Linear-regression based performance approximation for millimeter-wave multicell networks with β-Ginibre deployed base stations

Abstract

This paper investigates the performance of millimeter-wave (mmWave) multicell networks with repulsively deployed base stations (BSs), via the β-Ginibre point process (β-GPP) where β adjusts the BS repulsion. In particular, this study analytically expresses the SINR distributions and validates the results via extensive simulations. Further, this paper empirically demonstrates that the effects of BS repulsion can be well modeled via linear regression (LR) and this LR-based approach can be also quite simply approximated as the convex combination (CC) of the two extreme cases of β→0and β=1. Therefore, the proposed CC approximation can significantly reduce the time for calculating the SINR distributions in β-GPP mmWave multicell networks.

Keywords

• Linear regression
• MmWave multicell
• β-GPP
• 5G
• Stochastic geometry