Modelling and Simulation of various detection algorithms in uplink Massive MIMO systems: A Comparative Analysis. (December 2022)

• Record Type: Journal Article
• Authors: Chauhan, Rishika; Sharma, Shefali; Pachauri, Rahul
• Is Part Of: Advances in engineering software. Volume 174(2022)
• Publication Date: 2022-12
• Subjects: Massive MIMO -- Signal detection -- Equalization based linear detectors -- Approximate matrix inversion detection -- BOX equalization -- BER performance; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103318
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Performance analysis of detection algorithms is done in uplink massive MIMO system. Detection methods are compared via error rate routine and computational complexity. The research aims at obtaining an optimal detector to high BER and low complexity. Simulation reveal that the BOX equalization detector provides the best result. Abstract: Signal detection is one of the major issues in uplink massive MIMO (multiple input multiple output) systems due to the deployment of large number of antennas. In this research article, the modelling and comparative investigation of various detection algorithms in massive MIMO systems is done to achieve the optimal error rate performance with low complexity. The simulation-based comparison is conducted between equalization based linear detectors, approximate matrix inversion based linear detectors and non-linear BOX equalization detectors. The error rate performance is evaluated with different MIMO scenarios in terms of different ratio between number of users and base station (BS) antennas (β) and different number of iterations (n). The complexity analysis is also done for all the detectors. Simulation results manifest that the BOX equalization detector ADMIN provides the best performance with the lowest complexity. It is also observed that the approximate matrix inversion based linear detectors and the OCD detector perform better with the increased value of (β).