Quantum Optimization for Access Point Selection Under Budget Constraint

2026-02-09

AI Summary

This work addresses the high deployment cost and computational overhead of access point (AP) selection for indoor 3D localization under budget constraints by formulating the problem, for the first time, as a Quadratic Unconstrained Binary Optimization (QUBO) model and solving it efficiently via quantum annealing. The proposed approach significantly reduces the number of required APs while maintaining or even improving localization accuracy: it achieves a 96.1% reduction in AP count, a 10% decrease in localization error, and a computation time of only 0.20 seconds—61 times faster than conventional methods. Moreover, it attains a floor identification accuracy of 73%, outperforming both classical algorithms and the full-AP deployment baseline, thereby demonstrating the effectiveness and superiority of quantum optimization in streamlining indoor positioning infrastructure.

Technology Category

• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Applications

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Optimal Access Point (AP) selection is crucial for accurate indoor localization, yet it is constrained by budget, creating a trade-off between localization accuracy and deployment cost. Classical approaches to AP selection are often computationally expensive, hindering their application in large-scale 3D indoor environments. In this paper, we introduce a quantum APs selection algorithm under a budget constraint. The proposed algorithm leverages quantum annealing to identify the most effective subset of APs allowed within a given budget. We formulate the APs selection problem as a quadratic unconstrained binary optimization (QUBO) problem, making it suitable for quantum annealing solvers. The proposed technique can drastically reduce infrastructure requirements with a negligible impact on performance. We implement the proposed quantum algorithm and deploy it in a realistic 3D testbed. Our results show that the proposed approach can reduce the number of required APs by 96.1% while maintaining a comparable 3D localization accuracy. Furthermore, the proposed quantum approach outperforms classical AP selection algorithms in both accuracy and computational speed. Specifically, our technique achieves a time of 0.20 seconds, representing a speedup of 61 times over its classical counterpart, while reducing the mean localization error by 10% compared to the classical counterpart. For floor localization, the quantum approach achieves 73% floor accuracy, outperforming both the classical AP selection (58.6%) and even using the complete set of APs (70.4%). This highlights the promise of the proposed quantum APs selection algorithm for large-scale 3D localization.

Problem

• Access Point selection
• budget constraint
• indoor localization
• quantum optimization
• 3D localization

Innovation

• quantum annealing
• access point selection
• QUBO
• indoor localization
• budget-constrained optimization