Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/59380
Title: Physical layer algorithms and architectures for accurate detection and classification in cognitive radios
Authors: Narendar Madhavan
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Issue Date: 2014
Source: Narendar Madhavan. (2014). Physical layer algorithms and architectures for accurate detection and classification in cognitive radios. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The new mobile broadband technology is rapidly growing towards a fifth generation (5G), as there is an increasing demand for high data rate devices. The advent of new wireless communication services renders the electromagnetic spectrum congested. This congestion, coupled with rigid allocation of spectrum to the new wireless systems, leads to inefficient spectrum utilization and causes an apparent spectrum scarcity. Cognitive radio (CR) is envisaged as a solution to tackle the problem of this apparent spectrum scarcity. It is a conscious radio, which opportunistically uses the spectrum of a licensed user known as primary user (PU) while causing minimal interference to them. It utilizes software-defined radio (SDR) to adapt its transmission properties to match the required standards. In order to determine the spectral vacancies, the CR has to locate the PU signals accurately with the help of a spectrum sensing unit. In addition, after detecting the signal, the CR must be able to classify the detected signal into a known user type. This thesis presents methods for accurate wideband detection and modulation classification for a stand-alone CR scenario in the physical layer domain. These methods operate with partial knowledge about the PUs' signal and/or the noise environment. The proposed algorithms can also extract certain features of the signal spectrum such as edge frequencies and modulation scheme in a robust manner. In general, the research problem of signal detection and classification is addressed in this thesis using three methods- filter bank based detectors, higher order statistics and fractional lower order statistics. In the first work, a filter bank based detection method that helps in determining the location of the vacant bands, by locating the PU signal and its edge frequencies is presented. In order to increase the granularity and efficiency of detection, the method involves a new multi-stage discrete Fourier transform filter bank based energy detector. An algorithm is proposed that determines the edge frequencies of the PU channels using the properties of filter bank and the information from the energy detector. Simulation and computational analysis show that the proposed method gives a good balance between accuracy and complexity. The design example and simulations show that the gate count resource utilization of this detection scheme is 22.9% lesser than the wavelets based detection method at the cost of a slight degradation of 0.5% in detection accuracy. This method provides a good foundation to understand the underlying problem of spectrum sensing and its challenges. The algorithms in the first work are based on second-order statistics where the noise has a degrading effect on the signal. The sensing becomes even more challenging in the presence of strong interferers, noisy environment or under fading conditions. To mitigate this, the second work proposes a detection and classification algorithm based on higher-order statistics (HOS) and fractional lower-order statistics (FLOS). Spectrum sensing is performed using the proposed test statistic for detection that incorporates modified cumulants derived using fractional lower order statistics. The performance of the proposed method is compared with the conventional method based on higher-order statistics for various noise conditions. It is shown that the proposed method performs well in both Gaussian and non-Gaussian conditions as compared to the conventional method. An experimental data acquisition setup was employed to obtain real time signals from the wideband spectrum to analyze the noise characteristics. The main advantage of the proposed method is that the same statistic used for detection is also used for classification. After locating the vacant bands, the CR is required to change its transceiver parameters to use the vacant band opportunistically, which is achieved using SDR technology. In the third work, a CR scheme including a policy engine, two-stage spectrum sensing unit and an SDR is elucidated. The focus is on the working of a real-time policy engine with a two-stage spectrum sensing unit that aids policy driven cognitive radio. A new two-stage filter bank based energy detector and cumulants method has been proposed for accurate spectrum sensing. The filter bank used for channelization is used for spectrum sensing as well, resulting in complexity reduction. Furthermore, this thesis presents validation of some existing and proposed algorithms using real world signals captured using a USRP2 radio and GNURadio framework. The main contribution of the work is on improved sensing and classification methods for cognitive radios and military radios. By deploying SDR, an overall framework for spectrum sensing based policy-driven CR has been demonstrated.
URI: https://hdl.handle.net/10356/59380
DOI: 10.32657/10356/59380
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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