Radio Resource Dimensioning with Cox Process Based User Location Distribution

TL;DR

This paper introduces a novel radio resource dimensioning method for 5G NR that models user locations with Cox processes, deriving explicit congestion probability expressions to optimize resource allocation.

Contribution

It presents a new approach using Cox process models for indoor and outdoor user distributions and derives explicit formulas for congestion probability in 5G resource planning.

Findings

Derived explicit congestion probability expressions.

Validated dimensioning approach with numerical results.

Modeled user locations using Cox processes for accurate resource estimation.

Abstract

The upcoming fifth generation (5G) New Radio (NR) interface inherits many concepts and techniques from 4G systems such as the Orthogonal Frequency Division Multiplex (OFDM) based waveform and multiple access. Dimensioning 5G NR interface will likely follow the same principles as in 4G networks. It aims at finding the number of radio resources required to carry a forecast data traffic at a target users Quality of Services (QoS). The present paper attempts to provide a new approach of radio resources dimensioning considering the congestion probability, qualified as a relevant metric for QoS evaluation. We distinguish between the spatial random distribution of indoor users, modeled by a spatial Poisson Point Process (spatial PPP) in a typical area covered by a 5G cell, and the distribution of outdoor users modeled by a linear PPP generated in a random system of roads modeled according to a Poisson Line Process (PLP). Moreover, we show that the total requested Physical Resource Blocks (PRBs) follows a compound Poisson distribution and we attempt to derive the explicit expression of the congestion probability by introducing a mathematical tool from combinatorial analysis called the exponential Bell polynomials. Finally we show how to dimension radio resources, for a given target congestion probability, by solving an implicit relation between the necessary resources and the forecast data traffic expressed in terms of cell throughput. Different numerical results are presented to justify this dimensioning approach.