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 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.
A.Mohan Kumar, M. Al-Shalout, M. Elakiya et al.· International Conference on...· 0 citations
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