This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that computes future interference values at a given location by filtering measured interference at this location. The parametrization of the predictor 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 good performance in terms of accuracy between predicted and true values for relevant time horizons. Although the predictor is parametrized for the case of Poisson networks, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also works well in more general network scenarios. Numerical examples for underlay device-to-device communications and a common wireless sensor technology illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and resource allocation.
Our research investigates the concept of interference prediction as an unprecedented approach for interference management and medium access in wireless networks. This paper is a first step in this direction: it proposes and evaluates a simple interference prediction technique that is based on low-complexity learning. Nodes predict the interference situation they expect to experience in the near future and select the most favorable time slot to start the transmission of a multislot message. The performance gain is evaluated in a small-scale fading environment in terms of link outage and delay against random slot selection. Simulation results show that interference prediction is a promising building block for wireless systems. Additional studies are needed to explore advanced techniques and assess their feasibility.