User-segregation based channel estimation in the MIMO system

In a multi-user multi-cell massive Multiple Input Multiple Output (MIMO) system, pilot contamination is a challenge of channel estimation. Traditional channel gain estimation follows pilot-based estimation, which is processed with the contamination and degrades the spectral efficiency. In order to overcome the above-stated problem, this work proposes User Segregation-Based Channel Estimation (USCE) improve spectral efficiency. The estimation process considers the prior minimization of pilot contamination. Hence the convergence of the proposed model with minimal contamination is validated. Moreover, the proposed user segregation method includes knowledge of both large-scale fading and small-scale fading. In order to do the analysis, the number of users is changed from N=100, 500, and 1000 to fix τd=200. The worst-case, best-case, mean, median, and Standard deviation (SD) case scenarios are all taken into account during this study. When compared to alternative methods such as Blind downlink channel estimation (BDCE) and Orthogonal pilot reuse sequence-based channel estimation (OPRSCE), the simulated results show that the suggested approach regularly produces accurate estimation. Accordingly, the NMSE value attained by the proposed work is −14.254 dB, whereas the other models achieve the NMSE values of −32.453 dB, and −54.764 dB, respectively.