Fingerprint Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface Assisted Networks

Abstract The intelligent Reflective Surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain customized reflected wave direction by modulating the amplitude, phase, which can be easy deployed to change the wireless signal propagation environment and enhance the communication performance under Non-Line-of Sight (NLOS) environment where location services cannot be performed accurately. In this paper, a low-rank matrix reconstruction enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and thus receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as fingerprint to form a partial fingerprint database. As a next step, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve a sub-meter accuracy in a NLOS environment.