A Novel Algorithm for Cooperative Distributed Sequential Spectrum Sensing in Cognitive Radio

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.Comment: This paper has been withdrawn by the author due to the submission of detailed journal version of the same paper, to arXi

Cooperative spectrum sensing for cognitive radio

Cognitive Radio (CR) is a technology which improves the spectrum utilization which some frequency bands is unoccupied or temporary vacate by the users. It is designed to detect communication opportunities in wireless system and to be aware of and sensitive to the changes in its surroundings. However, hidden node has become one of the major problems in cognitive network. Thus, the spectrum sensing is designed to operate cooperatively. If a number of CRs from several locations are sensing the spectrum, they can share the information before making the decision to use the spectrum. This thesis proposes energy detection method on Centralized Cooperative Spectrum Sensing (CCSS) based for simulation and data measurement analysis by including Rayleigh fading channel and Additive White Gaussian Noise (AWGN) into the simulation. Results show cooperative spectrum sensing enhance the detection probability and improve the performance of spectrum sensing

Cooperative Sequential Compressed Spectrum Sensing over Wide Spectrum Band

Abstract-Cognitive radio (CR) techniques promise to significantly increase the available spectrum thus wireless bandwidth. With the increase of spectrum allowed for CR, it is critical and challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing framework which concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower cost spectrum sensing. First, to ensure more timely spectrum detection while avoiding the high overhead involved in periodic recovery of CS signals, we design a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further identify the sub-channels occupied, we exploit joint sparsity of the signals among neighboring users to achieve efficient cooperative wideband sensing. Our performance evaluations demonstrate that our proposed scheme can outperform other peer schemes significantly in terms of the detection delay, detection accuracy, sensing overhead and sensing accuracy