[论文解读] Quantum Optimization for Access Point Selection Under Budget Constraint

Mohamed Khalil Brik, Ahmed Shokry+1 more|arXiv (Cornell University)|Feb 9, 2026

Indoor and Outdoor Localization Technologies被引用 0

一句话总结

论文提出了一种基于量子退火的预算约束下的接入点（AP）选择方法，将其表述为一个QUBO，在保持3D定位精度可比的前提下实现了对所需AP数量的96.1%减少，并比经典方法实现61倍加速。

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.

研究动机与目标

• 推动室内3D定位并强调降低AP部署成本的必要性。
• 开发一个在预算约束下保持定位精度的AP选择框架。
• 利用量子退火解决AP选择问题的QUBO形式。
• 将量子AP选择与经典基线在精度与计算时间上进行对比。

提出的方法

• 将AP选择表述为带预算约束的二次无约束二进制优化（QUBO）。
• 使用二进制变量x_i表示是否选择AP，约束为sum x_i = k。
• 定义一个将AP重要性I_i和冗余R_ij结合起来的目标函数，带有平衡参数alpha。
• 通过惩罚项eta (sum x_i - k)^2来强制预算约束。
• 探索四种AP重要性度量（Entropy、Variance、AVG、MAX）来加权I_i。
• 使用RSS向量之间的绝对Pearson相关性来计算冗余。
• 使用量子退火模拟器(OpenJij)求解QUBO，并与经典SA进行比较。

Figure 1 : Example of WiFi fingerprinting location tracking process with four APs.

实验结果

研究问题

• RQ1 预算受限的AP选择问题是否能够有效地表述为适用于量子退火的QUBO？
• RQ2 在预算有限的情境中，哪种AP重要性度量最能预测定位性能？
• RQ3 量子AP选择在3D定位的精度和速度上与经典优化相比如何？
• RQ4 QUBO参数(alpha、eta)与退火设置对解质量有何影响？

主要发现

• 量子AP选择在保持可比的3D定位精度的同时实现了所需AP数量的96.1%减少。
• 平均3D定位误差在QA为11.7–11.58 m，优于SA的14.3 m，并且使用全部AP时为12.4 m。
• 楼层定位精度在QA为73%，超过SA的58.6%甚至超过全AP集合的70.4%。
• QA的求解时间为0.20秒，相较于经典基线实现了61x加速。
• 基于Entropy的AP重要性在所测试的度量中提供了最佳的平均3D误差。
• QA在所报告的实验中显示比SA更低的退火时间。

Figure 2 : Mean 3D error by selection method.