Spectrum Sensing in Cognitive Radio: Bootstrap and Sequential Detection Approaches

In this thesis, advanced techniques for spectrum sensing in cognitive radio are addressed. The problem of small sample size in spectrum sensing is considered, and resampling-based methods are developed for local and collaborative spectrum sensing. A method to deal with unknown parameters in sequential testing for spectrum sensing is proposed. Moreover, techniques are developed for multiband sensing, spectrum sensing in low signal to noise ratio, and two-bits hard decision combining for collaborative spectrum sensing. The assumption of using large sample size in spectrum sensing often raises a problem when the devised test statistic is implemented with a small sample size. This is because, for small sample sizes, the asymptotic approximation for the distribution of the test statistic under the null hypothesis fails to model the true distribution. Therefore, the probability of false alarm or miss detection of the test statistic is poor. In this respect, we propose to use bootstrap methods, where the distribution of the test statistic is estimated by resampling the observed data. For local spectrum sensing, we propose the null-resampling bootstrap test which exhibits better performances than the pivot bootstrap test and the asymptotic test, as common approaches. For collaborative spectrum sensing, a resampling-based Chair-Varshney fusion rule is developed. At the cognitive radio user, a combination of independent resampling and moving-block resampling is proposed to estimate the local probability of detection. At the fusion center, the parametric bootstrap is applied when the number of cognitive radio users is large. The sequential probability ratio test (SPRT) is designed to test a simple hypothesis against a simple alternative hypothesis. However, the more realistic scenario in spectrum sensing is to deal with composite hypotheses, where the parameters are not uniquely defined. In this thesis, we generalize the sequential probability ratio test to cope with composite hypotheses, wherein the thresholds are updated in an adaptive manner, using the parametric bootstrap. The resulting test avoids the asymptotic assumption made in earlier works. The proposed bootstrap based sequential probability ratio test minimizes decision errors due to errors induced by employing maximum likelihood estimators in the generalized sequential probability ratio test. Hence, the proposed method achieves the sensing objective. The average sample number (ASN) of the proposed method is better than that of the conventional method which uses the asymptotic assumption. Furthermore, we propose a mechanism to reduce the computational cost incurred by the bootstrap, using a convex combination of the latest K bootstrap distributions. The reduction in the computational cost does not impose a significant increase on the ASN, while the protection against decision errors is even better. This work is motivated by the fact that the sequential probability ratio test produces a smaller sensing time than its counterpart of fixed sample size test. A smaller sensing time is preferable to improve the throughput of the cognitive radio network. Moreover, multiband spectrum sensing is addressed, more precisely by using multiple testing procedures. In a context of a fixed sample size, an adaptive Benjamini-Hochberg procedure is suggested to be used, since it produces a better balance between the familywise error rate and the familywise miss detection, than the conventional Benjamini-Hochberg. For the sequential probability ratio test, we devise a method based on ordered stopping times. The results show that our method has smaller ASNs than the Bonferroni procedure. Another issue in spectrum sensing is to detect a signal when the signal to noise ratio is very low. In this case, we derive a locally optimum detector that is based on the assumption that the underlying noise is Student's t-distributed. The resulting scheme outperforms the energy detector in all scenarios. Last but not least, we extend the hard decision combining in collaborative spectrum sensing to include a quality information bit. In this case, the multiple thresholds are determined by a distance measure criterion. The hard decision combining with quality information performs better than the conventional hard decision combining

Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC

Energy efficient scheme based on simultaneous transmission of the local decisions in cooperative spectrum sensing

A common concern regarding cooperative spectrum sensing (CSS) schemes is the occupied bandwidth and the energy consumption during the transmissions of sensing information to the fusion center over the reporting control channels. This concern is intensified if the number of cooperating secondary users in the network is large. This article presents a new fusion strategy for a CSS scheme, aiming at increasing the energy efficiency of a recently proposed bandwidth-efficient fusion scheme. Analytical results and computational simulations unveil a high increase in energy efficiency when compared with the original approach, yet achieving better performances in some situations, and lower implementation complexity

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201