Cognitive radio technology is a promising technology to solve the wireless spectrum scarcity problem by intelligently allowing secondary, or unlicensed, users access to the primary, licensed, users' frequency bands. Cognitive technology involves two main tasks: 1) sensing the wireless medium to assess the presence of the primary users and 2) designing secondary spectrum access techniques that maximize the secondary users' benefits while maintaining the primary users' privileged status. On the spectrum sensing side, we make two contributions. First, we maximize a utility function representing the secondary throughput while constraining the collision probability with the primary below a certain value. We optimize therein the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order for wideband primary channels. Second, we design a cooperative spectrum sensing technique termed sensing with equal gain combining whereby cognitive radios simultaneously transmit their sensing results to the fusion center over multipath fading reporting channels. The proposed scheme is shown to outperform orthogonal reporting systems in terms of achievable secondary throughput and to be robust against phase and synchronization errors. On the spectrum access side, we make four contributions. First, we design a secondary scheduling scheme with the goal of minimizing the secondary queueing delay under constraints on the average secondary transmit power and the maximum tolerable primary outage probability. Second, we design another secondary scheduling scheme based on the spectrum sensing results and the primary automatic repeat request feedback. The optimal medium access probabilities are obtained via maximizing the secondary throughput subject to constraints that guarantee quality of service parameters for the primary. Third, we propose a three-message superposition coding scheme to maximize the secondary throughput without degrading the primary rate. Cognitive relaying is employed as an incentive for the primary network. The scheme is shown to outperform a number of reference schemes such as best relay selection. Finally, we consider a network of multiple primary and secondary users. We propose a three-stage distributed matching algorithm to pair the network users. The algorithm is shown to perform close to an optimal central controller, albeit at a reduced computational complexity.

abstract: In this dissertation, I propose potential techniques to improve the quality-of-service (QoS) of real-time applications in cognitive radio (CR) systems. Unlike best-effort applications, real-time applications, such as audio and video, have a QoS that need to be met. There are two different frameworks that are used to study the QoS in the literature, namely, the average-delay and the hard-deadline frameworks. In the former, the scheduling algorithm has to guarantee that the packet's average delay is below a prespecified threshold while the latter imposes a hard deadline on each packet in the system. In this dissertation, I present joint power allocation and scheduling algorithms for each framework and show their applications in CR systems which are known to have strict power limitations so as to protect the licensed users from interference. A common aspect of the two frameworks is the packet service time. Thus, the effect of multiple channels on the service time is studied first. The problem is formulated as an optimal stopping rule problem where it is required to decide at which channel the SU should stop sensing and begin transmission. I provide a closed-form expression for this optimal stopping rule and the optimal transmission power of secondary user (SU). The average-delay framework is then presented in a single CR channel system with a base station (BS) that schedules the SUs to minimize the average delay while protecting the primary users (PUs) from harmful interference. One of the contributions of the proposed algorithm is its suitability for heterogeneous-channels systems where users with statistically low channel quality suffer worse delay performances. The proposed algorithm guarantees the prespecified delay performance to each SU without violating the PU's interference constraint. Finally, in the hard-deadline framework, I propose three algorithms that maximize the system's throughput while guaranteeing the required percentage of packets to be transmitted by their deadlines. The proposed algorithms work in heterogeneous systems where the BS is serving different types of users having real-time (RT) data and non-real-time (NRT) data. I show that two of the proposed algorithms have the low complexity where the power policies of both the RT and NRT users are in closed-form expressions and a low-complexity scheduler. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

Many different types of promising spectrum sensing algorithms for Cognitive Radio (CR) have already been developed. However, many of these algorithms lack robustness with respect to signal statistical parameters uncertainties, such as the noise variance or the shape of its distribution (often assumed to be simply Gaussian). In conjunction with the low Signal-to-Noise Ratio (SNR) requirements, this lack of robustness can often render interesting sensing algorithms impractical for real-life applications. In this thesis, we primarily focus on the impact of heavy-tail noise distributions on different CR detectors and the use of signal limiters (mostly the spatial sign function) to improve their robustness to such noise distributions. Introducing a non-linear transformation of the received signal prior to its processing by the detector fundamentally changes the signal distribution which in turn modifies the distribution of the detector statistic. In order to parametrize the detector and study its performance, it is then necessary to know the shape of the modified distribution.Three types of detectors are investigated: a generic second-order cyclic-feature detectors, a Scaled-Largest Eigenvalue (SLE) detector studied in the context of stationary time-series and a new Sequential Likelihood Ratio Test (SLRT) detector. The analysis conducted for each detector revolves around the influence of its parameters, the distribution of the detector statistic and several comparisons with similar detectors for various detection scenarios. Our results indicate that at the cost of a moderate performance loss in a Gaussian noise environment, all the detectors fitted with a signal limiter become robust to impulsive noise and noise parameters uncertainties. We provide analytical approximations for the detectors statistical distribution that allow us to use the detectors in such configurations as well as to study their performance for different signal limiters and noise distributions. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Cognitive radios is one of the hot topics for emerging and future wireless commu- nication. It has been proposed as a suitable solution for the spectrum scarcity caused by the increase in frequency demand. The concept is based on allowing unlicensed users, called cognitive or secondary users, to share the unoccupied frequency bands with their owners, called the primary users, under constraints on the interference they cause to them. The objective of our work is to propose some enhancements to cognitive radio systems while taking into account practical constraints. Cogni- tive radios requires a capability to detect spectrum holes (spectrum sensing) and a scheduling flexibility to avoid the occupied spectrum and selectively use the empty spectrum (dynamic resource allocation). Thus, the work is composed of two main parts. The first part focuses on cooperative spectrum sensing. We compute in this part the analytical performance of cooperative spectrum sensing under non identical and imperfect channels. Different schemes are considered for the cooperation between users such as hard binary, censored information, quantized, and soft information. The second part focuses on the dynamic resource allocation. We first propose low-cost re- source allocation algorithms that use location information to estimate the interference to primary users to replace absence of instantaneous channel state information. We extend these algorithms to handle practical implementation constraints such as dis- 5 crete bit-loading and collocated subcarriers allocations. We then propose a reduced dimension approach based on the grouping of subcarriers into clusters and performing the resource allocation over clusters of subcarriers instead of single subcarriers. This approach is shown to reduce the computational complexity of the algorithm with lim- ited performance loss. In addition, it is valid for a generic set of resource allocation problems in presence of co-channel interference between users.

Recent advancements in frequency-agile radio technology and dynamic spectrum access network have created a huge space for improving the utilization efficiency of wireless spectrum. Existing algorithms and protocols, however, have not taken full advantage of the new technologies due to obsolete network design ideologies inherited from conventional network design, such as static spectrum access and static channelization. In this dissertation, we propose new resource management models and algorithms that capitalize on the frequency-agility of next generation radios and the dynamic spectrum access concepts to increase the utilization efficiency of wireless spectrum. We first propose a new analytical model for Dynamic Spectrum Access (DSA) networks. Compared to previous models, the new model is able to include essential DSA mechanisms such as spectrum sensing and primary interference avoidance into solid mathematical representation and thus drastically increase the accuracy of our model. The subsequent numerical study conforms well with existing empirical studies and provides fundamental insights on the design of future DSA networks. We then take advantage of partially overlapped channel in frequency-agile radio networks and propose simple joint channel scheduling and flow routing optimization algorithm that maximizes network throughput. The model quantifies the impact of fundamental network settings, such as node density and traffic load, on the performance of partially overlapped channel based networks. We then propose a cross-layer radio resource allocation algorithm JSSRC (Joint Spectrum Sharing and end-to-end data Rate Control) that iteratively adapts a frequency-agile radio network to optimum with regard to aggregate network spectrum utilization. Subsequently, we extend JSSRC to include routing and present TRSS (joint Transport, Routing and Spectrum Sharing) to solve the much more complex joint transport, routing and spectrum sharing optimization problem. Both JSSRC and TRSS enjoy theoretical convergence and achieve optimum with appropriate scheduling algorithms. The works together strive to improve efficiency of spectrum utilization in frequency-agile radio networks. Numerical and simulation studies show the effectiveness of our designs to reduce the so-called spectrum shortage problem. / Ph. D.

Spectrum sharing is possible, but lacks R & D support for practical solutions that satisfy both the incumbent and secondary or opportunistic users. The author found a lack of an openly available framework supporting experimental research on the performance of a Spectrum Access System (SAS) and propose to build an open-source Software Defined Radio (SDR) based framework. This framework will test different dynamic spectrum scenarios in a wireless testbed. This thesis presents our Spectrum Access System prototype, discusses the design choices and trade-offs and provides a proof of concept implementation. We show that an Internet-accessible CORNET test bed provides the ideal platform for developing and testing the SAS functionality and its building blocks and offerss the hardware and software as a community resource for research and education. This design provides the necessary interfaces for researchers to develop and test their SAS-related modules, waveforms and scenarios. / Master of Science / In this information age, the number of wireless devices is growing faster than the infrastructure required to make wireless communication possible. This creates a possibility of not having enough radio spectrum to keep up with this growing demand. To alleviate this issue, there is a need to research and find more ways of efficiently utilizing the current spectrum resources available. Dynamic spectrum allocation is one way forward to archiving this goal. Frequency channels are assigned to devices based on prevailing conditions like device location and availability of channels that would cause low interference to other devices. Spectrum utilization is based on time, frequency and space with devices having the ability to hop to the best channel available. In this thesis, an open-source Spectrum Access System (SAS) was created as a platform through which dynamic spectrum allocation research can be done. The SAS is centralized management system that logs information about the prevailing spectrum usage, and in turn uses this information to dynamically allocate spectrum to devices and networks. This thesis shows how it was implemented, its current performance, and the steps that different researchers can take to add their own functionalities.

Relay-assisted cooperative communication exploits spatial diversity to combat wireless fading, and is an appealing technology for next generation wireless systems. Several relay cooperation protocols have been proposed in the literature. In amplify-and-forward (AF)relaying, which is the focus of this thesis, the relay amplifies the signal it receives from the source and forwards it to the destination. AF has been extensively studied in the literature on account of its simplicity since the relay does not need to decode the received signal. We propose a novel optimal relaying policy for two-hop AF cooperative relay systems. In this, an average power-constrained relay adapts its gain and transmit power to minimize the fading-averaged symbol error probability (SEP) at the destination. Next, we consider a generalization of the above policy in which the relay operates as an underlay cognitive radio (CR). This mode of communication is relevant because it promises to address the spectrum shortage constraint. Here, the relay adapts its gain as a function of its local channel gain to the source and destination and also the primary such that the average interference it causes to the primary receiver is also constrained. For both the above policies, we also present near-optimal, simpler relay gain adaptation policies that are easy to implement and that provide insights about the optimal policies. The SEPs and diversity order of the policies are analyzed to quantify their performance. These policies generalize the conventional fixed-power and fixed-gain AF relaying policies considered in cooperative and CR literature, and outperform them by 2.0-7.7 dB. This translates into significant energy savings at the source and relay, and motivates their use in next generation wireless systems.

Thesis (MSc.) -- University of Limpopo, 2018 / A Major concern in the past years was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this problem, cognitive radios technology was proposed and this allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SUs). Dynamic access can efficiently be achieved by making necessary adjustment to some MAC layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm or MAC protocol. Proposed in this study, is a scheme (EXGPCSA) which incorporate sensing at the MAC layer and physical layer. Energy detector was used to detect the presence of primary users (SU). A choice of how long and how often to sense the spectrum was addressed at the MAC layer. The focal point of this study was on minimizing delays in finding available channels for transmission. EXGPCSA used channel grouping technique to reduce delays. Channels were divided into two groups and arranged in descending order of their idling probabilities. Channels with higher probabilities were selected for sensing. Three network scenarios were considered wherein a group of SUs participated in sensing and sharing their spectral observations. EXGPCSA was designed such that only SUs with higher SNR were allowed to share their observations with other neighbouring SUs. This rule greatly minimized errors in sensing. The efficiency of EXGPCSA was evaluated by comparing it to another scheme called generalized predictive CSA. A statistical t-test was used to test if there is significant difference between EXGPCSA and generalized predictive CSA in terms of average throughput. A test has shown that EXGPCSA significantly performed better than generalized predictive CSA. Both schemes were simulated using MATLAB R2015a in three different network scenarios.

Wireless communications systems underwent an evolution as the voice oriented applications evolved to data and multimedia based services. Furthermore, current wireless technologies, regulations and the un- derstanding of the technology are insufficient for the requirements of future wireless systems. Along with the rapid rise at the number of users, increasing demand for more communications capacity to deploy multimedia applications entail effective utilization of communications resources. Therefore, there is a need for effective spectrum allocation, adaptive and complex modulation, error recovery, channel estimation, diversity and code design techniques to allow high data rates while maintaining desired quality of service, and reconfigurable and flexible air interface technologies for better interference and fading management. However, traditional communications system design is based on allocating fixed amounts of resources to the user and does not consider adaptive spectrum utilization. Technologies which will lead to adaptive, intelligent, and aware wireless communications systems are expected to come up with consistent methodologies to provide solutions for the capacity, interference, and reliability problems of the wireless networks. Spectrum sensing feature of cognitive radio systems are a step forward to better recognize the problems and to achieve efficient spectrum allocation. On the other hand, even though spectrum sensing can constitute a solid base to achieve the reconfigurability and awareness goals of next generation networks, a new perspective is required to benefit from the whole dimensions of the available electro hyperspace. Therefore, spectrum sensing should evolve to a more general and comprehensive awareness providing a mechanism, not only as a part of CR systems which provide channel occupancy information but also as a communication environment awareness component of dynamic spectrum access paradigm which can adapt sensing parameters autonomously to ensure robust identification and parameter estimation for the signals over the monitored spectrum. Such an approach will lead to recognition of communications opportunities in different dimensions of spectrum hyperspace, and provide necessary information about the air interfaces, access techniques and waveforms that are deployed over the monitored spectrum to accomplish adaptive resource management and spectrum access. We define multidimensional signal analysis as a methodology, which not only provides the information that the spectrum hyperspace dimension in interest is occupied or not, but also reveals the underlaying information regarding to the parameters, such as employed channel access methods, duplexing techniques and other parameters related to the air interfaces of the signals accessing to the monitored channels and more. To achieve multidimensional signal analysis, a comprehensive sensing, classification, and a detection approach is required at the initial stage. In this thesis, we propose the multidimensional signal analysis procedures under signal identification algorithms in time, frequency. Moreover, an angle of arrival estimation system for wireless signals, and a spectrum usage modeling and prediction method are proposed as multidimensional signal analysis functionalities.

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