The recent release of the third generation partnership project, Release 17, calls for sub-meter cellular positioning accuracy with reduced latency in calculation. To provide such high accuracy on a worldwide scale, leveraging the received signal strength (RSS) for positioning promises ubiquitous availability in the current and future equipment. RSS Fingerprint-based techniques have shown a great potential for providing high accuracy in both indoor and outdoor environments. However, fingerprint-based positioning faces the challenge of providing a fast matching algorithm that can scale worldwide. In this paper, we propose a cosine similarity-based quantum algorithm for enabling fingerprint-based high accuracy and worldwide positioning that can be integrated with the next generation of 5G and 6G networks and beyond. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a complexity that is exponentially better than its classical version as well as the state-of-the-art quantum fingerprint positioning systems, both in the storage space and the running time. We implement the proposed quantum algorithm and evaluate it in a cellular testbed on a real IBM quantum machine. Results show the exponential saving in both time and space for the proposed quantum algorithm while keeping the same positioning accuracy compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum algorithms.

翻译：第三代合作伙伴计划最新发布的第17版要求实现亚米级蜂窝定位精度，并降低计算延迟。为在全球范围内提供如此高的精度，利用接收信号强度进行定位有望在现有及未来设备中实现普遍可用性。基于接收信号强度指纹的技术在室内外环境中均展现出高精度潜力。然而，指纹定位面临需提供可全球扩展的快速匹配算法的挑战。本文提出一种基于余弦相似度的量子算法，能够实现基于指纹的高精度全球定位，并可集成至下一代5G、6G及后续网络。通过将测试接收信号强度向量与指纹接收信号强度向量纠缠，所提量子算法在存储空间和运行时间上的复杂度相比经典版本及现有最优量子指纹定位系统均呈指数级提升。我们在真实IBM量子计算机的蜂窝测试平台上实现了该量子算法并进行评估。结果表明，与传统经典指纹技术及现有最优量子算法相比，所提量子算法在保持相同定位精度的同时，实现了时间与空间上的指数级节约。