Current wireless communications are confronted with two significant problems with regard to spectrum use --- spectrum scarcity and deployment difficulties. It is widely believed that Software Defined Radios (SDRs) and Cognitive Radios (CRs) are the key enabling technologies to address these problems. The reconfigurability of SDRs combined with the decoupling of policies and the platform in policy-based radios poses a new technical problem --- viz, enforcing policy conformance. Each DARPA XG radio is equipped with a set of policy conformance components (PCCs) which are responsible for ensuring that the radio is policy-conformant and does not cause harmful interference. The Policy Reasoner (PR) is the inference component of the PCCs whereas the Policy Enforcer (PE) performs enforcement. DARPA's XG program prescribes the Software Communications Architecture (SCA) as the model for SDR/CR architectures. Distributed processing is a fundamental aspect of the SCA, and it uses the Common Object Resource Broker Architecture (CORBA). It is reasonable to assume that some of the SDRs will be implemented as distributed systems, irrelevant of whether they are SCA compliant devices. It is thus obvious that middleware has to be secured for complete security. This thesis enumerates the requirements of an "ideal" PE. We have described the design and implementation of two possible implementations, which can fulfill some of these requirements. The PE can function similar to a firewall, and be at the very boundary of software and hardware components. The PE can also be implemented as a "man-in-the-middle" between the System Strategy Reasoner and the transmission hardware. We further describe a novel method of providing cache coherency for a cache-based PE. We also perform an in-depth analysis of the security requirements in a distributed implementation of a policy-based radio. To this end, we describe the design and implementation of such a system using CORBA middleware. We identify potential vulnerabilities due to the use of CORBA, and describe countermeasures for them. We compare the performance of transport and security mechanisms of two commercial, off-the-shelf (COTS) Object Request Brokers. We show that the magnitude of performance degradation can be reduced by the use of a cleverly selected combination of transport and security mechanisms. / Master of Science

In an interference network, multiple transmitters communicate with multiple receivers using the same communication channel. The capacity region of an interference network is defined as the set of data rates that can be simultaneously achieved by the users of the network. One of the most important example of an interference network is the wireless network, where the communication channel is the wireless channel. Wireless interference networks are known to be interference limited rather than noise limited since the interference power level at the receivers (caused by other user's transmissions) is much higher than the noise power level. Most wireless communication systems deployed today employ transmission strategies where the interfering signals are treated in the same manner as thermal noise. Such strategies are known to be suboptimal (in terms of achieving higher data rates), because the interfering signals generated by other transmitters have a structure to them that is very different from that of random thermal noise. Hence, there is a need to design transmission strategies that exploit this structure of the interfering signals to achieve higher data rates. However, determining optimal strategies for mitigating interference has been a long standing open problem. In fact, even for the simplest interference network with just two users, the capacity region is unknown. In this dissertation, we will investigate the capacity region of several models of interference channels. We will derive limits on achievable data rates and design effective transmission strategies that come close to achieving the limits. We will investigate two kinds of networks - "small" (usually characterized by two transmitters and two receivers) and "large" where the number of users is large. / text

DARPA's neXt Generation (XG) communication program proposes the use of Dynamic Spectrum Access (DSA) wherein intelligent radios can realize opportunistic usage of frequency bands by identifying the under-utilized spectrum and reasoning about it. Implementing such a flexible scheme requires changes in the current static spectrum management approach. As a result, declarative spectrum management through policy-based dynamic spectrum access has garnered significant attention recently. Policy-based dynamic spectrum access decouples the Spectrum Access Policies and Policy Processing Components from the Radio Platform. The Policies define conditions under which the radios are allowed to transmit in terms of frequencies used, geographic locations, time etc. The Policy Processing Components include a reasoning engine called the Policy Reasoner, which is responsible for enforcing these policies. This thesis describes the design and implementation of a novel policy reasoner called Bi- nary Decision Diagram based Reasoner for processing Spectrum Access Policies (BRESAP). BRESAP processes spectrum policies efficiently by reframing the policy reasoning problem as a graph based Boolean function manipulation problem. BRESAP uses Binary Decision Diagrams (BDDs) to represent, analyze and process the policies. It uses a set of efficient graph-theoretic algorithms to merge these policies into a single meta-policy and compute opportunity constraints. Our policy reasoner has the capability to respond to invalid and under-specified transmission requests sent by the System Strategy Reasoner (SSR). In case of invalid or under-specified transmission requests, BRESAP returns a set of opportunity constraints which inform the SSR of the changes needed to the transmission parameters in order to make them conform to the policies. We also propose three algorithms for computing the opportunity constraints. The complexity of the first algorithm is proportional to the number of variables in the metapolicy BDD, while the complexities of the second and third algorithms are proportional to sum of number of variables and the size (i.e., number of nodes) of the meta-policy BDD. / Master of Science

This thesis extensively analyses the performance of an energy detector which is widely employed to perform spectrum sensing in cognitive radio over different generalised channel models. In this analysis, both the average probability of detection and the average area under the receiver operating characteristic curve (AUC) are derived using the probability density function of the received instantaneous signal to noise ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then, the behaviour of the energy detector over к --- µ shadowed fading channel, which is a composite of generalized multipath/shadowing fading channel to model the lineof- sight (LoS) communication medium is investigated. The analysis of the energy detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to include maximal ratio combining (MRC), square law combining (SLC) and square law selection (SLS) with independent and non-identically (i:n:d) diversity branches. To overcome the problem of mathematical intractability in analysing the energy detector over i:n:d composite fading channels with MRC and selection combining (SC), two different unified statistical properties models for the sum and the maximum of mixture gamma (MG) variates are derived. The first model is limited by the value of the shadowing severity index, which should be an integer number and has been employed to study the performance of energy detector over composite α --- µ /gamma fading channel. This channel is proposed to represent the non-linear prorogation environment. On the other side, the second model is general and has been utilised to analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel. Finally, a special filter-bank transform which is called slantlet packet transform (SPT) is developed and used to estimate the uncertain noise power. Moreover, signal denoising based on hybrid slantlet transform (HST) is employed to reduce the noise impact on the performance of energy detector. The combined SPT-HST approach improves the detection capability of energy detector with 97% and reduces the total computational complexity by nearly 19% in comparison with previously implemented work using filter-bank transforms. The aforementioned percentages are measured at specific SNR, number of selected samples and levels of signal decomposition.

In the literature, most distributed detection developed so far mainly focuses on the test rule based on fixed sample size. However, in the real situations, sequential tests are more suitable to be utilized since it might achieve the same detection performance by using fewer number of samples as compared with the fixed-sample-size test. Thus, this theses will propose a new distributed sequential detection approach for the applications in wireless sensor networks(WSNs) and cognitive radios(CRs). First we refer to the sequential detection, and it has been developed by Wald in 1994, which is well known as the sequential probability ratio test (SPRT). The SPRT is proved to be able to decrease the required average sample numbers or reducing the average detection time. Indeed, the SPRT is the optimal sequential detection in terms of the minimizing the required number of samples given the constraint of false alarm and miss probabilities when the observation samples are independent and identical distributed (i.i.d.). However, if the observation samples are not dentically distributed, by simulation results show that the SPRT is not the optimal test. Based on a heuristic approach, this thesis then developed a new distributed detection scheme based on the sorted samples. Finally , the simulation results obtained by this thesis shows that the proposed scheme can further reduce the number of samples required for making the final decision as compared with SPRT.

abstract: Cognitive Radios (CR) are designed to dynamically reconfigure their transmission and/or reception parameters to utilize the bandwidth efficiently. With a rapidly fluctuating radio environment, spectrum management becomes crucial for cognitive radios. In a Cognitive Radio Ad Hoc Network (CRAHN) setting, the sensing and transmission times of the cognitive radio play a more important role because of the decentralized nature of the network. They have a direct impact on the throughput. Due to the tradeoff between throughput and the sensing time, finding optimal values for sensing time and transmission time is difficult. In this thesis, a method is proposed to improve the throughput of a CRAHN by dynamically changing the sensing and transmission times. To simulate the CRAHN setting, ns-2, the network simulator with an extension for CRAHN is used. The CRAHN extension module implements the required Primary User (PU) and Secondary User (SU) and other CR functionalities to simulate a realistic CRAHN scenario. First, this work presents a detailed analysis of various CR parameters, their interactions, their individual contributions to the throughput to understand how they affect the transmissions in the network. Based on the results of this analysis, changes to the system model in the CRAHN extension are proposed. Instantaneous throughput of the network is introduced in the new model, which helps to determine how the parameters should adapt based on the current throughput. Along with instantaneous throughput, checks are done for interference with the PUs and their transmission power, before modifying these CR parameters. Simulation results demonstrate that the throughput of the CRAHN with the adaptive sensing and transmission times is significantly higher as compared to that of non-adaptive parameters. / Dissertation/Thesis / M.S. Computer Science 2012

Recent measurements show that the spectrum is under-utilized by licensed users in wireless communication. Cognitive radio (CR) has been proposed as a suitable solution to manage the inefficient usage of the spectrum and increase coverage area of wireless networks. The concept is based on allowing a group of secondary users (SUs) to share the unused radio spectrum originally owned by the primary user (PUs). The operation of CR should not cause harmful interference to the PUs. In the other hand, relayed transmission increases the coverage and achievable capacity of communication systems and in particular in CR systems. In fact there are many types of cooperative communications, however the two main ones are decode-and-forward (DAF) and amplify-and-forward (AAF). Adaptive relaying scheme is a relaying technique by which the benefits of the amplifying or decode and forward techniques can be achieved by switching the forwarding technique according to the quality of the signal. In this dissertation, we investigate the power allocation for an adaptive relaying protocol (ARP) scheme in cognitive system by maximizing the end-to-end rate and searching the best carriers pairing distribution. The optimization problem is under the interference and power budget constraints. The simulation results confirm the efficiency of the proposed adaptive relaying protocol in comparison to other relaying techniques, and the consequence of the choice of the pairing strategy.

This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method.  With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue.  One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech. However, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power.  This greatly limits the usable bandwidth when using complex channel sensing methods.  Therefore, this thesis focuses on energy detection for channel sensing. Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices.  The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification. / Master of Science

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