A Context Augmented Multi-Play Multi-Armed Bandit Algorithm for Fast Channel Allocation in Opportunistic Spectrum Access
arXiv:2605.25391v1 Announce Type: new Abstract: We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario. Most existing MP-MAB methods are impractical for real-world OSA systems as they assume many ideal conditions, incur a heavy computational cost, and most importantly, ignore the impact of channel noise which is directly related to the quality of service. In this study, we embody this impact by modeling channel noise as a perturbation of the arm's reward function in MP-MAB. As there is an implicit
This research addresses immediate challenges in applying AI to time-sensitive, resource-constrained opportunistic spectrum access, reflecting a broader push for practical, robust AI deployments in critical infrastructure.
Improving AI's ability to manage complex, dynamic radio spectrum will enhance resource efficiency and reliability for telecommunications, military, and emerging IoT networks.
The focus on realistic constraints like channel noise and computational cost in multi-armed bandit algorithms provides a pathway for AI to move beyond theoretical models into deployable solutions for spectrum management.
- · Telecommunications companies
- · Defense contractors
- · IoT device manufacturers
- · AI algorithm developers
- · Legacy spectrum management systems
- · Inefficient wireless communication protocols
More efficient and reliable wireless communication due to optimized channel allocation.
Reduced operational costs for network operators and improved service quality for end-users in congested spectrum environments.
Potential for new spectrum-dependent services and hardware innovations enabled by highly adaptive radio resource management.
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