974 research outputs found
Data Driven Quickest Detection in Networks and Its Applications in Spectrum Sensing
Cognitive radio is one of the enabling technologies considered for the next generation communication systems for many mission-critical applications. In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. In this dissertation, the study is focused on data driven quickest detection applied to energy detection based spectrum sensing. Firstly, a framework that integrates quickest detection and belief propagation is applied to the cooperative spectrum sensing where the primary user (PU) activities are heterogeneous in the space and dynamic in the time. The performance of the proposed scheme is analyzed mathematically. Using numerical simulations, detection performance measured by false alarm rate and average detection delay is obtained for different setups. Numerical simulations have demonstrated the validity of the proposed technique.Secondly, we propose a universal quickest change detection scheme based on density ratio estimation for spectrum sensing by detecting the sudden change of spectrum (e.g., the emergence of PU), where neither the pre-change nor post-change distribution (even the distribution forms) is known to secondary users (SUs), thus achieving robustness to complex spectrum environment, where SUs have no prior information about the measurement distributions. The validity of the proposed schemes has been shown by numerical simulations.Finally, we extend the detection of change in spectrum to millimeter-wave environment. As millimeter-wave is becoming part of the physical layer standard in the next-generation cellular network, it also brings about many questions and challenges. Not all the existing theories and methods for traditional wireless communication can apply directly to millimeter-wave communication because of the adoption of directional antenna and the high frequency band used. We propose a data-driven spectrum change sensing technique based on mean recurrence time to efficiently detect the PU activities which is tolerant of small fluctuations. The proposed spectrum sensing works well without a priori knowledge of the sensed signal, and doesn\u27t take assumption of independent and identically distributed random variables. It can also serve as a general framework for detection in other areas. The experimental results validate the proposed detection framework
On the Performance of Quickest Detection Spectrum Sensing: The Case of Cumulative Sum
Quickest change detection (QCD) is a fundamental problem in many
applications. Given a sequence of measurements that exhibits two different
distributions around a certain flipping point, the goal is to detect the change
in distribution around the flipping point as quickly as possible. The QCD
problem appears in many practical applications, e.g., quality control, power
system line outage detection, spectrum reuse, and resource allocation and
scheduling. In this paper, we focus on spectrum sensing as our application
since it is a critical process for proper functionality of cognitive radio
networks. Relying on the cumulative sum (CUSUM), we derive the probability of
detection and the probability of false alarm of CUSUM based spectrum sensing.
We show the correctness of our derivations using numerical simulations.Comment: This paper is accepted for publication in IEEE Communication Letters
Jan 202
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
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