Environment-Aware Wireless Localization Enabled by Channel Knowledge Map
Long Yang, Yong Zeng, Xiaoli Xu, Yongming Huang
Abstract
The performance of wireless localization critically depends on the actual radio propagation environment. This paper proposes a novel framework towards environment-aware wireless localization, enabled by the emerging concept known as channel knowledge map (CKM). Specifically, we propose a line-of-sight (LoS) map-enabled environment-aware anchor selection scheme to minimize the Bayesian Cramer-Rao lower bound (BCRLB) of the positioning error. As the formulated problem is combinatorial, we propose an efficient greedy-based algorithm, which selects the best anchor node sequentially by ensuring that each newly selected anchor leads to the maximum reduction to the BCRLB. Simulation results show that the proposed environment-aware anchor selection can significantly outperform the benchmarking environment-ignorant schemes, including the min-distance based selection or simply activating all anchors.