Channel Status Learning for Cooperative Spectrum Sensing in Energy-Restricted Cognitive Radio Networks.

A cognitive radio (CR) is a promising technology to solve the emerging spectrum crisis, especially for applications where thousands of wireless sensor nodes are deployed. Since continuous spectrum sensing will greatly reduce the lifetime of a network composed of energy-restricted CR nodes, an accurate method for predicting spectrum occupancy is necessary to improve energy efficiency. This paper proposes a hidden Markov model (HMM)-based cooperative spectrum sensing (CSS) that predicts the status of a network environment. The traditional prediction algorithms for cooperative spectrum sensing assume that all CR nodes have the same network environment. However, the channel availability of various CR nodes can be quite different, and thus the traditional algorithms will lead to low prediction accuracy in a complex radio environment. The proposed methods learn the historical spectrum sensing results and help the network to make an energy-efficient spectrum sensing decision. More specifically, the hidden state of HMM is set to different areas, where primary users (PUs) perform different activities. A Baum-Welch (BW) algorithm is employed to estimate the parameters of the HMM based on the past spectrum sensing results, and then the parameters are fed to a forward algorithm for the predicting of PUs' activity. Based on the prediction, secondary users (SUs) are classified into either "interfered by PU" or "not interfered by PU." The nodes selected as "interfered by PU" will not perform spectrum sensing to reduce unnecessary energy consumption. The performance of the proposed method is evaluated using the simulations under different traffic conditions. The simulation results show that, compared with the conventional HMM-based methods, the effectiveness of the proposed algorithm in energy efficiency and spectrum utilization improved by about 13% and 15%, respectively.