Hybrid detection techniques for 5G and B5G M-MIMO system

Abstract

Massive-Multiple Inputs and Multiple Outputs (M−MIMO) will have a greater impact in the advanced radio framework. It efficiently increases the capacity, spectral access, and data speed of the framework. However, the detection of the signal becomes complicated due to the use of several antennas at the microcell. Separating such a vast range of connected devices is necessary to enable the detection of transmit antennas in response to various available data sources. In the presented work, novel hybrid algorithms such as QR-maximum likelihood detection (QR-MLD), QR-minimum means square error (MMSE), QR-zero forcing equaliser (ZFE), and QR-beam forming (QR-BF) are implemented for 16x16, 64x64, and 256x256 MIMO structures. The hybrid algorithms obtained an efficient bit error rate (BER) of 10-3 at the SNR of 2.9 dB with trivial complexity. Further, the proposed algorithms are compared with conventional methods. It is be noted that the QR-MLD achieves a gain of 3 dB when compared to the MMSE. It is concluded that the QR-MLD provided optimal performance and significantly enhanced the throughput gain of the framework.