Collaborative Spectrum Sensing via Online Estimation of Hidden Bivariate Markov Models

Abstract

Collaborative spectrum sensing exploits multiuser diversity by combining spectrum sensing information from multiple secondary users to make joint decisions about spectrum occupancy. In hard fusion schemes, each secondary user makes a hard decision on spectrum occupancy and a fusion center makes a final decision by combining the individual hard decisions according to a fusion rule. In soft fusion schemes, each secondary user provides a signal power measurement to the fusion center, which performs further processing on the collection of all observations to make a final decision. In this paper, we propose hard and soft fusion collaborative spectrum sensing schemes based on online hidden bivariate Markov chain modeling of the signals received by secondary users. Compared to prior collaborative sensing schemes, the proposed model-based schemes do not rely on precomputed thresholds or weights, and achieve superior performance. Online estimation of hidden bivariate Markov models provides predictive information that can be used to improve the performance of dynamic spectrum access. Numerical results are presented to demonstrate the performance and communication overhead tradeoffs of the proposed collaborative spectrum sensing schemes.

Non-stationary data segmentation with hidden evidential semi-Markov chains

“…Among extensions, hidden semi-Markov chains (HSMCs) can be very useful as they allow the modelling of any sojourn time in a given class, when it is necessarily geometric in HMCs [13], [14], [15], [16]. Hidden bivariate Markov chains [17], [18], double Markov chains [19], or still pairwise Markov chains (PMCs) [20], [21], [22], [23] are other extensions. This paper is related to "triplet Markov chains" (TMCs), which is an extension of PMCs consisting in considering a third stochastic sequence, which might or might not have a practical signification, along the hidden sequence to be estimated and the sequence of observations, and assuming the joint Markovianity of the three sequences.…”

Markov modeling of discrete fading channels using birth‐death channels

“…Finite‐state Markov channels (FSMCs) have been widely used to model practical communication systems, including 1.8‐GHz fading channels, wireless networks, railway fading channels, collaborative spectrum sensing, power‐line communication channel, multiple access channel, and cognitive radio network . An FSMC captures the correlation of a binary process that represents the success (indicated by bit 0) and failure (indicated by bit 1) of consecutive transmissions.…”

Assessing the segmentation performance of pairwise and triplet Markov models

“…Further researches demonstrate that TMMs allow a particular semi-Markovian modeling of X [13] , which is a valuable result since the hidden semi-Markov models are particularly wellsuited for a scope of applications [14,15] . Besides, the bivariate hidden Markov chains [16,17] , which are similar to a subclass of TMMs, do also provide a framework for efficient data processing. For these reasons, we believe that researches on TMMs may have a considerable impact, especially in the field of latent variable modeling.…”