Opportunistic Spectrum Access in Imperfect Spectrum Sensing Cognitive Networks

Abstract

Spectrum sensing strategy is key to realize cognitive radio. However, spectrum sensing error would affect the access strategy of secondary users in cognitive networks. This paper addresses the spectrum sensing strategy under imperfect spectrum sensing, and proposes opportunistic spectrum access strategies for the imperfect spectrum sensing and fading channels respectively. By setting the optimal operating point of the spectrum detection and updating the confidence vector, we turn the spectrum access optimizations under the imperfect spectrum sensing and fading channels into ones under the perfect spectrum sensing based on the partially observable Markov decision process model. Simulation results show that the strategies proposed could make the secondary users achieve about 10% margin in throughput and the cognitive networks have higher spectrum utilization.

A collaborative spectrum sensing algorithm for cognitive radio based on related vector machine

“…With the development of emerging wireless communication technologies, people's increasing share of the operating bandwidth of the system has made the limited spectrum resources more and more tense [1][2][3][4][5] . Cognitive radio energy effectively solves the problem of shortage of spectrum resources and insufficient utilization of frequency bands, so it has been widely concerned by researchers at home and abroad [6][7][8][9][10] . Bkassiny 11 used a machine learning approach to solve the spectrum sensing problem in a certain signal-to-noise environment.…”

Measurement Matrix Calibration Method for Modulated Wideband Converter under Imperfect Periodic Pseudorandom Waveform

“…It points out that a low-pass continuous-time signal must be discretized with a sampling rate at least the twice of the maximal frequency for the sake of undistorted reconstruction meanwhile band-pass analog signal requires a minimal sampling rate twice of its bandwidth and the corresponding sampling frequency is constrained within a range determined by the minimal and the maximal component. In the past decades, evolving wireless service and applications result in the everincreasing data types, high throughput and transmission rate, which brings about the significant expanding in occupied frequency bandwidth [3]- [5]. This demands excellent performance analog to digital converter (ADC), huge storage and logical device with high-speed processing ability, raising new challenges and bottleneck problems to the orthodox digital signal processing mechanism based on conventional sampling theorem.…”