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

Abstract

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.

Progress in the Graphene Oxide-Based Composite Coatings for Anticorrosion of Metal Materials

“…The titanate coupling agent acted as a bridge to covalently connect GO and the polymer matrix, and, consequently, increased the compatibility and interactions between GO and the polymer matrix. Through this method, high-performance nanocomposite coatings could be developed [66]. In other work, Zhou et al [67] used a co-crosslinking strategy between polydopamine (PDA) and polyethyleneimine (PEI) to modify GO.…”