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                <text>Faculty Publications</text>
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              <text>Rao, M Nagabhushana; Paulin, M Sudha; Varghese, Juvitha; Rekha, N.; Rekha, M.D.; Gogineni, Saikiran</text>
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              <text>Quantum-Assisted Metaheuristics for Adaptive Resource Allocation in 6G Networks</text>
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              <text>01-01-2025</text>
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              <text>2025 International Conference on Future Technologies, ICFT 2025;</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICFT66708.2025.11336371" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICFT66708.2025.11336371&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105033685667?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105033685667?origin=resultslist&lt;/a&gt;</text>
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              <text>Rao M.N., School of Computer Science &amp;amp; Technology, Malla Reddy (MR) Deemed to Be University, Dept. of CSE, Telangana, India; Paulin M.S., School of Business and Management, Christ University, Bangalore, India; Varghese J., School of Management, Kristu Jayanti College (Autonomous), Bangalore, India; Rekha N., School of Commerce, Finance and Accountancy, Christ (Deemed to Be) University, Bangalore, India; Rekha M.D., Department of Commerce, Christ (Deemed to Be University), Bangalore, India; Gogineni S., School of Computer Science &amp;amp; Engineering, Malla Reddy (MR) Deemed to Be University, Dept. of CSE, Telangana, India</text>
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              <text>With 6G wireless communication systems, the level of demands is now ultra-low-latency, connectivity of devices in large numbers, and flexible spectrum utilization. To resolve these issues, in the current paper, the Quantum-Assisted Metaheuristic (QAM) framework is proposed that combines quantum-inspired operators and traditional metaheuristic methods of adaptive resource allocation in 6G networks. This framework uses quantum-enhanced exploration, dynamically tuned parameters and hybrid quantum-classical computing to trade-off scalability against efficiency, depending on the traffic and channel conditions. Oh! SIM Evaluating a simulated 6G environment, it proves that QAM can be used to improve spectral efficiency by up to 28 percent and the allocation latency by 31 percent over the state-of-the-art metaheuristics, and still treats users of varying densities fairly with a fairness index of 0.94. The results demonstrate the strength and extensiveness of the QAM, and makes it a viable solution to the efficient and intelligent management of resources in next generation wireless networks.  2025 IEEE.</text>
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              <text>6G networks; adaptive resource allocation; low latency; metaheuristics; network optimization; quantum computing; spectrum management</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>ISBN: 979-833156815-3;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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