MULTIPLE RSS FINGERPRINT BASED INDOOR LOCALIZATION IN RIS-ASSISTED 5G WIRELESS COMMUNICATION SYSTEM

Abstract

The received signal strength (RSS) fingerprint based localization is a widely used technique for location estimation in the indoor environment with the fifth generation (5G) wireless communication. However, the RSS feature is easily affected by the noise and other variations of the propagation channel, thus limiting the localization accuracy. In this paper, we propose a multiple RSS fingerprint based localization scheme in the reconfigurable intelligent surface (RIS) assisted system, where the RSS values under different RIS configurations are leveraged as the fingerprints. However, it is challenging to set the favorable RIS configurations. To tackle this challenge, we design an optimization method based on Cramér-Rao Lower Bound (CRLB) to derive the optimal RIS configurations to achieve a robust and accurate location estimation, where the CRLB is minimized, and projected gradient descent (PGD) method is applied to solve this optimization problem. After the fingerprints are collected, deep neural network (DNN) is employed for location estimation. Simulation results reveal that the proposed scheme performs well in terms of localization accuracy and stability.