Analysis and implementation of Discrete Time Markov Chain based method for pilot decontamination in cognitive radio based ultra-dense networks

In the context of beyond 5G (B5G) systems, the deployment of ultra-dense networks (UDNs) equipped with a large number of base-station antennas holds the promise of achieving high spectral efficiency (SE) through Cognitive Radio Network (CRN) technology. However, this approach introduce the issue of pilot contamination (PC), stemming from the reuse of pilots in adjacent cells, which has a detrimental impact on channel estimation and overall network performance. To address this issue comprehensively, we propose the SD-GraW-PD scheme for CRNs. Our scheme begins by categorizing users into two groups: cell-centered and cell-edged users. To further mitigate the effects of PC, we introduce a Discrete Time Markov Chain (DTMC)-based approach that leverages mutually orthogonal Graeco-Latin squares matrix pilot allocation. Additionally, we apply DTMC analysis to implement the Weighted Graph-Coloring Pilot Decontamination (WGC-PD) technique, enhancing the decontamination process, particularly for cell-edged users. Through extensive numerical simulations, we evaluate the proposed methodology. The results unequivocally demonstrate significant improvements in network performance, including enhancements in uplink achievable rate and SINR, in comparison to existing methods. Our DTMC-based approach emerges as an efficient and effective solution to the persistent PC problem within CR-based UDNs, offering promising prospects for achieving higher SE in the context of B5G systems.