Cognitive Radio Networks (CRNs) are a next generation network that is expected to solve the wireless spectrum shortage problem, which is the shrinking of available wireless spectrum resources needed to facilitate future wireless applications. The first CRN standard, the IEEE 802.22, addresses this particular problem by allowing CRNs to share geographically unused TV spectrum to mitigate the spectrum shortage. Equipped with reasoning and learning engines, cognitive radios operate autonomously to locate unused channels to maximize its own bandwidth and Quality-of-Service (QoS). However, their increased capabilities over traditional radios introduce a new dimension of security threats. In an NSF 2009 workshop, the FCC raised the question, “What authentication mechanisms are needed to support cooperative cognitive radio networks? Are reputation-based schemes useful supplements to conventional Public Key Infrastructure (PKI) authentication protocols?” Reputation-based schemes in cognitive radio networks are a popular technique for performing robust and accurate spectrum sensing without any inter-communication with licensed networks, but the question remains on how effective they are at satisfying the FCC security requirements. Our work demonstrates that trust-based Cooperative Spectrum Sensing (CSS) protocols are vulnerable to rogue signals, which creates the illusion of inside attackers and raises the concern that such schemes are overly sensitive Intrusion Detection Systems (IDS). The erosion of the sensor reputations in trust-based CSS protocols makes CRNs vulnerable to future attacks. To counter this new threat, we introduce community detection and cluster analytics to detect and negate the impact of rogue signals on sensor reputations.

To support the rapidly increasing number of mobile users and mobile multimedia services, and the related demands for bandwidth, wireless communication technology is facing a potentially scarcity of radio spectrum resources. However, spectrum measurement campaigns have shown that the shortage of radio spectrum is due to inefficient usage and inflexible spectrum allocation policies. Thus, to be able to meet the requirements of bandwidth and spectrum utilization, spectrum underlay access, one of the techniques in cognitive radio networks (CRNs), has been proposed as a frontier solution to deal with this problem. In a spectrum underlay network, the secondary user (SU) is allowed to simultaneously access the licensed frequency band of the primary user (PU) as long as the interference caused by the SU to the PU is kept below a predefined threshold. By doing so, the spectrum utilization can be improved significantly. Moreover, the spectrum underlay network is not only considered as the least sophisticated in implementation, but also can operate in dense areas where the number of temporal spectrum holes is small. Inspired by the above discussion, this thesis provides a performance analysis of spectrum underlay networks which are subject to interference constraints. The thesis is divided into an introduction part and five parts based on peer-reviewed international research publications. The introduction part provides the reader with an overview and background on CRNs. The first part investigates the performance of secondary networks in terms of outage probability and ergodic capacity subject to the joint outage constraint of the PU and the peak transmit power constraint of the SU. The second part evaluates the performance of CRNs with a buffered relay. Subject to the timeout probability constraint of the PU and the peak transmit power constraint of the SU, system performance in terms of end-to-end throughput, end-to-end transmission time, and stable transmission condition for the relay buffer is studied. The third part analyzes a cognitive cooperative radio network under the peak interference power constraint of multiple PUs with best relay selection. The obtained results readily reveal insights into the impact of the number of PUs, channel mean powers of the communication and interference links on the system performance. The fourth part studies the delay performance of CRNs under the peak interference power constraint of multiple PUs for point-to-point and point-to-multipoint communications. A closedform expression for outage probability and an analytical expression for the average waiting time of packets are obtained for point-to-point communications. Moreover, the outage probability and successful transmission probability for packets in point-to-multipoint communications are presented. Finally, the fifth part presents work on the performance analysis of a spectrum underlay network for a general fading channel. A lower bound on the packet timeout probability and the average number of transmissions per packet are obtained for the secondary network.

A Research Report submitted to the Faculty of Engineering and the Built Environment, University of Witwatersrand, in the partial fulfilment of the requirements for the degree of Master of Science in Engineering Johannesburg, 2015. / Due to the enormous demand for multimedia services which relies hugely on the availability of spectrum, service providers and technologist are devising a means or method which is able to fully satisfy these growing demands. The availability of spectrum to meet these demands has been a lingering issue for the past couple of years. Many would have it tagged as spectrum scarcity but really the main problem is not how scarce the spectrum is but how efficiently allocated to use is the spectrum. Once such inefficiency is tackled effectively, then we are a step closer in meeting the enormous demands for uninterrupted services. However, to do so, there are techniques or methodologies being developed to aid in the efficient management of spectrum. In this research project, two methodologies were considered and the efficiency of these methodologies in the areas of spectrum management. The Geo-location Spectrum Database (GLSD) which is the most adopted technique and the Cognitive radio spectrum sensing technique are currently the available techniques in place. The TV whitespaces (TVWS) was explored using both techniques and certain comparison based on performances; implementation, practicability, cost and flexibility were used as an evaluation parameter in arriving at a conclusion. After accessing both methodologies, conclusions were deduced on the preferred methodology and how its use would efficiently solve the issues encountered in spectrum management

Natural and manmade noise signals tend to exhibit impulsive behaviors. Therefore modeling those signals as α-stable processes is better suited towards the development of a practical spectrum sensing scheme. However, the performances of detectors operating in an α-stable noise environment are difficult to evaluate. This is because an α-stable random variable can usually only be modeled by the characteristic function since closed-form expressions are usually not available except for the special values of the characteristic exponent that correspond to the Cauchy and Gaussian noise distributions. In this thesis, we derive a general closed-form expression for the probability density function (PDF) of symmetric alpha stable processes having rational characteristic exponent (0<α≤2). Consequently, we obtain analytical expressions for the PDF and corresponding complementary cumulative distribution function (CCDF) of the proposed fractional lower order moment (FLOM) detector. Utilizing false alarm and detection probabilities, the performance analysis of the proposed spectrum sensing scheme is conducted with the assumption that the cognitive radio (CR) users are operating in non-fading channels. We validate the analytical results with Monte Carlo simulations. The effect of the distribution parameters on the receiver operating characteristic (ROC) curves is verified. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

The sky-rocketing growth of multimedia infotainment applications and broadband-hungry mobile devices exacerbate the stringent demand for ultra high data rate and more spectrum resources. Along with it, the unbalanced temporal and geographical variations of spectrum usage further inspires those spectral-efficient networks, namely, cognitive radio and heterogeneous cellular networks (HCNs). This thesis focuses on the system design and performance enhancement of cognitive radio (CR) and HCNs. Three different aspects of performance improvement are considered, including link reliability of cognitive radio networks (CNs), security enhancement of CNs, and energy efficiency improvement of CNs and HCNs. First, generalized selection combining (GSC) is proposed as an effective receiver design for interference reduction and reliability improvement of CNs with outdated CSI. A uni- ed way for deriving the distribution of received signal-to-noise ratio (SNR) is developed in underlay spectrum sharing networks subject to interference from the primary trans- mitter (PU-Tx) to the secondary receiver (SU-Rx), maximum transmit power constraint at the secondary transmitter (SU-Tx), and peak interference power constraint at the PU receiver (PU-Rx), is developed. Second, transmit antenna selection with receive generalized selection combining (TAS/GSC) in multi-antenna relay-aided communica- tion is introduced in CNs under Rayleigh fading and Nakagami-m fading. Based on newly derived complex statistical properties of channel power gain of TAS/GSC, exact ergodic capacity and high SNR ergodic capacity are derived over Nakagami-m fading. Third, beamforming and arti cial noise generation (BF&AN) is introduced as a robust scheme to enhance the secure transmission of large-scale spectrum sharing networks with multiple randomly located eavesdroppers (Eves) modeled as homogeneous Poisson Point Process (PPP). Stochastic geometry is applied to model and analyze the impact of i BF&AN on this complex network. Optimal power allocation factor for BF&AN which maximizes the average secrecy rate is further studied under the outage probability con- straint of primary network. Fourth, a new wireless energy harvesting protocol is proposed for underlay cognitive relay networks with the energy-constrained SU-Txs. Exact and asymptotic outage probability, delay-sensitive throughput, and delay-tolerant through- put are derived to explore the tradeoff between the energy harvested from the PU-Txs and the interference caused by the PU-Txs. Fifth, a harvest-then-transmit protocol is proposed in K-tier HCNs with randomly located multiple-antenna base stations (BSs) and single antenna mobile terminals (MTs) modeled as homogeneous PPP. The average received power at MT, the uplink (UL) outage probability, and the UL average ergodic rate are derived to demonstrate the intrinsic relationship between the energy harvested from BSs in the downlink (DL) and the MT performance in the UL. Throughout the thesis, it is shown that link reliability, secrecy performance, and energy efficiency of CNs and HCNs can be signi cantly leveraged by taking advantage of multiple antennas, relays, and wireless energy harvesting.

Advances in wireless technology have sparked a plethora of mobile communication standards to support a variety of applications. FCC predicts a looming crisis due to the exponentially growing demand for spectrum and it recommends to increase the efficiency of spectrum utilization. Cognitive Radio (CR) is envisioned as a radio technology which detects and exploits empty spectrum to improve the quality of communication. Spectrum analyzer for detecting spectrum holes is a key component required for implementing cognitive radio. Mitola's vision of using an RF Analog-to-Digital (ADC) to digitize the entire spectrum is not yet a reality. The traditional spectrum analysis technique based on a RF Front end using an LO Sweep is too slow, making it unsuitable to track fast hopping signals. In this work, we demonstrate an RF Frontend that can simplify the ADC's requirement by splitting the input spectrum into multiple channels. It avoids the problem of PLL settling by incorporating LO synthesis within the signal path using a concept called Iterative Down Converter. An example 0.75GHz-11.25GHz RF Channelizer is designed in 65nm Standard CMOS Process. The channelizer splits the input spectrum (10.5GHz bandwidth) into seven channels (each of bandwidth 1.5GHz). The channelizer shows the ability to rapidly switch from one channel to another (within a few ns) as well as down-converting multiple channels simultaneously (concurrency). The channelizer achieves a dynamic range of 54dB for a bandwidth of 10.5GHz, while consuming 540mW of power. Harmonic rejection mixer plays a key role in a broadband receiver. A novel order scalable harmonic rejection mixer architecture is described in this research. A proof-of-principle prototype has been designed and fabricated in a 45nm SOI technology. Experimental results demonstrate an operation range of 0.5GHz to 1.5GHz for the LO frequency while offering harmonic rejection better than 55dB for the 3rd harmonic and 58dB for the 5th harmonic across LO frequencies. While cognitive radio solves the spectrum efficiency problem in frequency domain, the electronic beam steering provides a spatial domain solution. Electronic beam forming using phased arrays have been claimed to improve spectrum efficiency by serving more number of users for a given bandwidth. A LO path phase-shifter with frequency-doubling is demonstrated for WiMAX applications.

Wireless systems have become an essential part of every sector of the national and global economy. In addition to existing commercial systems including GPS, mobile cellular, and WiFi communications, emerging systems like video over wireless, the Internet of Things, and machine-to-machine communications are expected to increase mobile wireless data traffic by several orders of magnitude over the coming decades, while natural resources like energy and radio spectrum remain scarce. The projected growth of the number of connected nodes into the trillions in the near term and increasing user demand for instantaneous, over-the-air access to large volumes of content will require a 1000-fold increase in network wireless data capacity by 2020. Spectrum is the lifeblood of these future wireless networks and the ’data storm’ driven by emerging technologies will lead to a pressing ’artificial’ spectrum scarcity. Cognitive radio is a paradigm proposed to overcome the existing challenge of underutilized spectrum. Emerging cognitive radio systems employing multi-tiered, shared-spectrum access are expected to deliver superior spectrum efficiency over existing scheduled-access systems; they have several device categories (3 or more tiers) with different access privileges. We focus on lower tiered ’smart’ devices that evaluate the spectrum dynamically and opportunistically use the underutilized spectrum. These ’smart’ devices require spectrum sensing for incumbent detection and interferer avoidance. Incumbent detection will rely on database lookup or narrowband high-sensitivity sensing. Integrated interferer detectors, on the other hand, need to be fast, wideband, and energy efficient, while requiring only moderate sensitivity. These future 'smart' devices operating in small cell environments will need to rapidly (in 10s of μs) detect a few (e.g. 3 to 6) strong interferers within roughly a 1GHz span and accordingly reconfigure their hardware resources or request adjustments to their wireless connection consisting of primary and secondary links in licensed and unlicensed spectrum. Compressive sampling (CS), an evolutionary sensing/sampling paradigm that changes the perception of sampling, has been extensively used for image reconstruction. It has been shown that a single pixel camera that exploits CS has the ability to obtain an image with a single detection element, while measuring the image fewer times than the number of pixels with the prior assumption of sparsity. We exploited CS in the presented works to take a ’snapshot’ of the spectrum with low energy consumption and high frequency resolutions. Compressive sampling is applied to break the fixed trade-off between scan time, resolution bandwidth, hardware complexity, and energy consumption. This contrasts with traditional spectrum scanning solutions, which have constant energy consumption in all architectures to first order and a fixed trade-off between scan time and resolution bandwidth. Compressive sampling enables energy-efficient, rapid, and wideband spectrum sensing with high frequency resolutions at the expense of degraded instantaneous dynamic range due to the noise folding. We have developed a quadrature analog-to-information converter (QAIC), a novel CS rapid spectrum sensing technique for band-pass signals. Our first wideband, energy-efficient, and rapid interferer detector end-to-end system with a QAIC senses a wideband 1GHz span with a 20MHz resolution bandwidth and successfully detects up to 3 interferers in 4.4μs. The QAIC offers 50x faster scan time compared to traditional sweeping spectrum scanners and 6.3x the compressed aggregate sampling rate of traditional concurrent Nyquist-rate approaches. The QAIC is estimated to be two orders of magnitude more energy efficient than traditional spectrum scanners/sensors and one order of magnitude more energy efficient than existing low-pass CS spectrum sensors. We implemented a CS time-segmented quadrature analog-to-information converter (TS-QAIC) that extends the physical hardware through time segmentation (e.g. 8 physical I/Q branches to 16 I/Q through time segmentation) and employs adaptive thresholding to react to the signal conditions without additional silicon cost and complexity. The TS-QAIC rapidly detects up to 6 interferers in the PCAST spectrum between 2.7 and 3.7GHz with a 10.4μs sensing time for a 20MHz RBW with only 8 physical I/Q branches while consuming 81.2mW from a 1.2V supply. The presented rapid sensing approaches enable system scaling in multiple dimensions such as ADC bits, the number of samples, and the number of branches to meet user performance goals (e.g. the number of detectable interferers, energy consumption, sensitivity and scan time). We envision that compressive sampling opens promising avenues towards energy-efficient and rapid sensing architectures for future cognitive radio systems utilizing multi-tiered, shared spectrum access.

Submitted in fulfilment of the academic requirements for the degree of Doctor of Philosophy (Ph.D.) in Engineering, in the School of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, at the University of the Witwatersrand, Johannesburg, South Africa, 2017 / The continuous evolution of wireless communications technologies has increasingly imposed a burden on the use of radio spectrum. Due to the proliferation of new wireless networks applications and services, the radio spectrum is getting saturated and becoming a limited resource. To a large extent, spectrum scarcity may be a result of deficient spectrum allocation and management policies, rather than of the physical shortage of radio frequencies. The conventional static spectrum allocation has been found to be ineffective, leading to overcrowding and inefficient use. Cognitive radio (CR) has therefore emerged as an enabling technology that facilitates dynamic spectrum access (DSA), with a great potential to address the issue of spectrum scarcity and inefficient use. However, provisioning of reliable and robust communication with seamless operation in cognitive radio networks (CRNs) is a challenging task. The underlying challenges include development of non-intrusive dynamic resource allocation (DRA) and optimization techniques. The main focus of this thesis is development of adaptive channel assembling (ChA) and DRA schemes, with the aim to maximize performance of secondary user (SU) nodes in CRNs, without degrading performance of primary user (PU) nodes in a primary network (PN). The key objectives are therefore four-fold. Firstly, to optimize ChA and DRA schemes in overlay CRNs. Secondly, to develop analytical models for quantifying performance of ChA schemes over fading channels in overlay CRNs. Thirdly, to extend the overlay ChA schemes into hybrid overlay and underlay architectures, subject to power control and interference mitigation; and finally, to extend the adaptive ChA and DRA schemes for multiuser multichannel access CRNs. Performance analysis and evaluation of the developed ChA and DRA is presented, mainly through extensive simulations and analytical models. Further, the cross validation has been performed between simulations and analytical results to confirm the accuracy and preciseness of the novel analytical models developed in this thesis. In general, the presented results demonstrate improved performance of SU nodes in terms of capacity, collision probability, outage probability and forced termination probability when employing the adaptive ChA and DRA in CRNs. / CK2018

The prime wireless spectrum is inherently a critical yet scarce resource. As the demand of wireless bandwidth grows exponentially, it becomes a crucial issue to improve the spectrum utilization for the development and deployment of any new wireless technologies. In this thesis, we seek to address this problem through cooperative diversity and dynamic spectrum trading, in the context of the envisioned primary-secondary dynamic spectrum sharing paradigm. For an OFDMA-based cellular primary network which owns an exclusive right to access a certain spectrum band, we propose XOR-assisted cooperative diversity to improve the spectral efficiency of the allocated band, as well as an optimization framework to address the resource allocation problem. For the secondary network that utilizes cognitive radios to opportunistically exploit the spectrum white spaces, we establish a spectrum secondary market, design the market institution based on double auctions, and solve the decision making problem using reinforcement learning, to improve spectrum utilization via trading among secondary users.

The prime wireless spectrum is inherently a critical yet scarce resource. As the demand of wireless bandwidth grows exponentially, it becomes a crucial issue to improve the spectrum utilization for the development and deployment of any new wireless technologies. In this thesis, we seek to address this problem through cooperative diversity and dynamic spectrum trading, in the context of the envisioned primary-secondary dynamic spectrum sharing paradigm. For an OFDMA-based cellular primary network which owns an exclusive right to access a certain spectrum band, we propose XOR-assisted cooperative diversity to improve the spectral efficiency of the allocated band, as well as an optimization framework to address the resource allocation problem. For the secondary network that utilizes cognitive radios to opportunistically exploit the spectrum white spaces, we establish a spectrum secondary market, design the market institution based on double auctions, and solve the decision making problem using reinforcement learning, to improve spectrum utilization via trading among secondary users.