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Resource allotment in Ad Hoc networks is a difficult proposition due to the dynamic and decentralized nature of the networks. Conventional procedures often stumble to adjust to varying network conditions, ushering in suboptimal performance. This article presents a novel strategy for resource allotment in Ad Hoc networks by integrating regulations from Ad Hoc Networking, Multi-Arm Bandit algorithms, and quantum computing principles. An all-around mathematical outline of the proposed Quantum-enhanced Multi-Arm Bandit (Q-MAB) approach, including problem formulation/system model, algorithm creation, theoretical investigation, and simulation outcomes, is presented. Theoretical investigations backed by propositions, proofs, and simulation outcomes reveal the efficacy of the Q-MAB algorithm compared to the traditional MAB allotment procedure in optimizing resource allotment.

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Q-MAB: Resource Allotment in Ad Hoc Networks Utilizing Quantum-Enhanced Multi-arm Bandit Resource Allotment Procedure

  • 会议论文

  • 发表日期：

    2025年

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  • Sapthak Mohajon Turjya,
  • Raj Singh,
  • Pritam Sarkar,
  • Anjan Bandyopadhyay,
  • Sujata Swain,
  • Siddhartha Bhattacharyya,
  • Jan Platos,
  • Vaclav Snasel

摘要

Resource allotment in Ad Hoc networks is a difficult proposition due to the dynamic and decentralized nature of the networks. Conventional procedures often stumble to adjust to varying network conditions, ushering in suboptimal performance. This article presents a novel strategy for resource allotment in Ad Hoc networks by integrating regulations from Ad Hoc Networking, Multi-Arm Bandit algorithms, and quantum computing principles. An all-around mathematical outline of the proposed Quantum-enhanced Multi-Arm Bandit (Q-MAB) approach, including problem formulation/system model, algorithm creation, theoretical investigation, and simulation outcomes, is presented. Theoretical investigations backed by propositions, proofs, and simulation outcomes reveal the efficacy of the Q-MAB algorithm compared to the traditional MAB allotment procedure in optimizing resource allotment.