2017/09/26 by Indika Dhanapala, Dhanapala, Indika S. A., Ramona Marfievici +7
Computer Science · Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #Millimeter-Wave Propagation and Modeling #Networking and Internet Architecture (cs.NI) #Wireless Networks and Protocols
paper · pdf · doi:10.48550/arxiv.1709.08950
openalex publication_date 2017/09/26 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz\nunlicensed spectrum causing packet losses and increased channel contention. In\nparticular, WiFi interference is a severe problem for low-power wireless\nnetworks as its presence causes a significant degradation of the overall\nperformance. In this paper, we propose a proactive approach based on WiFi\ninterference modeling for accurately predicting transmission opportunities for\nlow-power wireless networks. We leverage statistical analysis of real-world\nWiFi traces to learn aggregated traffic characteristics in terms of\nInter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov\nModulated Poisson Process (MMPP(2)) model, enable accurate estimation of\ninterference. We further use a hidden Markov model (HMM) for channel occupancy\nprediction. We evaluated the performance of i) the MMPP(2) traffic model w.r.t.\nreal-world traces and an existing Pareto model for accurately characterizing\nthe WiFi traffic and, ii) compared the HMM based white space prediction to\nrandom channel access. We report encouraging results for using interference\nmodeling for white space prediction.\n