TY - JOUR
T1 - Interference Prediction in Wireless Networks: Stochastic Geometry Meets Recursive Filtering
AU - Schmidt, Jorge F.
AU - Schilcher, Udo
AU - Atiq, Mahin K.
AU - Bettstetter, Christian
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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.
AB - 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.
KW - Wireless sensor networks
KW - Filtering
KW - Wireless networks
KW - Stochastic processes
KW - Interference
KW - Steady-state
KW - Wireless fidelity
UR - https://ieeexplore.ieee.org/document/9354031/
U2 - 10.1109/TVT.2021.3059032
DO - 10.1109/TVT.2021.3059032
M3 - Article
SN - 1939-9359
VL - 70
SP - 2783
EP - 2793
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 3
M1 - 9354031
ER -