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.

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.

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