An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition

This thesis introduces an innovative signal detector algorithm in facilitating the cognitive radio functionality for the new IEEE 802.22 Wireless Regional Area Networks (WRAN) standard. It is a signal detector based on a Singular Value Decomposition (SVD) technique that utilizes the eigenvalue of a received signal. The research started with a review of the current spectrum sensing methods which the research classifies as the specific, semiblind or blind signal detector. A blind signal detector, which is known as eigenvalue based detection, was found to be the most desired detector for its detection capabilities, time of execution, and zero a-priori knowledge. The detection algorithm was developed analytically by applying the Signal Detection Theory (SDT) and the Random Matrix Theory (RMT). It was then simulated using Matlab® to test its performance and compared with similar eigenvalue based signal detector. There are several techniques in finding eigenvalues. However, this research considered two techniques known as eigenvalue decomposition (EVD) and SVD. The research tested the algorithm with a randomly generated signal, simulated Digital Video Broadcasting-Terrestrial (DVB-T) standard and real captured digital television signals based on the Advanced Television Systems Committee (ATSC) standard. The SVD based signal detector was found to be more efficient in detecting signals without knowing the properties of the transmitted signal. The algorithm is suitable for the blind spectrum sensing where the properties of the signal to be detected are unknown. This is also the advantage of the algorithm since any signal would interfere and subsequently affect the quality of service (QoS) of the IEEE 802.22 connection. Furthermore, the algorithm performed better in the low signal-to-noise ratio (SNR) environment. In order to use the algorithm effectively, users need to balance between detection accuracy and execution time. It was found that a higher number of samples would lead to more accurate detection, but will take longer time. In contrary, fewer numbers of samples used would result in less accuracy, but faster execution time. The contributions of this thesis are expected to assist the IEEE 802.22 Standard Working Group, regulatory bodies, network operators and end-users in bringing broadband access to the rural areas

Performance analysis of energy detector over generalised wireless channels in cognitive radio

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is widely employed to perform spectrum sensing in cognitive radio over different generalised channel models. In this analysis, both the average probability of detection and the average area under the receiver operating characteristic curve (AUC) are derived using the probability density function of the received instantaneous signal to noise ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then, the behaviour of the energy detector over к --- µ shadowed fading channel, which is a composite of generalized multipath/shadowing fading channel to model the lineof- sight (LoS) communication medium is investigated. The analysis of the energy detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to include maximal ratio combining (MRC), square law combining (SLC) and square law selection (SLS) with independent and non-identically (i:n:d) diversity branches. To overcome the problem of mathematical intractability in analysing the energy detector over i:n:d composite fading channels with MRC and selection combining (SC), two different unified statistical properties models for the sum and the maximum of mixture gamma (MG) variates are derived. The first model is limited by the value of the shadowing severity index, which should be an integer number and has been employed to study the performance of energy detector over composite α --- µ /gamma fading channel. This channel is proposed to represent the non-linear prorogation environment. On the other side, the second model is general and has been utilised to analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel. Finally, a special filter-bank transform which is called slantlet packet transform (SPT) is developed and used to estimate the uncertain noise power. Moreover, signal denoising based on hybrid slantlet transform (HST) is employed to reduce the noise impact on the performance of energy detector. The combined SPT-HST approach improves the detection capability of energy detector with 97% and reduces the total computational complexity by nearly 19% in comparison with previously implemented work using filter-bank transforms. The aforementioned percentages are measured at specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio

Polarity-Coincidence-Array based spectrum sensing for multiple antenna cognitive radios in the presence of non-gaussian noise

One of the main requirements of the cognitive radio (CR) systems is the ability to perform spectrum sensing in a reliable manner in challenging environments that arise due to propagation channels which undergo multipath fading and non-Gaussian noise. While most existing literature on spectrum sensing has focused on impairments introduced by additive white Gaussian noise (AWGN), this assumption fails to model the behavior of certain types of noise in practice. In this paper, the use of a non-parametric and easily implementable detection device, namely polarity-coincidence-array (PCA) detector, is proposed for the detection of weak primary signals with a cognitive radio equipped with multiple antennas. Its performance is evaluated in the presence of heavy-tailed noise. The detector performance in terms of the probabilities of detection and false alarm is derived when the communication channels between the primary user transmitter and the multiple antennas at the cognitive radio are AWGN as well as when they undergo Rayleigh fading. From the numerical results, it is observed that a significant performance enhancement is achieved by the PCA detector compared to that of the energy detector with AWGN as well as fading channels as the heaviness of the tail of the non-Gaussian noise increases

Enhancing Spectrum Utilization in Dynamic Cognitive Radio Systems

Cognitive radio (CR) is regarded as a viable solution to enabling flexible use of the frequency spectrum in future generations of wireless systems by allowing unlicensed secondary users (SU) to access licensed spectrum under the specific condition that no harmful interference be caused to the licensed primary users (PU) of the spectrum. In practical scenarios, the knowledge of PU activity is unknown to CRs and radio environments are mostly imperfect due to various issues such as noise uncertainty and multipath fadings. Therefore, important functionalities of CR systems are to efficiently detect availability of radio spectrum as well as to avoid generating interference to PUs, by missing detection of active PU signals. Typically, CR systems are expected to be equipped with smart capabilities which include sensing, adapting, learning, and awareness concerned with spectrum opportunity access, radio environments, and wireless communications operations, such that SUs equipped with CRs can efficiently utilize spectrum opportunities with high quality of services. Most existing researches working on CR focus on improving spectrum sensing through performance measures such as the probabilities of PU detection and false alarm but none of them explicitly studies the improvement in the actual spectrum utilization. Motivated by this perspective, the main objective of the dissertation is to explore new techniques on the physical layer of dynamic CR systems, that can enhance actual utilization of spectrum opportunities and awareness on the performance of CR systems. Specifically, this dissertation investigates utilization of spectrum opportunities in dynamic scenarios, where a licensed radio spectrum is available for limited time and also analyzes how it is affected by various parameters. The dissertation proposes three new methods for adaptive spectrum sensing which improve dynamic utilization of idle radio spectrum as well as the detection of active PUs in comparison to the conventional method with fixed spectrum sensing size. Moreover, this dissertation presents a new approach for optimizing cooperative spectrum sensing performance and also proposes the use of hidden Markov models (HMMs) to enabling performance awareness for local and cooperative spectrum sensing schemes, leading to improved spectrum utilization. All the contributions are illustrated with numerical results obtained from extensive simulations which confirm their effectiveness for practical applications

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Efficient Identification and Utilization of Spectrum Opportunities in Cognitive Radio Networks.

There has been an exponential increase in spectrum demands due to new emerging wireless services and applications, making it harder to find unallocated spectrum bands for future usage. This potential resource scarcity is rooted at inefficient utilization of spectrum under static spectrum allocation. Therefore, a new concept of dynamic spectrum access (DSA) has been proposed to opportunistically utilize the legacy spectrum bands by cognitive radio (CR) users. Cognitive radio is a key technology for alleviating this inefficient spectrum utilization, since it can help discover spectrum opportunities (or whitespaces) in which legacy spectrum users do not temporarily use their assigned spectrum bands. In a DSA network, it is crucial to efficiently identify and utilize the whitespaces. We address this issue by considering spectrum sensing and resource allocation. Spectrum sensing is to discover spectrum opportunities and to protect the legacy users (or incumbents) against harmful interference from the CR users. In particular, sensing is an interaction between PHY and MAC layers where in the PHY-layer signal detection is performed, and in the MAC-layer spectrum sensing is scheduled and spectrum sensors are coordinated for collaborative sensing. Specifically, we propose an efficient MAC-layer sensing scheduling algorithm that discovers spectrum opportunities as much as possible for better quality-of-service (QoS), and as fast as possible for seamless service provisioning. In addition, we propose an optimal in-band spectrum sensing algorithm to protect incumbents by achieving the detectability requirements set by regulators (e.g., FCC) while incurring minimal sensing overhead. For better utilization of discovered spectrum opportunities, we pay our attention to resource allocation in the secondary spectrum market where legacy license holders temporarily lease their own spectrum to secondary wireless service providers (WSPs) for opportunistic spectrum access by CR users. In this setting, we investigate how a secondary WSP can maximize its profit by optimally controlling the admission and eviction of its customers (i.e., CR users). In addition, we also focus on the price and quality competition between co-located WSPs where they contend for enticing customers by providing more competitive service fee while leasing the channels with best matching quality.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78741/1/hyoilkim_1.pd

Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities

An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method

Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors. © 2012 IEEE

Wireless Positioning and Tracking for Internet of Things in GPS-denied Environments

Wireless positioning and tracking have long been a critical technology for various applications such as indoor/outdoor navigation, surveillance, tracking of assets and employees, and guided tours, among others. Proliferation of Internet of Things (IoT) devices, the evolution of smart cities, and vulnerabilities of traditional localization technologies to cyber-attacks such as jamming and spoofing of GPS necessitate development of novel radio frequency (RF) localization and tracking technologies that are accurate, energy-efficient, robust, scalable, non-invasive and secure. The main challenges that are considered in this research work are obtaining fundamental limits of localization accuracy using received signal strength (RSS) information with directional antennas, and use of burst and intermittent measurements for localization. In this dissertation, we consider various RSS-based techniques that rely on existing wireless infrastructures to obtain location information of corresponding IoT devices. In the first approach, we present a detailed study on localization accuracy of UHF RF IDentification (RFID) systems considering realistic radiation pattern of directional antennas. Radiation patterns of antennas and antenna arrays may significantly affect RSS in wireless networks. The sensitivity of tag antennas and receiver antennas play a crucial role. In this research, we obtain the fundamental limits of localization accuracy considering radiation patterns and sensitivity of the antennas by deriving Cramer-Rao Lower Bounds (CRLBs) using estimation theory techniques. In the second approach, we consider a millimeter Wave (mmWave) system with linear antenna array using beamforming radiation patterns to localize user equipment in an indoor environment. In the third approach, we introduce a tracking and occupancy monitoring system that uses ambient, bursty, and intermittent WiFi probe requests radiated from mobile devices. Burst and intermittent signals are prominent characteristics of IoT devices; using these features, we propose a tracking technique that uses interacting multiple models (IMM) with Kalman filtering. Finally, we tackle the problem of indoor UAV navigation to a wireless source using its Rayleigh fading RSS measurements. We propose a UAV navigation technique based on Q-learning that is a model-free reinforcement learning technique to tackle the variation in the RSS caused by Rayleigh fading