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A Stochastically Geometrical Poisson Point Process Approach for the Future 5G D2D Enabled Cooperative Cellular Network

Due to recent developments in the cellular communication system, stochastic process implementation is necessary. The cellular communication system exhibits random patterns in various domains, thereby compelling the utilization of stochastic processes to achieve an optimal output. User behaviors with respect to the variable geographical pattern, population density, architecture, data usage, and mobility over various cells are random in nature. Therefore, the stochastic-geometry-based Poisson point process (PPP) technique can be implemented to accurately analyze these random processes in device-to-device (D2D)-based cooperative cellular networks. The stochastic modeling entails the consideration of transmitters and receivers as the elements of stochastic point processes. The hexagonal method is not applicable for the implementation of heterogeneous network topologies, as it is not suitable for topologies, in which the cell size is not fixed. Therefore, a randomly designed heterogeneous network uses stochastic geometry as a viable solution for predicting the probabilistic parameters, including the cell interference, load distribution, coverage probability, base station (BS) mapping, and signal-to-interference-plus-noise ratio (SINR). Moreover, as a network architecture that is based on relay nodes (RNs), cellular and D2D users can be utilized in the domain of homogeneous random models. The associated phenomenon can be considered independent and Poisson. In this paper, the stochastic-geometric-based PPP approach is introduced for modeling the SINR, success probability, ergodic capacity, and outage probability for the D2D-enabled cooperative cellular network. The proposed PPP realistic model utilizes BS, RN, the cellular user (CU), and D2D user positioning method to design an interference-free network. The success probability, ergodic capacity, and outage probability for cellular and D2D users are used as metrics for evaluating the results with respect to various SINR threshold values and node densities. Moreover, the total success probability, ergodic capacity, and outage probability have been calculated for various multiple-input-multiple-output (MIMO) antenna configurations to validate the results. The results confirmed that the proposed PPP model approach outperforms the grid model and conventional multi-antenna ultra-dense network (UDN) approaches.