In recent years, multiuser-multiple-input multiple-output (MU-MIMO)-based wireless communication system has emerged as a prominent 5G technique that has several advantages over conventional MIMO systems such as high data rate and channel capacity. In this paper, the authors introduce a novel low-complexity radix factorization-based fast Fourier transform (FFT) as a multibeamformer and maximal likelihood-MU detection (ML-MUD) techniques as an optimal signal subdetector which results with considerable complexity reduction with intolerable error rate performance.
The proposed radix-factorized FFT-multibeamforming (RF-FFT-MBF) architectures have the potential to reduce both hardware complexity and energy consumptions as compared to its state-of-the-art methods while meeting the throughput requirements of emerging 5G devices. Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors.
Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.
Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.
