Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 920-925· 0 citations· 17 references
Abstract
The high rate of Internet of Things (IoT) networks development has posed a serious problem of effective resource allocation because devices are heterogeneous, the traffic conditions are dynamic, and the energy and latency requirements are severe. Traditional resource allocation methods and classical reinforcement learning methods are not always the best methods to perform in a highly dynamic environment because they lack the adaptability and reduce convergence. The proposed paper introduces a Quantum Reinforcement Learning (QRL)-motivated adaptive resource allocation model which uses quantum-inspired state representation, as well as quantum-classical policy optimization, to optimize resource allocation. The proposed model is a dynamic distribution of bandwidth, transmission power, and computational resources the real-time state of network. Experimental assessment shows better performance than the traditional and classical reinforcement learning techniques. The proposed QRL framework attains the average accuracy of resource allocation 97.84%, lessens the communication latency by 43%, escalates the throughput by 64%, and lessens the energy usage by 37%. The system also has better convergence and stability exception when applied to different network load. Combination of quantum feature encoding improves efficacy in decision and learning. The findings affirm that the suggested framework offers a powerful and scalable system of smart resource management in the next-generation IoT systems.
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
The Sixth Generation (6G) wireless networks are projected to enable ultra-dense connectivity, very high rates of data transmission, and dynamic use of spectrum and need smart and dynamic spectrum assignment. The classic spectrum allocation and classical reinforcement learning algorithms are not as effective as the complexity, scalability, and instant adaptability of 6 G space. To overcome these issues, this paper suggests a Quantum Reinforcement Learning Assisted Spectrum Allocation Framework that will combine quantum computing concepts and reinforcement learning to manage spectrum efficiently. The suggested model represents the network state in quantized form and then optimizes the spectrum allocation decision in real time with the help of a variational quantum circuit. The framework constantly communicates with the network environment and advances allegation policies on the foundation of reward feedback. It is shown in results of experiments that the proposed model has a spectrum allocation accuracy of 96.35%, spectral efficiency of $9.92 \mathrm{bps} / \mathrm{Hz}$ and throughput of $\mathbf{3 1 9 . 8 ~ M b p s}$ when the network is loaded to capacity. Also, the suggested method minimizes the latency to 21.6 ms and reduces the interference to a significant level in comparison with traditional methods. These findings verify that the developed quantum reinforcement learning model offers smart, adaptive, and efficient spectrum allocation that can be used in future 6G wireless communication systems.
K. Kannan, M. Al-Shalout, T.S. Valarmathi et al.· International Conference on...· 0 citations
The impending deployment of autonomous vehicles has added new requirements about wireless communication in highly dynamic vehicular networks, particularly in terms of reliability and low latency. The allocation of the spectrum is still a difficult problem, as traffic and mobility conditions change continuously, which implies the variation in vehicle density, channel quality, interference, and communication requirements. These variations are not easily handled by the conventional static, rule-based and non-adaptive allocation schemes, affecting the efficient use of the spectrum, communication latency, and packet delivery reliability. To overcome these challenges, this study introduces an Adaptive Deep Reinforcement Learning (ADRL) based dynamic spectrum allocation framework for AVNs. The proposed framework uses a Deep Q-Network (DQN) that, based on observation of the current conditions of the vehicular network (vehicle density, channel quality, level of interference, load of the traffic flow), automatically determines the appropriate spectrum resources. A simulation-based vehicular communication scenario is created to evaluate under various traffic densities and channel-quality levels with dynamic mobility and channel conditions. Experimental analysis takes into account the following factors: spectral efficiency, spectrum utilization, communication latency, packet delivery ratio, reliability, and learning convergence. Results show that the proposed ADRL framework can ensure efficient spectrum allocation and reliable communication in a fast-growing network density and degraded channel environment and has stable convergence characteristics in its training behavior. The results show that adaptive deep reinforcement learning is an effective method to spectrum management in the next generation of autonomous vehicular communication networks that is robust, resource-efficient, and has low latency.
Results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.
L. Hoang, Van-Tam Hoang, Huu-Huy Ngo· International journal of Com...· 1 citation
Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Haoyu Gu· Scientific Journal of Intell...· 0 citations
Digital Twin-Based Low-Energy Reinforcement Learning for Multi-Cell IoV (DT-LERL) distributed collaborative training architecture in cellular-based IoV scenarios is designed, which allows the twin to replace the end-side vehicular entities by introducing digital twins to carry out scenario interactions and model training.
A. Alamoudi, Abdullah S. Almansouri· Journal of Big Data· 0 citations
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