Due to the ever increasing dependency on existing wireless technologies and the growing usage of sophisticated wireless devices, the demand for bandwidth is rising exponentially. Also, the Federal Communications Commission (FCC) has reserved a considerable amount of spectrum for licensed users. As a result, the unlicensed spectrum usage is constrained to the overcrowded unlicensed spectrum. Various spectral management surveys have indicated inefficient spectrum utilization in the licensed spectral bands. The congested unlicensed spectrum and inefficiently used licensed frequency bands calls for an approach to use the available spectrum opportunistically. Therefore, the concept of "Spectrum Pooling", which is based on Dynamic Spectrum Access (DSA), was proposed to make the unused sections of licensed spectrum available to the unlicensed users. In Spectrum Pooling, an empty section of licensed spectrum is borrowed by a secondary user for certain period of time without interfering with the licensed user. Orthogonal Frequency Division Multiplexing (OFDM) is a transmission scheme that is a candidate for Spectrum Pooling since it is capable of forming an adaptive spectral shape that allows coexistence of licensed and unlicensed users while attemting to minimize any interference. Subcarriers in the OFDM signal can be deactivated to generate Non-Contiguous OFDM (NC-OFDM). Even though NC-OFDM allows efficient use of available spectrum, it causes out of band (OOB) radiation, which adversely affects the performance of adjacent user. This thesis presents two novel techniques for combat the effects of OOB radiation generated by NC-OFDM. The proposed techniques employ a filtering-based approach combined with the technique of windowing in order to suppress the unwanted sidelobes by around 35dB-40dB. The attenuation is achieved without affecting other transmission parameters of the secondary user significantly.

Spectrum scarcity is an important challenge faced by high-speed wireless communications. Meanwhile, caused by current spectrum assignment policy, a large portion of spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users. Moreover, opening of TV white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial requirement in CR networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to una ordably high sampling rates at energy-constrained SUs. Compressive sensing (CS) was developed to overcome this issue, which enables sub-Nyquist sampling by exploiting sparse property. As the spectrum utilization is low, spectral signals exhibit a natural sparsity in frequency domain, which motivates the promising application of CS in wideband CRNs. This thesis proposes several e ective algorithms for invoking CS in wideband CRNs. Speci cally, a robust compressive spectrum sensing algorithm is proposed for reducing computational complexity of signal recovery. Additionally, a low-complexity algorithm is designed, in which original signals are recovered with fewer measurements, as geolocation database is invoked to provide prior information. Moreover, security enhancement issue of CRNs is addressed by proposing a malicious user detection algorithm, in which data corrupted by malicious users are removed during the process of matrix completion (MC). One key spotlight feature of this thesis is that both real-world signals and simulated signals over TVWS are invoked for evaluating network performance. Besides invoking CS and MC to reduce energy consumption, each SU is supposed to harvest energy from radio frequency. The proposed algorithm is capable of o ering higher throughput by performing signal recovery at a remote fusion center.

<p>   The need for RF spectrum for the rapidly growing broadband access services is evident. Cognitive radio is an emerging technology that aims to introduce secondary usage of the spectrum resources without interfering with the primary usage of the licensed users but with a lower priority.</p><p>   Signal detection for cognitive radios has drawn a lot of interest in the research community, where different algorithms are suggested. The most commonly used algorithms are energy detection, feature detection, eigenvalue based detection. Energy detection is the simplest and most common way to detect signals. It has fast sensing time but poor performance. The feature detection and eigenvalue based detection methods are more sophisticated and offer better performance but they are more complex and expensive. This thesis will present the pros and cons of each method and offer comparisons between them.</p><p>   To evaluate the performance of different algorithms used in cognitive radio, different research testbeds have been suggested in the literature. Some of the most frequently used testbeds are based on GNU-radio, WARP, or BEE2. GNU-radio is the simplest testbed and is free, but it has low bandwidth and poor performance. WARP and BEE2 are more advanced testbeds. They offer good performance and are easy to update, but they are more complex and expensive. These three testbeds will be described, compared, and their advantages and disadvantages will be observed in this thesis.</p>

Cognitive radio (CR) is a novel wireless communication approach that may alleviate the looming spectrum-shortage crisis. Orthogonal frequency division multiplexing (OFDM) is an attractive modulation candidate for CR systems. In this thesis, we study resource allocation (RA) for OFDM-based CR systems using both aggressive and protective sharing. In aggressive sharing, cognitive radio users (CRUs) can share both non-active and active primary user (PU) bands. We develop a model that describes aggressive sharing, and formulate a corresponding multidimensional knapsack problem (MDKP). Low-complexity suboptimal RA algorithms are proposed for both single and multiple CRU systems. A simplified model is proposed which provides a faster suboptimal solution. Simulation results show that the proposed suboptimal solutions are close to optimal, and that aggressive sharing of the whole band can provide a substantial performance improvement over protective sharing, which makes use of only the non-active PU bands. Although aggressive sharing generally yields a higher spectrum-utilization efficiency than protective sharing, aggressive sharing may not be feasible in some situations. In such cases, sharing only non-active PU bands is more appropriate. When there are no fairness or quality of service (QoS) considerations among CRUs, both theoretical analysis and simulation results show that plain equal power allocation (PEPA) yields similar performance as optimal power allocation in a multiuser OFDM-based CR system. We propose a low-complexity discrete bit PEPA algorithm. To improve spectrum-utilization efficiency, while considering the time-varying nature of the available spectrum as well as the fading characteristics of wireless communication channels and providing QoS provisioning and fairness among users, this thesis introduces the following novel algorithms: (1) a distributed RA algorithm that provides both fairness and efficient spectrum usage for ad hoc systems; (2) a RA algorithm for non-real-time (NRT) services that maintains average user rates proportionally on the downlink of multiuser OFDM-based CR systems; and (3) cross-layer RA algorithms for the downlink of multiuser OFDM-based CR systems for both real-time (RT) services and mixed (RT and NRT) services. Simulation results show that the proposed algorithms provide satisfactory QoS to all supported services and perform better than existing algorithms designed for multiuser OFDM systems.

The need for RF spectrum for the rapidly growing broadband access services is evident. Cognitive radio is an emerging technology that aims to introduce secondary usage of the spectrum resources without interfering with the primary usage of the licensed users but with a lower priority.    Signal detection for cognitive radios has drawn a lot of interest in the research community, where different algorithms are suggested. The most commonly used algorithms are energy detection, feature detection, eigenvalue based detection. Energy detection is the simplest and most common way to detect signals. It has fast sensing time but poor performance. The feature detection and eigenvalue based detection methods are more sophisticated and offer better performance but they are more complex and expensive. This thesis will present the pros and cons of each method and offer comparisons between them.    To evaluate the performance of different algorithms used in cognitive radio, different research testbeds have been suggested in the literature. Some of the most frequently used testbeds are based on GNU-radio, WARP, or BEE2. GNU-radio is the simplest testbed and is free, but it has low bandwidth and poor performance. WARP and BEE2 are more advanced testbeds. They offer good performance and are easy to update, but they are more complex and expensive. These three testbeds will be described, compared, and their advantages and disadvantages will be observed in this thesis.

Spectrum sensing is an essential pre-processing step of cognitive radio technology for dynamic radio spectrum management. One of the main functions of Cognitive radios is to detect the unused spectrum and share it without harmful interference with other users. The detection of signal components present within a determined frequency band is an important requirement of any sensing technique. Most methods are restricted to the detection of the spectral lines. However, these methods may not comply with the needs imposed by practical applications.  This master thesis work presents a novel method to detect significant spectral components in measured non-flat spectra by classifying them in two groups: signal and noise frequency lines. The algorithm based on Fisher’s discriminant analysis, aside from the detection of spectral lines, estimates the magnitude of the spectral lines and provides a measure of the quality of classification to determine if a spectral line was incorrectly classified. Furthermore, the frequency lines with higher probability of misclassification are regrouped and the validation process recomputed, which results in lower probabilities of misclassification. The proposed automatic detection algorithm requires no user interaction since any prior knowledge about the measured signal and the noise power is needed. The presence or absence of a signal regardless of the shape of the spectrum can be detected. Hence, this method becomes a strong basis for high-quality operation mode of cognitive radios. Simulation and measurement results prove the advantages of the presented technique. The performance of the technique is evaluated for different signal-to-noise ratios (SNR) ranging from 0 to -21dB as required by the IEEE standard for smart radios. The method is compared with previous signal detection methods.

Driven by both the need for ubiquitous wireless services and the stringent strain on radio spectrum faced in today's wireless communications, cognitive radio (CR) have been investigated as a promising solution to deploy Wireless Regional Area Networks (WRANs) for an efficient spectrum utilization. Communication devices with CR capabilities are able to access spectrum bands licensed for other wireless services in an opportunistic and secondary fashion, while preventing harmful interference to incumbent licensed services. However, a lesson learned from early experiences in developing such macro-cellular networks is that it becomes increasingly less economically viable to develop CR macrocellular infrastructures for increasing data rates in both line-of-sight as well as non-line-of-sight situation of WRAN, and the corresponding quality of service (QoS) in macrocellular networks is also noticeably degraded due to path loss, shadowing, and multipath fading due to wall penetration. Moreover, there are several challenges to make the real-world CR enabling dynamic spectrum access a difficult problem to implement without harmful interference. First, the hardware design of cognitive radio on the physical layer involves the tuning over a broad range of spectrum to detect a weak signal in a dynamic environment of fading channels, which in turn makes identification of the spectrum opportunities hard to achieve in an efficient and accurate manner. Second, opportunistic media access based on imperfect spectrum usage information obtain from physical layer brings up undesirable interference issue, as well as reliability issues introduced by mutual interference. Third, the curial issue is to determine which channels to use for data transmissions in presence of the dynamic and opportunistic nature of wireless environments, in the case where pre-defined dedicated control channel is not available in the complex and heterogenous networks. In this dissertation, a novel framework called Cognitive-Empowered Femtocell (CEF), which combines CR techniques with femtocell networking, is introduced to tackle these challenges and achieve better spectrum reuse, lower interference, easy integration, wider network coverage, as well as fast and cost effective early stage WRAN. In this framework, a sensing coordination scheme is proposed to gracefully unshackles the master/slave relationship between central controllers and end users, while maintaining order and coordination such that better sensing precision and efficiency can be achieved. As such, the network intelligence can be expanded from controlling the intelligence paradigm to better understand the satisfy wireless user needs. We also discuss design and deployment aspects such as sensing with reasoning approach, gossip-enabled stochastic media access without a dedicated control channel, all of which are important to the success of the CEF framework. We illustrate that such a framework allows wireless users to intelligently capture spectrum opportunities while mitigating interference to other users, as well as improving the network capacity. Performance analysis and simulations were conducted based on these techniques to provide insight on the future direction of interference suppression for dynamic spectrum access.

This thesis investigates cooperative communication under the framework of cognitive radio network, which consists of primary and secondary users(PU & SU). The cooperative and cognitive radio network (CCR) adopts overlay dynamic spectrum access, That is, the SU simultaneously assists PU¡¦s transmission and transmits its own message using spectrum shared by primary user. The secondary user adopts decode-and-forward (DF) relaying to assist the primary user in transmitting message. With secondary user¡¦s assistance, the cooperative system can be treat as an equivalent multiple input single output (MISO) system to attain the spatial diversity of the primary user. The virtual MISO system can reduce the outage probability and enhance the transmission reliability. Under the requirement on primary user's transmission quality, secondary user transmits both user¡¦s signals simultaneously, so that the secondary acquires authority to access spectrum. Based on limited feedback regarding SNR of link between primary transmitter and receiver, secondary user allocates transmission power of primary signal and secondary signal to increase throughput and spectrum efficiency of SU subject to satisfying PU¡¦s outage constraint.

In this thesis, we consider cognitive radio networks (CRN) combined with cooperative transmission, and investigate relay selection and power allocation strategies to maximize network lifetime (NLT). Cognitive radio network enhances spectrum efficiency resource by exploiting capabilities of cognition, learning and coordination against insufficient spectrum resource. In underlay cognitive radio network, however, transmitted energy of secondary user is constrained by interference level observed at primary user (PU). Though cooperation among secondary users (SU), multiple relays from virtual antenna array to improve transmission rate and reliability by exploiting spatial diversity. Most existing works assume that cooperative secondary users are plugged and with infinite energy device. In this thesis, we consider secondary cooperative systems where relays are battery-powered and with finite energy. We will investigate relay-selection schemes to reduce energy consumption of secondary relays and prolong network lifetime under the premises that secondary user¡¦s transmission rate is guaranteed and interference constraint of primary user is met. Our major difference between this work and previous works is the definition of network lifetime, which is defined by the maximum duration that the probability of secondary user¡¦s achievable rate below the guaranteed value, i.e. outage probability, is lower than a predetermined threshold. We proposed four relay-selection methods which take channel state information (CSI) and residual energy information (REI) into considerations to prolong network lifetime. Since the selection metrics of the proposed strategies requires CSI and REI of each individual relay, so the relay-selection can be accomplished in distributed manner through opportunistic sensing. No additional overhead is demanded for information exchange.

Consider a cognitive overlaid network (CON) that has two tiers with different priorities: a primary tier vs. a secondary tier, which is an emerging network scenario with the advancement of cognitive radio (CR) technologies. The primary tier consists of randomly distributed primary radios (PRs) of density n, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed CRs of density m = n^y with y greater than or equal to 1, which can only access the spectrum opportunistically to limit the interference to PRs. In this dissertation, the fundamental limits of such a network are investigated in terms of the asymptotic throughput and packet delay performance when m and n approaches infinity. The following two types of CONs are considered: 1) selfish CONs, in which neither the primary tier nor the secondary tier is willing to route the packets for the other, and 2) supportive CONs, in which the secondary tier is willing to route the packets for the primary tier while the primary tier does not. It is shown that in selfish CONs, both tiers can achieve the same throughput and delay scaling laws as a stand-alone network. In supportive CONs, the throughput and delay scaling laws of the primary tier could be significantly improved with the aid of the secondary tier, while the secondary tier can still achieve the same throughput and delay scaling laws as a stand-alone network. Finally, the throughput and packet delay of a CON with a small number of nodes are investigated. Specifically, we investigate the power and rate control schemes for multiple CR links in the same neighborhood, which operate over multiple channels (frequency bands) in the presence of PRs with a delay constraint imposed on data transmission. By further considering practical limitations in spectrum sensing, an efficient algorithm is proposed to maximize the average sum-rate of the CR links over a finite time horizon under the constraints on the CR-to-PR interference and the average transmit power for each CR link. In the proposed algorithm, the PR occupancy of each channel is modeled as a discrete-time Markov chain (DTMC). Based on such a model, a novel power and rate control strategy based on dynamic programming (DP) is derived, which is a function of the spectrum sensing output, the instantaneous channel gains for the CR links, and the remaining power budget for the CR transmitter. Simulation results show that the proposed algorithm leads to a significant performance improvement over heuristic algorithms.