Open AccessMaximum Likelihood Localization Method With MIMO-OFDM Transmission
Author(s) -
Wan Amirul Mahyiddin,
Ahmad Loqman Ahmad Mazuki,
Kaharudin Dimyati,
Fuad Erman
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3125451
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In this research, we propose to estimate the location of mobile users by using the maximum likelihood (ML) method with statistical properties of the transmission signal angle of departure (AOD) and received signal strength (RSS) from access points (APs) to user equipment (UE). Location estimation (LE) is performed at each UE by using a signal from a multiple-input, multiple-output (MIMO) antenna system at the AP, which transmits specially designed, MIMO-orthogonal frequency division multiplexing (MIMO-OFDM), beamforming signals. The ML localization method is derived from statistical models of AOD and RSS of the OFDM signal. We also derive the theoretical root mean square error (RMSE) given the statistical models. Based on the results, the ML with the AOD and RSS methods has a lower RMSE than the other methods and can achieve close to the theoretical RMSE. The RMSE can also be significantly reduced by using a higher number of APs along with proper AP placement. In addition, the LE performance increases as the number of antennas and the number of subcarriers increases but with diminishing effectiveness. The developed RMSE calculation tool in this paper can be an important instrument to investigate and plan the deployment of APs for localization and can be further extended into larger-scale studies.