risk considerations into decision-making for optimizing maintenance policies. As a result, the q-learning algorithm is used to identify the best maintenance actions based on equipment reliability. Implementing an automated maintenance system that considers equipment reliability and costs can help reduce accidents resulting from maintenance program deficiencies. This study thus contributes to the field by providing an efficient approach to equipment maintenance.

关键词： Boxing match algorithm maintenance optimization Markov decision process metaheuristic algorithm q-learning algorithm real time data reinforcement learning

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Inverse q-learning Optimal Control for Takagi-Sugeno Fuzzy Systems

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IEEE Transactions on Fuzzy Systems 2025年

作者： Song, Wenting Ning, Jun Tong, Shaocheng Liaoning University of Technology College of Science Jinzhou121000 China Dalian Maritime University Navigation College Dalian116026 China

Inverse reinforcement learning optimal control is under the framework of learner-expert, the learner system can learn expert system's trajectory and optimal control policy via a reinforcement learning algorithm and does not need the predefined cost function, so it can solve optimal control problem effectively. This paper develops a fuzzy inverse reinforcement learning optimal control scheme with inverse reinforcement learning algorithm for Takagi-Sugeno (T-S) fuzzy systems with disturbances. Since the controlled fuzzy systems (learner systems) desire to learn or imitate expert system's behavior trajectories, a learner-expert structure is established, where the learner only know the expert system's optimal control policy. To reconstruct expert system's cost function, we develop a model-free inverse q-learning algorithm that consists of two learning stages: an inner q-learning iteration loop and an outer inverse optimal iteration loop. The inner loop aims to find fuzzy optimal control policy and the worst-case disturbance input via learner system's cost function by employing zero-sum differential game theory. The outer one is to update learner system's state-penalty weight via only observing expert systems' optimal control policy. The model-free algorithm does not require that the controlled system dynamics are known. It is proved that the designed algorithm is convergent and also the developed inverse reinforcement learning optimal control policy can ensure T-S fuzzy learner system to obtain Nash equilibrium solution. Finally, we apply the presented fuzzy inverse q-learning optimal control method to nonlinear unmanned surface vehicle system and the computer simulation results verified the effectiveness of the developed scheme. © 2025 IEEE.

关键词： fuzzy inverse reinforcement learning optimal control q-learning algorithm Takagi-Sugeno (T-S) fuzzy systems zero-sum differential game

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