Interference Prediction in Wireless Networks: Stochastic Geometry meets Recursive Filtering
This work addresses interference management for wireless networks, offering a tool to improve medium access, scheduling, and resource allocation, though it is incremental as it builds on existing stochastic geometry and filtering methods.
The paper tackles the problem of predicting interference levels in wireless networks by designing a recursive predictor that filters measured interference and uses an ARMA model within a steady-state Kalman filter for low computational effort, achieving good accuracy in predictions for relevant time horizons.
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.