Cognitive radio (CR) is a promising technique to improve spectrum efficiency for wireless communications. This thesis focuses on the resource allocation in two kinds of CR networks (CRNs), traditional CRNs (TCRNs) and cooperative CRNs (CCRNs). In TCRNs, CR sources and destinations communicate directly. By exploring the heterogeneity among CRs, a prioritized CSMA/CA is proposed for demand-matching spectrum allocation. A distributed game is formulated and no-regret learning is adopted to solve the game. Simulation results indicate increase on the number of satisfied CRs. In CCRNs, some nodes are selected as relays to assist the communication. A two-layer auction game is proposed with the first layer performing spectrum allocation and relay formation, and the second layer performing relay allocation. These two layers interact and jointly solve the resource allocation problem. Simulation results show that, compared to counterparts, both the network throughput and convergence time can be improved.

The concept of Cognitive radio enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users. Proactive channel access has been proposed for the purpose of reducing the interference to primary users and to reduce the idle channel search delay for the secondary users. There are many methods used in the literature to model the channel state fluctuations. Based on these models the future channel states are predicted. In this thesis we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree algorithm.

In the overlay Cognitive Radio (CR) networks, the low priority Secondary Users (SUs) must constantly monitor the occupied spectrum to detect the possible appearances of the high priority Primary Users (PUs) within the same spectrum portion. On detection, the SUs must vacate the occupied spectrum portion without interfering with the PUs beyond a certain threshold duration and must opportunistically access another idle spectrum portion to guarantee their seamless communication. This mechanism is known as the spectrum handoff process. In this thesis, we first introduce a novel approach to model the CR channel which is capable of capturing a more realistic behavior of the spectrum occupancy by both user types and that is more suitable for modeling the spectrum handoff process as opposed to the existing approaches. Then using that as a base we focus on building analytical models to capture the various aspects of the spectrum handoff process in a realistic manner.

Recently, interest has been shown in cognitive radio (CR) systems since they can op- portunistically access unused spectrum bands thereby increasing usable communication capacity. Spectrum sensing has been identified as a key function to ensure that CR can detect spectrum holes. In a CR network, a fast and accurate spectrum sensing scheme is important. Spectrum sensing can be viewed as a signal detection problem. Most of the existing spectrum sensing schemes are based on fixed sample size detectors which means that their sensing time is preset and fixed. However, the work of Wald [27] showed that a detector based on sequential detection requires less average sensing time than a fixed sample size detector. In this thesis, we have applied the method of sequential detection to reduce the average sensing time. Simulation results have shown that, compared to the fixed sample size energy detector, a sequential detector can reduce sensing time by up to 85% in the AWGN channel for the same detection performance. In order to limit sensing time, especially in a fading environment, a truncated sequential detector is developed. The simulation results show that the truncated sequential detector requires less sensing time than the sequential detector, but the performance degrades due to truncation. Finally, a cooperative spectrum sensing scheme is used where each individual sensor uses a sequential detector. The combining rule used at the fusion center is a selection combining rule. Simulation results show that the proposed cooperative spectrum sensing scheme can reduce the sensing time compared to the individual spectrum sensing scheme.

Cognitive radio (CR) is a promising technique to improve spectrum efficiency for wireless communications. This thesis focuses on the resource allocation in two kinds of CR networks (CRNs), traditional CRNs (TCRNs) and cooperative CRNs (CCRNs). In TCRNs, CR sources and destinations communicate directly. By exploring the heterogeneity among CRs, a prioritized CSMA/CA is proposed for demand-matching spectrum allocation. A distributed game is formulated and no-regret learning is adopted to solve the game. Simulation results indicate increase on the number of satisfied CRs. In CCRNs, some nodes are selected as relays to assist the communication. A two-layer auction game is proposed with the first layer performing spectrum allocation and relay formation, and the second layer performing relay allocation. These two layers interact and jointly solve the resource allocation problem. Simulation results show that, compared to counterparts, both the network throughput and convergence time can be improved.

The concept of Cognitive radio enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users. Proactive channel access has been proposed for the purpose of reducing the interference to primary users and to reduce the idle channel search delay for the secondary users. There are many methods used in the literature to model the channel state fluctuations. Based on these models the future channel states are predicted. In this thesis we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree algorithm.

In the overlay Cognitive Radio (CR) networks, the low priority Secondary Users (SUs) must constantly monitor the occupied spectrum to detect the possible appearances of the high priority Primary Users (PUs) within the same spectrum portion. On detection, the SUs must vacate the occupied spectrum portion without interfering with the PUs beyond a certain threshold duration and must opportunistically access another idle spectrum portion to guarantee their seamless communication. This mechanism is known as the spectrum handoff process. In this thesis, we first introduce a novel approach to model the CR channel which is capable of capturing a more realistic behavior of the spectrum occupancy by both user types and that is more suitable for modeling the spectrum handoff process as opposed to the existing approaches. Then using that as a base we focus on building analytical models to capture the various aspects of the spectrum handoff process in a realistic manner.

With advances in communications technologies, there is a constant need for higher data rates. One possible solution to overcome this need is to allocate additional bandwidth. However, due to spectrum scarcity this is no longer feasible. In addition, the results of spectrum measurement campaigns discovered the fact that the available spectrum is under-utilized. One of the most significant solutions to solve the under- utilization of radio-frequency (RF) spectrum is the cognitive radio (CR) concept. A valid mathematical model that can be applied for most practical scenarios and also captures the random fluctuations of the spectrum is necessary. This model provides a significant insight and also a better quantitative understanding of such systems and this is the topic of this dissertation. Compact mathematical formulations that describe the realistic spectrum usage would improve the recent theoretical work to a large extent. The data generated for such models, provide a mean for a more realistic evaluation of the performance of CR systems. However, measurement based models require a large amount of data and are subject to measurement errors. They are also likely to be subject to the measurement time, location, and methodology. In the first part of this dissertation, we introduce cognitive radio networks and their role on solving the problem of under-utilized spectrum. In the second part of this dissertation, we target the random variable which accounts for the fraction of available subcarriers for the secondary users in an OFDMA based CR system. The time and location dependency of the traffic is taken into account by a non-homogenous Poisson Point Process (PPP). In the third part, we propose a comprehensive statistical model for user activity, spectrum occupancy, and system throughput in the presence of mutual interference in an OFDMA-based CR network which accounts for the sensing procedure of spectrum sensor, spectrum demand-model and spatial density of primary users, system objective for user satisfaction which is to support as many users as possible, and environment-dependent conditions such as propagation path loss, shadowing, and channel fading. In the last part of this dissertation, unlike the second and the third parts that the modeling is theoretical and based on limiting assumptions, the spectrum usage modeling is based on real data collected from an extensive measurement.

Cognitive radio (CR) paradigm is a new radio technology proposed to solve spectrum scarcity and underutilization. Central to CR is spectrum sensing (SS), which is responsible for detecting unoccupied frequencies. Since Detection techniques differ in their performance, selecting the optimal detection method to locally perform SS has received significant attention. This research work aims to enhance the reliability of local detection decisions, under low SNR, by developing a spectrum sensing that can take advantage of multiple detection techniques. This model can either select the optimal technique or make these techniques cooperate with one another to achieve better sensing performance. The model performance is measured with respect to detection and false alarm probability as well as sensing time. To develop this model, the performance of three detection techniques is evaluated and compared. Furthermore, the voting and the maximum a posteriori probability (MAP) fusion models were developed and employed to combine spectrum sensing results obtained from the three techniques. It is concluded that the cyclostationary feature detection technique is a superior detector in low SNR situations. MAP fusion model is found to be more reliable than the voting model.

This thesis focuses on the development of multiuser access schemes for spectrum sharing systems whereby secondary users that are randomly positioned over the coverage area are allowed to share the spectrum with primary users under the condition that the interference observed at the primary receiver is below a predetermined threshold. In particular, two scheduling schemes are proposed for selecting a user among those that satisfy the interference constraints and achieve an acceptable signal-to-noise ratio level above a predetermined signal-to-noise threshold at the secondary base station. The first scheme selects the user that reports the best channel quality. In order to alleviate the high feedback load required by the first scheme, a second scheme is proposed that is based on the concept of switched diversity where the base station scans the users in a sequential manner until an acceptable user is found. In addition, the proposed scheduling schemes operate under two power adaptive settings at the secondary users that are based on the amount of interference available at the secondary transmitter. In the On/Off power setting, users are allowed to transmit based on whether the interference constraint is met or not, while in the full power adaptive setting, users are allowed to vary their transmission power to satisfy the interference constraint. A special case of the proposed schemes is also analyzed whereby all the users are assumed to be at the same position, thus operating under the influence of independent and identically distributed Rayleigh fading channels. Finally, several numerical results are illustrated for the proposed algorithms where the trade-off between the average spectral efficiency and average feedback load of both schemes are shown.