Maximum likelihood localization method with MIMO-OFDM transmission

Mahyiddin, Wan Amirul and Mazuki, Ahmad Loqman Ahmad and Dimyati, Kaharudin and Erman, Fuad (2021) Maximum likelihood localization method with MIMO-OFDM transmission. IEEE Access, 9. pp. 150355-150365. ISSN 2169-3536, DOI

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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.

Item Type: Article
Funders: University of Malaya Faculty Grant (GPF035A-2019)
Uncontrolled Keywords: Array signal processing; Antennas; Location awareness; Receivers; OFDM; Massive MIMO; Global Positioning System; Localization; wireless; MIMO; OFDM; maximum likelihood
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 17 Mar 2022 01:11
Last Modified: 17 Mar 2022 01:11

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