On the use of Hidden Markov Processes and auto-regressive filters to incorporate indoor bursty wireless channels into network simulation platforms
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In this paper we thoroughly analyze two alternatives to replicate the bursty behavior that characterizes real indoor wireless channels within Network Simulation platforms. First, we study the performance of an improved Hidden Markov Process model, based on a time-wise configuration so as to decouple its operation from any particular traffic pattern. We compare it with the behavior of Bursty Error Model Based on an Auto-Regressive Filter, a previous proposal of ours that emulates the received Signal to Noise Ratio by means of an auto-regressive filter that captures the “memory” assessed in real measurements. We also study the performance of one of the legacy approaches intrinsically offered by most network simulation frameworks. By means of a thorough simulation campaign, we demonstrate that our two models are able to offer a much more realistic behavior, yet maintaining an affordable response in terms of computational complexity.