Ghrabat, Fadhil, Zhu, Huiling, Wang, Jiangzhou (2024) Machine learning-based intelligent localization technique for channel classification in massive MIMO. Discover Internet of Things, 4 . Article Number 20. E-ISSN 2730-7239. (doi:10.1007/s43926-024-00070-9) (KAR id:107593)
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Official URL: https://doi.org/10.1007/s43926-024-00070-9 |
Abstract
Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.
Item Type: | Article |
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DOI/Identification number: | 10.1007/s43926-024-00070-9 |
Uncontrolled keywords: | CNN; salp swarm algorithm (SSA); identification and mitigation; CIR; EDDPG; massive MIMO; non line of sight (NLOS); line of sight (LOS) |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 31 Oct 2024 16:05 UTC |
Last Modified: | 05 Nov 2024 13:13 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107593 (The current URI for this page, for reference purposes) |
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