# Interference Prediction in Wireless Networks: Stochastic Geometry meets Recursive Filtering

## TL;DR

This paper introduces a recursive interference prediction method for wireless networks using stochastic geometry and Kalman filtering, enabling accurate, low-complexity forecasts to enhance interference management across various scenarios.

## Contribution

It presents a novel ARMA-based recursive predictor that accurately estimates future interference levels with low computational effort, adaptable to diverse wireless network conditions.

## Key findings

- High prediction accuracy for relevant time horizons
- Effective in Poisson and non-Poisson network scenarios
- Applicable to device-to-device, Wi-Fi, and LTE coexistence

## Abstract

This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation of interference into an autoregressive moving average (ARMA) representation. This ARMA model is inserted into a steady-state Kalman filter enabling nodes to predict with low computational effort. Results show a good accuracy of predicted values versus true values for relevant time horizons. Although the predictor is parameterized for Poisson-distributed nodes, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also tends to work well in more general network scenarios. Numerical examples for underlay device-to-device communications, a common wireless sensor technology, and coexistence scenarios of Wi-Fi and LTE illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and radio resource allocation.