Modelling and Simulation of various detection algorithms in uplink Massive MIMO systems: A Comparative Analysis

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 inves-tigation 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 (beta) 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 (beta).

Automated summary

This research focuses on the modeling and comparative investigation of signal detection algorithms in uplink massive MIMO systems. The goal is to achieve optimal error rate performance with low complexity. Through simulation-based comparisons, different detection algorithms were evaluated and their performance was analyzed under various MIMO scenarios. The results showed that the BOX equalization detector provided the best performance.