MODELLING OF NON-UNIFORM HETEROGENEOUS CELLULAR NETWORK FOR 5G AND BEYOND NETWORKS

Abstract

With the growth of IoT in world during the past two decades, utilization of internet and devices connected to internet are increasing. These internets of connected things require high data speed, better coverage, less latency, better spectrum, and energy efficiency for future networks. To meet these requirements several Heterogeneous cellular networks have been proposed with orthogonal multiple access techniques. These techniques are not sufficient for future cellular network such as 5G, 6G and beyond networks because we have limited radio access resources like frequency and time. In all these techniques users have access to these resources that are orthogonal in nature. A novel multiple access technique called as non-orthogonal multiple access is brought forward in family of multiplexing techniques to provide access to these resources based on different power levels or code division techniques. We proposed 3 regions Non-Uniform Heterogeneous Communication Network with NOMA power domain as multiplexing technique. NuHCN model consists of three regions of coverage for MBSs called as inner, annular, and outer region of coverage. Network consists of two tiers of BSs that is MBS tier and SBS tier. In this research we considered 6 types and 3 groups of NOMA users in Network. For simplicity only 2 users are considered in each NOMA group. Firstly, we discussed users association and users grouping in Non-Uniform HetNet. Users’ association in NuHCN is discussed based on receives signal strength in different regions. Distribution of distances from BSs to users are critical for analysis of performance parameters of coverage, so we derived PDFs from CDF of users in network. Results for PDFs derived analytically are shown in this research and plotted in MATLAB. Analytical results are verified by Monte Carlo simulations. Based on the PDFs we derived results for coverage performance of users located in NuHCN. Analytical results are plotted and compared with Monte Carlo simulations. Based on the analytical results of coverage, we derived rate coverage also known as throughput for users located in NOMA NuHCN. Analytical results are plotted for throughput. Afterwards based on SINR equations for users in NOMA network we compared throughput for NOMA with OMA which showed that NOMA is a potential candidate for improved throughput as compared to OMA.