2019/03/26 by Jorge F. Schmidt, Udo Schilcher, Schmidt, Jorge F. +5 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1903.10899
openalex publication_date 2019/03/26 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
This article proposes and evaluates a technique to predict the level of\ninterference in wireless networks. We design a recursive predictor that\nestimates future interference values by filtering measured interference at a\ngiven location. The predictor's parameterization is done offline by translating\nthe autocorrelation of interference into an autoregressive moving average\n(ARMA) representation. This ARMA model is inserted into a steady-state Kalman\nfilter enabling nodes to predict with low computational effort. Results show a\ngood accuracy of predicted values versus true values for relevant time\nhorizons. Although the predictor is parameterized for Poisson-distributed\nnodes, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows\nthat it also tends to work well in more general network scenarios. Numerical\nexamples for underlay device-to-device communications, a common wireless sensor\ntechnology, and coexistence scenarios of Wi-Fi and LTE illustrate its broad\napplicability. The predictor can be applied as part of interference management\nto improve medium access, scheduling, and radio resource allocation.\n