Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

Abstract

A realistic simulated 5G DM-MIMO wireless network operating at 28 GHz mmWaves has been deployed using Open Street Maps and Matlab® over the campus of Universidad San Francisco de Quito (USFQ). Received Signal Strength fingerprints have been collected at Base Station antenna array, and the K-Nearest Neighbors method has been used to perform the match between the received RF patterns and the stored fingerprints. Three different procedures were tested and their results were compared, exhibiting very good outcomes in all the cases.