Indoor Localization with Multi-beam of 5G New Radio Signals

In this work, we investigate the property of the multi-beam of 5G new radio (NR) signals for indoor localization. Specifically, the 5G NR signals are firstly sampled by a self-developed software-defined receiver, and the multi-beam is extracted via detecting the multiple synchronization signal blocks (SSBs). Secondly, with the assistance of the pilots in the multiple SSBs, the reference signal received power (RSRP) and reference signal received quality (RSRQ) of the multi-beam are calculated. Thirdly, by stacking the RSRP and RSRQ of the multi-beam as the observables, a fingerprint database is constructed. With the aim to efficiently process the fingerprint features and improve the accuracy of indoor localization, a CatBoost-based algorithm is proposed, and the parameters are further optimized by tree-structured parzen estimator (TPE). To verify the effectiveness of the proposed method, indoor field tests are carried out in an office scenario, where real 5G signals are transmitted from a commercial 5G NR base station indoors. The field tests demonstrate that, by taking the advantages of the multi-beam of 5G NR, the localization accuracy can be able to achieve the accuracy of 1.06 m in the metric of root mean squared error (RMSE), even when only one base station is heard indoors. By comparison with the single-beam, the accuracy of multi-beam has improved 48%.