I/O HSMM: Learning behavioral dynamics of a cognitive wireless network node from spectrum sensing

Abstract

Abstract-We introduce a generative model, dubbed I/O HSMM, for learning the bi-modal behavioral dynamics of a network of cognitive radios (CRs). Each of the two modes of the CRs is represented as a Hidden Semi-Markov model (HSMM), where the states, state durations and emissions, transition probabilities between states, and transitions between modes are uncovered based solely on RF spectrum sensing. The learning of the CR dynamics is non-parametric and derived from the Hierarchical Dirichlet Process (HDP), with the switching between the two modes modeled as a latent variable that is estimated as a part of the learning process. The non-parametric model provides flexibility in handling unknown communication protocols. We evaluate the quality of learning against ground truth, and demonstrate that this approach is promising and merits extension to more complex models.

A Generic Clustering-Based Algorithm for Approximating IOHMM Topology and Parameters

“…2) Beyond IOHMMs, the algorithm is extended to Input/Output Hidden Semi-Markov Models (IOHSMMs) [26], as well as HMMs and HSMMs [27] for which the algorithms are provided (Section.III-A).…”