Intelligent Reflecting Surface enabled Fingerprinting-based Localization with Deep Reinforcement Learning

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 10; pp. 1 - 11
• Main Authors: Wang, Yuhao, Ho, Ivan Wang-Hei, Zhang, Shuowen, Wang, Yuhong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Configurations; Deep learning; Deep reinforcement learning; Fingerprint recognition; Fingerprinting; Hardware; Intelligent reflecting surface; Localization; Location awareness; Position measurement; Receivers; Reconfigurable intelligent surfaces; Signal strength; Wireless communication
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2023.3275581

Intelligent reflecting surface (IRS) is considered a promising solution to manipulate the radio frequency transmission environment in the sixth-generation (6 G) wireless systems. However, little attention was received by IRS-aided localization techniques. Among range-free wireless localization strategies, received signal strength indicator (RSSI) fingerprinting-based technique is preferred since it can be easily accessed. Inspired by these and the tremendous success of deep reinforcement learning (DRL), we propose an IRS-enabled fingerprinting-based localization methodology with the aid of DRL. Specifically, we firstly propose an IRS-enabled fingerprinting-based localization system. In this system, RSSI lists are created by periodic IRS configurations and pre-collected as database. When a request of localization from a receiver is sent to the server, the database is compared with the online-measured RSSI data to identify the best receiver position estimate using the nearest neighbor algorithm. In addition, we develop a DRL-based IRS configuration selector to identify the most qualified IRS configurations so as to minimize the localization error. We also propose a communication protocol for the operation of the proposed localization methodology. Extensive simulation under different circumstances have been conducted and the results indicate that the localization accuracy scales with the number of IRS configurations. With the aid of DRL, the localization accuracy is further boosted by more than 40% as compared with previous work.