Software Defined Radios (SDRs) are radio implementations in software versus the classic method of using discrete electronics. Considering the various classes of radio applications ranging from mobile-handsets to cellular base-stations, SDRs cover a wide range of power and computational needs. As a result, computing heterogeneity, in terms of Field-Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs), and General Purpose Processors (GPPs), is needed to balance the computing and power needs of such radios. Whereas SDR represents radio implementation, Cognitive Radio (CR) represents a layer of intelligence and reasoning that derives reconfiguration of an SDR to suit an application's need. Realizing CR requires a new dimension for radios, dynamically creating new radio implementations during runtime so they can respond to changing channel and/or application needs. This thesis explores the use of integrated GPP and DSP based processors for realizing SDR and CR applications. With such processors a GPP realizes the mechanism driving radio reconfiguration, and a DSP is used to implement the SDR by performing the signal processing necessary. This thesis discusses issues related to implementing radios in this computing environment and presents a sample solution for integrating both processors to create SDR-based applications. The thesis presents a sample application running on a Texas Instrument (TI) OMAP3530 processor, utilizing its GPP and DSP cores, on a platform called the Beagleboard. For the application, the Center for Wireless Telecommunications' (CWT) Public Safety Cognitive Radio (PSCR) is ported, and an Android based touch screen interface is used for user interaction. In porting the PSCR to the Beagleboard USB bandwidth and memory access latency issues were the main system bottlenecks. Latency measurements of these interfaces are presented in the thesis to highlight those bottlenecks and can be used to drive GPP/DSP based system design using the Beagleboard. / Master of Science

Recently, wireless access has become an essential part of modern society. Consequently, the demand for new wireless applications and services, as well as the number of wireless users, are gradually increasing. Given that this amount of expansion is eventually controlled by the available radio frequency spectrum, government regulatory agencies have recently adopted a strict approach to the licensing of limited amounts of spectrum to different entities (e.g., public safety, military, service providers, unlicensed devices, and TV). All of them possess exclusive transmissions to their assigned frequency channels. A new study on spectrum efficiency revealed big geographic and temporal variations in spectrum utilisation, ranging from 15-85% in the bands below 3GHz. These variations were less at frequencies above this figure. Recently, the Cognitive Radio (CR) has risen as an encouraging piece of technology to improve spectrum efficiency and to solve the problem of spectrum scarcity. This is because CR allows the secondary (unlicensed) users to occupy unused licensed spectrum bands temporarily, given that the interference of the primary (licensed) users is prohibited or minimised. In this thesis, various spectrum handoff management schemes have been proposed in order to improve the performance evaluation for CR networks. The proposed spectrum handoff schemes use the Opportunistic Spectrum Access (OSA) concept to utilise available spectrum bands. The handoff Secondary Users (SUs) have a higher priority to occupy available spectrum channels in the licensed and unlicensed spectrum bands without interfering with the legacy spectrum owner, i.e. primary users (PUs). However, existing spectrum handoff management schemes in CR networks do not provide high transmission opportunities for handoff secondary users to utilise the available radio spectrum resources. The first part of this thesis addresses the issue of spectrum handoff management in a licensed spectrum band environment. In this case, both reactive and proactive spectrum handoff schemes are proposed. Queuing theory or/and simulation experiments have been used to evaluate the performance of the proposed schemes and compare them with other existing schemes. Handoff delay has mainly been used to investigate the impact of successive handoff operations on the performance of the proposed CR networks. Implemented models have shown an improvement in the adopted performance measures. According to the achieved results, the improvement of the proposed, prioritised handoff schemes in some cases is approximately 75% when compared with existing schemes. On the other hand, the second part of this research proposed a prioritised spectrum handoff scheme in a heterogeneous spectrum environment, which is composed of a pool of licensed and unlicensed spectrum channels. In general, the availability of substantial numbers of the licensed spectrum channels is the key benefit of using this type of radio spectrum channel. Whereas, accessing with equal rights for all types of users is the main advantage of using unlicensed spectrum channels. In this respect, no transmission interruptions occur once a user obtains a channel. In addition, the proposed schemes use only the unlicensed spectrum channels as their backup channels. This enables the user to resume interrupted transmission in the case of the spectrum handoff operation (mainly; due to the appearance of the primary users), and thus facilitates a SUs communication. The proposed principle is investigated using a retrial queuing theory as well as extensive simulation experiments, and is compared with another non-prioritised scheme which do not give any preference to handoff SUs over new SUs. The results indicate that the proposed model has improved on current average handoff delay. This thesis contributes to knowledge by further enhancing the efficient utilisation of available radio spectrum resources and therefore subsequently provides an improvement in the spectrum capacity for wireless cognitive radio networks.

No / The growth of wireless devices affects the availability of limited frequencies or spectrum bands as it has been known that spectrum bands are a natural resource that cannot be added. Meanwhile, the licensed frequencies are idle most of the time. Cognitive radio is one of the solutions to solve those problems. Cognitive radio is a promising technology that allows the unlicensed users known as secondary users (SUs) to access licensed bands without making interference to licensed users or primary users (PUs). As cloud computing has become popular in recent years, cognitive radio networks (CRNs) can be integrated with cloud platform. One of the important issues in CRNs is security. It becomes a problem since CRNs use radio frequencies as a medium for transmitting and CRNs share the same issues with wireless communication systems. Another critical issue in CRNs is performance. Security has adverse effect to performance and there are trade-offs between them. The goal of this paper is to investigate the performance related to security trade-off in CRNs with supporting cloud platforms. Furthermore, Queuing Network Models with preemptive resume and preemptive repeat identical priority are applied in this project to measure the impact of security to performance in CRNs with or without cloud platform. The generalized exponential (GE) type distribution is used to reflect the bursty inter-arrival and service times at the servers. The results show that the best performance is obtained when security is disabled and cloud platform is enabled.

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In this work, we make contributions in three main areas of Cognitive Engine (CE) design for link adaptation. The three areas are CE design, CE training, and the impact of imperfect observations in the operation of the CE. First, we present a CE design for link adaptation and apply it to a system which can adapt its use of multiple antennas in addition to modulation and coding. Our design moves forward the state of the art in several ways while having a simple structure. Specifically, the CE only needs to observe the number of successes and failures associated with each set of channel conditions and communication method. From these two numbers, the CE can derive all of its functionality: estimate confidence intervals, balance exploration vs. exploitation, and utilize prior knowledge such as communication fundamentals. Finally, the CE learns the radio abilities independently of the operation objectives. Thus, if an objective changes, information regarding the radio's abilities is not lost. Second, we provide an overview of CE training, and we analytically estimate the number of trials needed to conclusively find the best performing method in a list of methods sorted by their potential performance. Furthermore, we propose the Robust Training Algorithm (RoTA) for applications where stable performance is of topmost importance. Finally, we test four key training techniques and identify and explain the three main factors that affect performance during training. Third, we assess the impact of the estimation noise on the performance of a CE. Furthermore, we derive the effect of estimation delay, in terms of the correlation between the observed SNR and the true SNR. We evaluate the effect of estimation noise and delay to the operation of the CE individually and jointly. It is found that impairments on learning make the CE more conservative in its choices leading to submaximal performance. It is found that the CE should learn using the impaired observations, if the observations are highly correlated with the actual conditions. Otherwise, it is better for the CE to learn with knowledge of the ideal conditions, if that knowledge is available. / Ph. D.

Power consumption is one of the most important aspects in mobile and wireless communications. Existing research has shown significant power reduction through limited radio reconfiguration based on the channel conditions, especially for short range sensor network applications. A cognitive radio (CR) is an intelligent wireless communication system which is able to determine the most favorable operating parameters (cognition) based on the radio environment and its own capabilities and characteristics (awareness) and reconfigure the radio accordingly (reconfigurability). This work leverages the advances in cognitive radio technology to dynamically implement favorable trade-offs in radio parameters to achieve more efficient use of radio resource (e.g., minimizing power consumption) on the required Quality of Service (QoS) of an application and channel. A CR-based approach enables us not only to adjust modulation, coding, and radiated power as in a conventional radio, but also to learn and to control component characteristics (e.g., the power amplifier (PA) efficiency characteristic) to minimize power consumption. Significant power savings using this approach are shown in this work for single input single output (SISO) systems and multiple input multiple output (MIMO) systems. This work has a broad potential impact on the research of improving power efficiency of communication systems. It establishes a cognitive radio based methodology for system power consumption optimization. It emphasizes the difference between radiated power (power radiated from the transmit antenna) and the consumed power (power drawn from the power source, such as a battery). It provides a way to connect communication (which usually cares about radiated power, received signal to noise ratio, etc.) to hardware (which focuses on speed, efficiency, power consumption, etc.) and software (which emphasizes complexity, speed, etc.). This design methodology enhances the capability to jointly optimize communication, hardware, and software. In addition, this CR-based framework can be adapted for general radio resource management with various radio operation optimization targets, such as spectrum utilization. / Ph. D.

Cognitive Radio (CR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than existing multi-channel multi-radio (MC-MR) technology. In this thesis, we investigate the important problem of multi-hop networking with CR nodes. In a CR network, each node has a set of frequency bands (not necessarily of equal size) that may not be the same as those at other nodes. The uneven size of frequency bands prompts the need of further division into sub-bands for optimal spectrum sharing. We characterize behaviors and constraints for such multi-hop CR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We give a formal mathematical formulation with the objective of maximizing the network throughput for a set of user communication sessions. Since such problem formulation falls into mixed integer non-linear programming (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and linearization. Subsequently, we develop a nearoptimal algorithm to this MINLP problem. This algorithm is based on a novel sequential fixing (SF) procedure, where the integer variables are determined iteratively via a sequence of linear program (LP). In order to implement and evaluate these algorithms in a controlled laboratory setting, we design and implement an emulation testbed. The highlights of our experimental research include: • Emulation of a multi-hop CR network with arbitrary topology; • An implementation of the proposed SF algorithm at the application layer; • A source routing implementation that can easily support comparative study between SF algorithm and other schemes; • Experiments comparing the SF algorithm with another algorithm called Layered Greedy Algorithm (LGA); • Experimental results show that the proposed SF significantly outperforms LGA. In summary, the experimental research in this thesis has demonstrated that SF algorithm is a viable algorithm for optimal spectrum sharing in multi-hop CR networks. / Master of Science