journal-of-discrete-mathematical-sciences-and-cryptography Journal of Discrete Mathematical Sciences and Cryptography 2169-0065 0972-0529 Taru Publications 10.47974/JDMSC-1751 A mathematical analysis of M-MIMO using ML based signal detection algorithms Jaiman Akash Department of Computer Science and Engineering, Jaipur, Rajasthan, Poornima Institute of Engineering and Technology, India Kumar Arun Department of Electronics and Communication Engineering, Bengaluru, Karnataka, New Horizon College of Engineering, India Sharma Himanshu Department of CSE, Jaipur, Rajasthan, JECRC University, India Gour Nidhi Department of CSE, Jaipur, Rajasthan, JECRC University, India Gour Nishant Department of CSE, Jaipur, Rajasthan, JECRC University, India Chakravarty Sumit Department of Electrical Engineering, GA, Kennesaw State University, United States 26 3 759 766 01 04 2023

The advanced radio communication is being rapidly deploying in the world. Multiple Inputs and Multiple Outputs (MIMO) is limited to 8 Antennas at the base station already utilized in fourth generation(4G). Massive-MIMO (M-MIMO) has been gaining and anormal popularities for its characteristics and utilisation in 5G. The throughput of the system is significantly increased by utilizing maximum number of antennas at the BS. However, the detection of the original signal is considered a complex task in massive MIMO. Hence, in this article we introduce a novel QRM-MLD method with an aim to enhance the throughput with a low intricacy. Further we compared a proposed Machine learning (ML) based QRM-MLD with existing detection scheme the experimental result revealed that the QRM-MLD acquired a gain of 2DB as compound with conventional QRM-MLD.

 Massive MIMO ML Detection methods access open retracted no