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

In this dissertation we consider the problem of cooperative sensing for secondary user access to primary user spectrum in cognitive radio systems. Using a fusion center or an access point, the cooperative users decide on the availability of spectrum for their use. Both Neyman-Pearson and Bayes criterion are considered for performance assessment. Our work on the asymptotic performance of counting rules with a very large number of sensors in decentralized detection problem shows that majority logic fusion rule has the same order of performance when compared to the best fusion rule based on the binary decisions received from the observing sensors in a network. In cognitive radio context, very large number of sensors may not be realistic and hence we would like to examine the performance of majority logic and counting rules involving a finite and small number of sensors. Uniformly most powerful test for decentralized detection for testing parameter θ when the observation is a sample from uniform (0,θ) distribution is investigated and it is shown that OR rule has the best performance among all counting rules in error free channel. The numerical study for reporting channel as a binary symmetric channel (BSC) with probability of bit error is also investigated and the results show that 2-out-of-5 or 2-out-of-10 has better performance among other k-out-of-n rules, whenever OR rule is not able to provide a probability of false alarm at the sensor, that lies over (0,1) at a given probability of bit error.

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

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 / Abstract: The scarcity of frequency spectrum used for wireless communication systems has attracted a considerable amount of attention in recent years. The cognitive radio (CR) terminology has been widely accepted as a smart platform mainly aimed at the efficient interrogation and utilization of permitted spectrum. Non-parametric spectrum sensing, or estimation, represents one of the prominent tools that can be proposed when CR works under an undetermined environment. As such, the periodogram, filter bank, and multi-taper methods are well considered in many studies without relying on the transmission channel's characteristics. A unified approach to all these non-parametric spectrum sensing techniques is presented in this paper with analytical and performance comparison using simulation methods. Results show that the multi-taper method outperforms the others.

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.