There is a belief that radio frequencies  are running out. However, according to a report from the Federal Communications Commission (FCC) in 2002, a different story was told : At any given time and location, much of the prized spectrum lies idle. At the same time, FCC revealed the fact that, in many bands, spectrum access is a more significant problem than physical scarcity of spectrum, in large part due to legacy command-and-control regulation that limits the ability of potential spectrum users to obtain such access. Hence, as opposed to static spectrum access, dynamic spectrum access (DSA) was proposed to solve the predicament. One such DSA model propose the existence of Primary users (licensed users and Secondary users (unlicensed users). Multicarrier communication technology is adopted to enable the coexistence of PU and SU. Orthogonal Frequency Division Multiplexing (OFDM) technology has been popular for multicarrier communications. A disadvantage for OFDM in the Cognitive Radio environment is its large side lobes in the frequency domain, which is a result of single-symbol pulse duration. Filter Bank Multicarrier (FBMC) uses filters that have small side lobes to synthesize/analyze the sub-carriers so as to greatly alleviate the previous mentioned disadvantage. FMT is one FBMC technique.  Although many hardware implementations have been explored during last few decades on OFDM, few FMT hardware implementation results, especially Hardware/Software Co-design, have been presented. This paper presents a HW/SW Co-design implementation result of FMT transceiver on the Altera DE4 board. / Master of Science

Recent measurements on radio spectrum usage have revealed the abundance of underutilized bands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access. Cognitive radio based secondary networks that utilize such unused spectrum holes in the licensed band, have been proposed as a possible solution to the spectrum crisis. The idea is to detect times when a particular licensed band is unused and use it for transmission without causing interference to the licensed user. We argue that prior knowledge about occupancy of such bands and the corresponding achievable performance metrics can potentially help secondary networks to devise effective strategies to improve utilization. In this work, we use Shepard's method of interpolation to create a spectrum map that provides a spatial distribution of spectrum usage over a region of interest. It is achieved by intelligently fusing the spectrum usage reports shared by the secondary nodes at various locations. The obtained spectrum map is a continuous and differentiable 2-dimension distribution function in space. With the spectrum usage distribution known, we show how different radio spectrum and network performance metrics like channel capacity, secondary network throughput, spectral efficiency, and bit error rate can be estimated. We show the applicability of the spectrum map in solving the intra-cell channel allocation problem in centralized cognitive radio networks, such as IEEE 802.22. We propose a channel allocation scheme where the base station allocates interference free channels to the consumer premise equipments (CPE) using the spectrum map that it creates by fusing the spectrum usage information shared by some CPEs. The most suitable CPEs for information sharing are chosen on a dynamic basis using an iterative clustering algorithm. Next, we present a contention based media access control (MAC) protocol for distributed cognitive radio network. The unlicensed secondary users contend among themselves over a common control channel. Winners of the contention get to access the available channels ensuring high utilization and minimum collision with primary incumbent. Last, we propose a multi-channel, multi-hop routing protocol with secondary transmission power control. The spectrum map, created and maintained by a set of sensors, acts as the basis of finding the best route for every source destination pair. The proposed routing protocol ensures primary receiver protection and maximizes achievable link capacity. Through simulation experiments we show the correctness of the prediction model and how it can be used by secondary networks for strategic positioning of secondary transmitter-receiver pairs and selecting the best candidate channels. The simulation model mimics realistic distribution of TV stations for urban and non-urban areas. Results validate the nature and accuracy of estimation, prediction of performance metrics, and efficiency of the allocation process in an IEEE 802.22 network. Results for the proposed MAC protocol show high channel utilization with primary quality of service degradation within a tolerable limit. Performance evaluation of the proposed routing scheme reveals that it ensures primary receiver protection through secondary power control and maximizes route capacity.

Channel impulse responses collected on the Virginia Tech campus show combinations of specular multipath and diffuse scattering at LMDS frequencies. An algorithm is presented that estimates link performance based on the channel impulse response. Presented and analyzed are representative impulse responses (one is primarily specular in nature and one shows significant diffuse scattering) to show that the proposed algorithm is appropriate for analyzing channels exhibiting either of these characteristics. Monte Carlo simulations logged the sequence number of each bit error to gain an understanding of the distribution of errors over time. The results show that for these static channels the errors occur randomly rather than in bursts, leading to the conclusion that average bit-error rate statistics are appropriate for channel characterization. Zero-Forcing (Z-F) and Minimum Mean Square Error (MMSE) equalizers employed on these channels had a significant impact on the link quality. In many cases, the performance of the MMSE equalizer performed only slightly better than the Z-F equalizer. However, when deep nulls were present in the channel response, the MMSE equalizer performed significantly better. Algorithms for determining the number of taps necessary to approach an optimum equalization are presented for both types of equalizers and a '98%' rule of thumb is presented. The algorithm's role in adaptive and cognitive radio systems is discussed and two applications are presented to illustrate its utility. / Ph. D.

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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No / Eigenvalue based spectrum sensing schemes such as Maximum Minimum Eigenvalue (MME), Maximum Energy Detection (MED) and Energy with Minimum Eigenvalue (EME) have higher spectrum sensing performance without requiring any prior knowledge of Primary User (PU) signal but the decision hypothesis used in these eigenvalue based sensing schemes depends on the calculation of maximum eigenvalue from covariance matrix of measured signal. Calculation of the covariance matrix followed by eigenspace analysis of the covariance matrix is a resource intensive operation and takes overhead time during critical process of spectrum sensing. In this paper we propose a new blind spectrum sensing scheme based on the approximation of the maximum eigenvalue using state of the art results from Random Matrix Theory (RMT). The proposed sensing scheme has been evaluated through extensive simulations on wireless microphone signals and the proposed scheme shows higher probability of detection (Pd) performance. The proposed spectrum sensing also shows higher detection performance as compared to energy detection scheme and RMT based sensing schemes such as MME and EME.

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.