High traffic load over the unlicensed portion of the radiospectrum (a.k.a., ISM bands) along with inefficient usage of thelicensed spectrum gave impetus for a new paradigm in spectrumallocation, whose main purpose is to improve spectrum efficiencythrough opportunistic access. Cognitive radios (CRs) havebeen proposed as a key enabling technology for such paradigm.Operating a CR network (CRN) without impacting the performance oflicensed (primary) users requires new protocols for informationexchange as well as mathematical tools to optimize thecontrollable parameters of the CRN. In this dissertation, wetarget the design of such protocols. First, we develop adistributed CRN MAC (COMAC) protocol that enables unlicensed usersto dynamically utilize the spectrum while limiting theinterference they inflict on primary (PR) users. The main noveltyin COMAC lies in not assuming a predefined CR-to-PR power mask andnot requiring coordination with PR users. Second, we propose anovel distance-dependent MAC protocol for CRNs in whicheach CR is equipped with multiple transceivers. Our protocol(called DDMAC) attempts to maximize the CRN throughput byfollowing a novel probabilistic channel assignment mechanism. Thismechanism exploits the dependence between the signal's attenuationmodel and the transmission distance while considering the trafficprofile. We show that through its distance- and traffic-aware,DDMAC significantly improves network throughput. Finally, weaddress the problem of assigning channels to CR transmissions,assuming one transceiver per CR. The main goal of our design is tomaximize the CRN throughput with respect to both spectrumassignment and transmission power. Specifically, we presentcentralized and distributed solutions that leverage the uniquecapabilities of CRs. Compared with previously proposed protocols,our schemes are shown to significantly improve network throughput.

Recent survey campaigns have shown a tremendous under utilization of the bandwidth allocated to various wireless services. Motivated by this and the ever increasing demand for wireless applications, the concept of cognitive radio (CR) systems has rendered hope to end the so called spectrum scarcity. This thesis presents various different facets related to the design and analysis of CR systems in a unified way. We begin the thesis by presenting an information theoretic study of cognitive systems working in the so called low interference regime of the overlay mode. We show that as long as the coverage area of a CR is less than that of a primary user (PU) device, the probability of the cognitive terminal inflicting small interference at the PU is overwhelmingly high. We have also analyzed the effect of a key parameter governing the amount of power allocated to relaying the PU message in the overlay mode of operation in realistic environments by presenting a simple and accurate approximation. Then, we explore the possibilities of statistical modeling of the cumulative interference due to multiple interfering CRs. We show that although it is possible to obtain a closed form expression for such an interference due a single CR, the problem is particularly difficult when it comes to the total CR interference in lognormally faded environments. In particular, we have demonstrated that fitting a two or three parameter lognormal is not a feasible option for all scenarios. We also explore the second-order characteristics of the cumulative interference by evaluating its level crossing rate (LCR) and average exceedance duration (AED) in Rayleigh and Rician channel conditions. We show that the LCRs in both these cases can be evaluated by modeling the interference process with gamma and noncentral χ2 processes, respectively. By exploiting radio environment map (REM) information, we have presented two CR scheduling schemes and compared their performance with the naive primary exclusion zone (PEZ) technique. The results demonstrate the significance of using an intelligent allocation method to reap the benefits of the tremendous information available to exploit in the REM based methods. At this juncture, we divert our attention to multiple-input multiple-output (MIMO) CR systems operating in the underlay mode. Using an antenna selection philosophy, we solve a convex optimization problem accomplishing the task and show via analysis and simulations that antenna selection can be a viable option for CRs operating in relatively sparse PU environments. Finally, we study the impact of imperfect channel state information (CSI) on the downlink of an underlay multiple antenna CR network designed to achieve signal-to-interference-plus-noise ratio (SINR) fairness among the CR terminals. By employing a newly developed convex iteration technique, we solve the relevant optimization problem exactly without performing any relaxation on the variables involved.

Faced with imminent spectrum scarcity largely due to inflexible licensed band arrangements, cognitive radio (CR) has been proposed to facilitate higher spectrum utilization by allowing cognitive users (CUs) to access the licensed bands without causing harmful interference to primary users (PUs). To achieve this without the aid of PUs, the CUs have to perform spectrum sensing reliably detecting the presence or absence of PU signals. Without reliable spectrum sensing, the discovery of spectrum opportunities will be inefficient, resulting in limited utilization enhancement. This dissertation examines three major techniques for spectrum sensing, which are matched filter, energy detection, and cyclostationary feature detection. After evaluating the advantages and disadvantages of these techniques, we narrow down our research to a focus on cyclostationary feature detection (CFD). Our first contribution is to boost performance of an existing and prevailing CFD method. This boost is achieved by our proposed optimal and sub-optimal schemes for identifying best hypothesis test points. The optimal scheme incorporates prior knowledge of the PU signals into test point selection, while the sub-optimal scheme circumvents the need for this knowledge. The results show that our proposed can significantly outperform other existing schemes. Secondly, in view of multi-antenna deployment in CR networks, we generalize the CFD method to include the multi-antenna case. This requires effort to justify the joint asymptotic normality of vector-valued statistics and show the consistency of covariance estimates. Meanwhile, to effectively integrate the received multi-antenna signals, a novel cyclostationary feature based channel estimation is devised to obtain channel side information. The simulation results demonstrate that the errors of channel estimates can diminish sharply by increasing the sample size or the average signal-to-noise ratio. In addition, no research has been found that analytically assessed CFD performance over fading channels. We make a contribution to such analysis by providing tight bounds on the average detection probability over Nakagami fading channels and tight approximations of diversity reception performance subject to independent and identically distributed Rayleigh fading. For successful coexistence with the primary system, interference management in cognitive radio networks plays a prominent part. Normally certain average or peak transmission power constraints have to be placed on the CR system. Depending on available channel side information and fading types (fast or slow fading) experienced by the PU receiver, we derive the corresponding constraints that should be imposed. These constraints indicate that the second moment of interference channel gain is an important parameter for CUs allocating transmission power. Hence, we develop a cooperative estimation procedure which provides robust estimate of this parameter based on geolocation information. With less aid from the primary system, the success of this procedure relies on statistically correlated channel measurements from cooperative CUs. The robustness of our proposed procedure to the uncertainty of geolocation information is analytically presented. Simulation results show that this procedure can lead to better mean-square error performance than other existing estimates, and the effects of using inaccurate geolocation information diminish steadily with the increasing number of cooperative cognitive users.