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 cognitive radio (CR) concept is expected to be adopted along with many technologies to meet the requirements of the next generation of wireless and mobile systems, the 5G. Consequently, it is important to determine the performance of the CR systems with respect to these requirements. In this thesis, after briefly describing the 5G requirements, we present three main directions in which we aim to enhance the CR performance. The first direction is the reliability. We study the achievable rate of a multiple-input multiple-output (MIMO) relay-assisted CR under two scenarios; an unmanned aerial vehicle (UAV) one-way relaying (OWR) and a fixed two-way relaying (TWR). We propose special linear precoding schemes that enable the secondary user (SU) to take advantage of the primary-free channel eigenmodes. We study the SU rate sensitivity to the relay power, the relay gain, the UAV altitude, the number of antennas and the line of sight availability. The second direction is the scalability. We first study a multiple access channel (MAC) with multiple SUs scenario. We propose a particular linear precoding and SUs selection scheme maximizing their sum-rate. We show that the proposed scheme provides a significant sum-rate improvement as the number of SUs increases. Secondly, we expand our scalability study to cognitive cellular networks. We propose a low-complexity algorithm for base station activation/deactivation and dynamic spectrum management maximizing the profits of primary and secondary networks subject to green constraints. We show that our proposed algorithms achieve performance close to those obtained with the exhaustive search method. The third direction is the energy efficiency (EE). We present a novel power allocation scheme based on maximizing the EE of both single-input and single-output (SISO) and MIMO systems. We solve a non-convex problem and derive explicit expressions of the corresponding optimal power. When the instantaneous channel is not available, we present a simple sub-optimal power that achieves a near-optimal EE. The simulations show that the sub-optimal solution is very close to the optimal one. In the MIMO case, we show that adopting more antennas is more energy efficient.

Wireless communication systems have evolved substantially over the last two decades. The explosive growth of the wireless communications market is expected to continue in the future, as the demand for all types of wireless services is increasing. Beside providing higher data rates, next generation wireless networks (NGWN) are expected to have advanced capabilities such as interoperability, efficient spectrum utilization along with a wide variety of applications over different domains (e.g., public safety and military, aeronautical networks, femtocells, and so on) to the mobile users while serving as many users as possible. However, these advanced capabilities and services must be achieved under the constraint of limited available resources such as electromagnetic spectrum and power. In addition, NGWNs (and nodes within) need to modify themselves under rapidly changing conditions such as wireless propagation channel characteristics, traffic load, and so on. Moreover, NGWNs are expected to optimize their parameters by evaluating their experiences in the past. All of these characteristics imply that NGWNs should be equipped with cognitive capabilities including sensing, awareness, adaptation and responding to changing conditions along with learning about the past experiences. In this dissertation, environment, channel, and interference awareness are investigated in detail for NGWN. Methods for being aware of environment, channel, and interference are provided along with some possible ways of adapting several design parameters of NGWNs. In addition, cross-layer optimization issues are addressed from the perspective of both recently emerging technology called cognitive radio (CR) and NGWN.

In the last decade, cellular networks have been characterized by an ever-growing user data demand. This caused increasing capacity shortfall and coverage issues, aggravated by inefficient fixed spectrum management policies and obsolete network structures. From a practical point of view, novel technical and architectural solutions have been proposed to frame next generation cellular networks, capable of meeting the identified target performance to satisfy the user data demands. Specifically, new spectrum management policies based on the so-called dynamic spectrum access (DSA), together with hierarchical approaches to network planning, where a tier of macro base stations is underlaid with a tier of massively deployed low-power small base stations, are seen as promising candidates to achieve this scope. The resulting two-tiered network layout may improve the capacity of current networks in several ways, thanks to a better average link quality between the devices, a more efficient usage of spectrum resources and a potentially higher spatial reuse. In this thesis, we focus on the challenging problem arising when the two tiers share the transmit band, to capitalize on the available spectrum and avoid possible inefficiencies. In this case, the coexistence of the two tiers is not feasible, if suitable interference management techniques are not designed to mitigate/cancel the mutual interference generated by the active transmitters in the network. This thesis is divided in three main parts, and proposes a rather exhaustive approach to the development of new DSA and interference management techniques, to go from the theoretical basis up to a proof-of-concept development.

The aggregate interference distribution in cognitive radio networks is studied in a rigorous and analytical way using the popular Poisson point process model. While a number of results are available for this model for non-cognitive radio networks, cognitive radio networks present extra levels of difficulties for the analysis, mainly due to the exclusion region around the primary receiver, which are typically addressed via various ad-hoc approximations (e.g., based on the interference cumulants) or via the large-deviation analysis. Unlike the previous studies, we do not use here ad-hoc approximations but rather obtain the asymptotic interference distribution in a systematic and rigorous way, which also has a guaranteed level of accuracy at the distribution tail. This is in contrast to the large deviation analysis, which provides only the (exponential) order of scaling but not the outage probability itself. Unlike the cumulant-based analysis, our approach provides a guaranteed level of accuracy at the distribution tail. Additionally, our analysis provides a number of novel insights. In particular, we demonstrate that there is a critical transition point below which the outage probability decays only polynomially but above which it decays super-exponentially. This provides a solid analytical foundation to the earlier empirical observations in the literature and also reveals what are the typical ways outage events occur in different regimes. The analysis is further extended to include interference cancelation and fading (from a broad class of distributions). The outage probability is shown to scale down exponentially in the number of canceled nearest interferers in the below-critical region and does not change significantly in the above-critical one. The proposed asymptotic expressions are shown to be accurate in the non-asymptotic regimes as well.

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.

Our research aims at contributing to the evolution of modern wireless communications and to the development of software-defined radio (SDR) and cognitive radio, in particular. It promotes a general resource management framework that facilitates the integration of computing and radio resource management. This dissertation discusses the need for computing resource management in software-defined and cognitive radios and introduces an SDR computing resource management framework with cognitive capabilities. The hard real-time computing requirements of software-defined digital signal processing chains (SDR applications), the associated radio propagation and quality of service (QoS) implications, and heterogeneous multiprocessor platforms with limited computing resources (SDR platforms) define the context of these studies. We examine heterogeneous computing techniques, multiprocessor mapping and scheduling in particular, and elaborate a flexible framework for the dynamic allocation and reallocation of computing resources for wireless communications. The framework should facilitate partial reconfigurations of SDR platforms, dynamic switches between radio access technologies (RATs), and service and QoS level adjustments as a function of the environmental conditions. It, therefore, assumes the facilities of the platform and hardware abstraction layer operating environment (P-HAL-OE). We suggest a modular framework, distinguishing between the computing system modeling and the computing resource management. Our modeling proposal is based on two computing resource management techniques, which facilitate managing the strict timing constraints of real-time systems. It is scalable and can account for many different hardware architectures and computing resource types. This work focuses on processing and interprocessor bandwidth resources and processing and data flow requirements. Our computing resource management approach consists of a general-purpose mapping algorithm and a cost function. The independence between the algorithm and the cost function facilitates implementing many different computing resource management policies. We introduce a dynamic programming based algorithm, the tw-mapping, where w controls the decision window. We present a general and parametric cost function, which guides the mapping process under the given resource constraints. An instance of it facilitates finding a mapping that meets all processing and data flow requirements of SDR applications with the available processing and bandwidth resources of SDR platforms. Several SDR reconfiguration scenarios and analyses based on simulations demonstrate the suitability and potentials of our framework for a flexible computing resource management. We extend our SDR computing resource management concepts to the cognitive radio context. The two primary objectives of cognitive radio are highly reliable communications whenever and wherever needed and the efficient use of the radio spectrum. We formulate a third objective as the efficient use of computing resources. We analyze the cognitive capabilities of our framework─the cognitive radio’s interface to SDR platforms─and indicate the potentials of our cognitive computing resource management proposal. The cognitive computing resource management needs to be coordinated with the radio resource management. We, therefore, introduce the joint resource management concept for cognitive radios. We present three cognitive cycles and discuss several interrelations between the radio, computing, and application resources, where application resources refer to the available SDR and user applications. Our approach potentiates flexibility and facilitates radio against computing resource tradeoffs. It promotes cognition at all layers of the wireless system for a cooperative or integrated resource management that may increase the performance and efficiency of wireless communications. / El objetivo de las investigaciones que se están llevando a cabo dentro del grupo de investigación es contribuir a la evolución de las radiocomunicaciones modernas y, en particular, al desarrollo de los conceptos software radio (SDR) y cognitive radio. El planteamiento general es el de extender la flexibilidad global del sistema de comunicaciones planteando la definición y desarrollo de un entorno en el que pudiesen explorarse las relaciones entre la computación y las prestaciones del sistema de comunicaciones móviles facilitando la integración de los recursos de computación con los recursos radio. Dentro de este marco, la presente tesis plantea la discusión de la necesidad de la gestión de los recursos de computación en entornos SDR y cognitive radio y define un entorno de operación que asume las características especificas del concepto SDR a la vez que incorpora capacidades cognitivas en la gestión de los recursos de computación de las plataformas que den soporte a las nuevas generaciones de sistemas móviles. Los estrictos requerimientos de procesado en tiempo real de las cadenas de procesado digital de la señal definidas por software (aplicaciones SDR), las implicaciones asociadas con la propagación radio y el concepto de calidad de servicio (QoS) y plataformas heterogéneas de múltiples procesadores con recursos de computo limitados (plataformas SDR) definen el contexto de estos estudios. Se examinan técnicas de cómputo de propósito general para definir un entorno de operación que fuese capaz de asignar de forma flexible y dinámica los recursos de cómputo necesarios para facilitar las radiocomunicaciones a los niveles de QoS deseados. Ello debería facilitar los cambios dinámicos de una tecnología de acceso radio a otra, permitiendo el ajuste del tipo de servicio o calidad de servicio en función de las preferencias de los usuarios y las condiciones del entorno. Dicho entorno de operación asume las potencialidades del platform and hardware abstraction layer operating environment (P-HAL-OE). La estructura del entorno de operación se define de forma modular y consiste en un modelado genérico y flexible de las plataformas de computación SDR y en una gestión de recursos de computación abierta y capaz de ajustarse a diferentes objetivos y políticas. En el trabajo se exponen dos técnicas de gestión que pretenden asegurar la consecución estricta de los límites temporales típicos de los sistemas en tiempo real. En cuanto al modelado, este es escalable y capaz de capturar un amplio abanico de arquitecturas hardware y recursos de computación. En el presente trabajo nos centramos en los recursos y requerimientos del procesado y transferencia de datos. Se introduce un algoritmo de mapeo genérico e independiente de la función de coste. La independencia entre el algoritmo y la función de coste facilita la implementación de diferentes políticas de gestión de recursos computacionales. El tw-mapping es un algoritmo basado en dynamic programming, donde w controla la ventana de decisión. Se presenta una función de coste genérica y parametrizable que permite guiar el proceso de gestión de los recursos. Una instancia de ella facilita encontrar una solución al proceso de asignación de recursos que cumpla todos los requerimientos de procesado y trasferencia de datos de las aplicaciones SDR con los recursos disponibles de las plataformas SDR. Diferentes escenarios y varios análisis basados en simulaciones demuestran la adecuación del entorno de trabajo definido y desarrollado, así como sus potencialidades para una gestión flexible de los recursos de cómputo. Se extienden los conceptos mencionados previamente para entornos cognitive radio. Los principales objetivos del concepto cognitive radio son la disponibilidad de comunicaciones altamente robustas en cualquier lugar y momento en que sean necesarias y el uso eficiente del espectro. Como tercer objetivo formulamos el uso eficiente de los recursos de cómputo. Analizamos las capacidades cognitivas de nuestro entorno de operación─la interfaz del sistema cognitive radio a las plataformas SDR─y resaltamos las potencialidades de nuestra propuesta de gestión cognitiva de los recursos computacionales. Dicha gestión cognitiva de los recursos computacionales plantea una integración con la gestión de los recursos radio. Para ello introducimos el concepto de gestión de recursos conjunta para entornos cognitive radio. Se presentan tres ciclos cognitivos y se discuten algunas interrelaciones entre los recursos radio, de cómputo y de aplicación, donde los recursos de aplicación se refieren a las aplicaciones SDR y de usuario disponibles. Nuestra propuesta de gestión de recursos conjunta potencia la flexibilidad y facilita los intercambios entre recursos radio y de computación

Radio Communication technologies are undergoing drastic demand over the past two decades. The precious radio resource, electromagnetic radio spectrum, is in vain as technology advances. It is required to come up with a solution to improve its wise uses. Cognitive Radio enabled by Software-Defined Radio brings an intelligent solution to efficiently use the Radio Spectrum. It is a method to aware the radio communication system to be able to adapt to its radio environment like signal power and free spectrum holes. The approach will pose a question on how to efficiently detect a signal. In this thesis different spectrum sensing algorithm will be explained and a special concentration will be on new sensing algorithm based on the Eigenvalues of received signal. The proposed method adapts blind signal detection approach for applications that lacks knowledge about signal, noise and channel property. There are two methods, one is ratio of the Maximum Eigenvalue to Minimum Eigenvalue and the second is ratio of Signal Power to Minimum Eigenvalue. Random Matrix theory (RMT) is a branch of mathematics and it is capable in analyzing large set of data or in a conclusive approach it provides a correlation points in signals or waveforms. In the context of this thesis, RMT is used to overcome both noise and channel uncertainties that are common in wireless communication. Simulations in MATLAB and real-time measurements in LabVIEW are implemented to test the proposed detection algorithms. The measurements were performed based on received signal from an IF-5641R Transceiver obtained from National Instruments.