scholarly journals Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 331 ◽  
Author(s):  
Wang ◽  
Wu ◽  
Yao ◽  
Qin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Spectrum Sensing ◽  
Clustering Algorithm ◽  
Hidden Markov ◽  
Wideband Spectrum Sensing ◽  
Density Based Clustering ◽  
Radio Technology ◽  
Simulation Results ◽  
Cognitive Radio Technology

Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance.

scholarly journals Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 351
Author(s):  
André Berchtold
Keyword(s):  
Markov Chains ◽  
Markov Model ◽  
Em Algorithm ◽  
Hidden Markov Model ◽  
Confidence Intervals ◽  
Hidden Markov ◽  
Simple Calculation ◽  
Standard Methods ◽  
Transition Distribution ◽  
Simulation Results

The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.

A Text Clustering Algorithms Based on Hidden Markov Model

2011 ◽  
Vol 135-136 ◽  
pp. 1155-1158
Author(s):  
Wei Li ◽  
Mei An Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Transfer Matrix ◽  
Clustering Algorithm ◽  
Hidden Markov ◽  
Probability Model ◽  
Clustering Algorithms ◽  
Text Clustering ◽  
Global Perspective ◽  
Abstract Structure

Based on the probability model of clustering algorithm constructs a model for each cluster, calculate probability of every text falls in different models to decide text belongs to which cluster, conveniently in global Angle represents abstract structure of clusters. In this paper combining the hidden Markov model and k - means clustering algorithm realize text clustering, first produces first clustering results by k - means algorithm, as the initial probability model of a hidden Markov model ,constructed probability transfer matrix prediction every step of clustering iteration, when subtraction value of two probability transfer matrix is 0, clustering end. This algorithm can in global perspective every cluster of document clustering process, to avoid the repetition of clustering process, effectively improve the clustering algorithm .

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