COMPARATIVE ANALYSIS OF CF-MMIMO AND MMIMO SYSTEMS WITH DIVERSE ALGORITHMS IN URBAN AND RURAL ENVIRONMENTS: EVALUATION AND INSIGHTS

Abstract

The rapid advancement of 6G wireless communication systems brings forth unprecedented opportunities for improving coverage, data rates, and quality of service. As the demand for higher capacity and enhanced reliability continues to escalate, it becomes imperative to develop novel techniques that address the challenges of future wireless networks. One such technique is Cell-Free massive multiple-input multiple-output (CF-mMIMO) [1], which holds immense potential for augmenting coverage and reducing bit error rates (BER) across diverse area types, including urban and rural environments. CF-mMIMO represents a state-of-the-art technology that harnesses the synergy between cloud computing and massive MIMO systems. By combining the scalability of cloud resources with the spatial multiplexing capabilities of massive MIMO, CF-mMIMO introduces substantial improvements in coverage and spectral efficiency. Nevertheless, to achieve optimal performance in varying area types, the selection of appropriate precoding techniques assumes paramount importance. This paper conducts a comprehensive investigation into the effects of different precoding schemes, such as channel inversion, block diagonalization, and dirty paper coding, on the performance of CF-mMIMO in urban and rural areas. The outcomes of this research hold significant implications for the design and deployment of 6G networks, providing valuable guidance to network planners and engineers aiming to optimize coverage and minimize bit error rates across diverse area types.