Cognitive radio is an autonomous transceiver that is continuously sensing theongoing communication in its environment, it then starts the communication whenever it is appropriate. Therefore, cognitive radio helps improving the spectrum utilization of the overall communication system. However, without suitable spectrum sensing techniques, cognitive radio would fail. Hence, in this thesis we investigate and implement various spectrum sensing algorithms via software defined radio for both single antenna and multiple antenna cases. The main communi-cation scheme that we are using is OFDM. Moreover, both computer simulations and real-world measurements, have also been done for comparison and analysis ofthe detector’s performance. The detectors we are using are based on correlationfunction of the received signal and generalized likelihood ratio test with its eigen-value. The results from the simulations and measurements are then representedas probability of missed detection curves for different signal to noise ratios. Ourresults show that the performance of the generalized likelihood ratio test baseddetectors are at least 2 dB better than the correlation based detector in our mea-surement. Moreover, our simulations show that they are able to outperform thecorrelation function detector by more than 6 dB. Although, generalized likelihoodratio test based detectors seem to be better than the correlation function baseddetector, it requires more computational power which may limit its practical use.

Dynamic spectrum access based on cognitive radio has been regarded as a prospective solution to improve spectrum utilization for wireless communications. By considering the allocation efficiency, fairness, and economic incentives, spectrum marketing has been attracting more and more attentions in recent years. In this thesis, we focus on one of the most effective spectrum marketing methods, i.e., auction approach, in multi-channel cognitive radio networks. After presenting some fundamentals and related works, we begin our discussion in a recall-based auction system where buyers have various service requirements and the seller could recall some sold items after the auction to deal with a sudden increase of its own demand. Both single-winner and multi-winner auctions are designed and analyzed. In addition, we also consider the heterogeneity of radio resource sellers and formulate a framework of combinatorial spectrum auction. With theoretical analyses and simulation results, we show that our proposed algorithms can improve spectrum utilization while satisfy the heterogeneous requirements of different wireless users.

Feature-based spectrum sensing techniques have emerged as good balance between energy-based techniques and coherent-based techniques, where the former require minimal prior information of the observed signal, and the latter have robust detection performance when the observed signal is very weak. In this thesis, we focus on pilot tone-aided detection as a feature-based detection class. We propose an improved pilot tone-aided spectrum sensor that utilizes the presence of the pilot tone and the overall energy of the received signal. We show that the optimal Neyman-Pearson detector is a weighted summation of a feature-based component and an energy-based component. The former provides coherent gains at the low signal-to-noise ratio (SNR) regime, whereas the latter provides non-coherent gains at moderate SNR levels. The proposed detector intelligently adapts its weights based on the SNR of the observed signal and the power allocation factor of the pilot tone. This helps it attain significant performance gains compared with the conventional pilot tone-aided detectors. In addition, we present suboptimal detectors that reduce the computational complexity. For instance, we demonstrate that moment estimators are effective techniques for spectrum sensing. Motivated by insights gained from the derivations of these moment estimators, we present a selective mean-variance estimator that performs well in the absence of the prior knowledge about the pilot tone. Moreover, we analyze the impact of two model uncertainties on the detection performance of the proposed detector: Noise uncertainty and imperfect pilot-matching. We show that unlike the energy detector, the proposed detector does not suffer from the SNR wall under the noise uncertainty model due to the coherent gains embedded in the feature-based component. Also, unlike existing pilot tone-aided detectors, the proposed detector is resilient against imperfect synchronization due to the non-coherent gains embedded in computing the overall energy of the signal. Also, we show that the proposed detector achieves the lowest sample complexity, leading to tangible improvements to the aggregate throughput of the secondary user. Extensive simulation and analytical results are provided to verify these conclusions. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2014-04-15 15:31:27.253

In this thesis, we consider joint beamforming, power, and channel allocation in a multi-user and multi-channel underlay cognitive radio network (CRN). In this system, beamforming is implemented at each SU-TX to minimize the co-channel interference. The formulated joint optimization problem is a non-convex, mixed integer nonlinear programming (MINLP) problem. We propose a solution which consists of two stages. At first, given a channel allocation, a feasible solutions for power and beamforming vectors are derived by converting the problem into a convex form with an introduced optimal auxiliary variable and semidefinite relaxation (SDR) approach. Next, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm are proposed to determine optimal channel allocations. Simulation results show that beamforming, power and channel allocation with SA (BPCA-SA) algorithm achieves a close optimal sum-rate with a lower computational complexity compared with beamforming, power and channel allocation with GA (BPCA-GA) algorithm. Furthermore, our proposed allocation scheme shows significant improvement than zero-forcing beamforming (ZFBF).

To meet the ever increasing spectrum demand, developing a mechanism for dynamic spectrum access seems inevitable. Spectrum sensing enables cognitive radios (CRs) to identify and use frequency bands (channels) that are not being used by primary users (PUs) at a particular place and time. However, sensing errors and limited sensing resources, such as sensing hardware and sensing time, introduce significant technical challenges to the development of such an ideal capability. Adaptive sensing strategies allow the sensing resources to be spent on more promising primary channels. This is achieved by exploiting past sensing outcomes of one secondary user (SU), or, as proposed in this research, multiple spatially distributed SUs. We propose adaptive sensing strategies for three different scenarios. First, we assume that a SU sequentially senses a number of primary channels to find the first available channel. We propose a two-stage spectrum detection strategy that allows the spectrum detector to quickly detect and skip though most of busy channels and spend most of its time on channels that are more likely to be idle. Second, we consider the case where multiple SUs jointly try to locate idle channels within a given sensing time, which itself is divided into a number of sensing slots. We propose a cooperative spectrum search strategy that specifies the channel to be sensed by each SU in each slot in such a way to maximize the expected number of identified idle channels. Third, we consider a primary network that operates in a synchronous time-framed fashion. We assume that the occupancy state of each primary channel over different time frames follows a discrete-time Markov process. We propose a cooperative sensing strategy that decides which channel should be sensed by which SU in each frame. The goal is to maximize a utility function that accounts for both the number of detected idle channel-frames and the number of miss-detected busy channel-frames. We present analytical and numerical results to demonstrate the effectiveness of the proposed sensing strategies in increasing identified time-frequency spectrum opportunities and/or reducing interference with licensed systems.

Radio spectrum is scarce in wireless communication. While there is an increasing demand for spectrum due to the substantial growth of wireless communication systems, extensive measurements observe that conventional static spectrum allocation policies introduce significant inefficiency in spectrum utilization. To achieve higher spectrum efficiency, cognitive radio networks have emerged as a revolutionary technology by allowing unlicensed (secondary) users to utilize licensed bands opportunistically without harming licensed (primary) users. In this thesis, we seek to design a new framework that addresses three important issues in cognitive radio networks simultaneously: protection of primary users, incentives for primary networks to share their spectrum and the performance guarantee for secondary users. Leveraging the idea of Value at Risk from economics, in our solution, primary networks maximize their profits by charging secondary users for opportunistic spectrum access, while in the meantime secondary users impose utility constraints to manage the risks and guarantee performance probabilistically.

To meet the ever increasing spectrum demand, developing a mechanism for dynamic spectrum access seems inevitable. Spectrum sensing enables cognitive radios (CRs) to identify and use frequency bands (channels) that are not being used by primary users (PUs) at a particular place and time. However, sensing errors and limited sensing resources, such as sensing hardware and sensing time, introduce significant technical challenges to the development of such an ideal capability. Adaptive sensing strategies allow the sensing resources to be spent on more promising primary channels. This is achieved by exploiting past sensing outcomes of one secondary user (SU), or, as proposed in this research, multiple spatially distributed SUs. We propose adaptive sensing strategies for three different scenarios. First, we assume that a SU sequentially senses a number of primary channels to find the first available channel. We propose a two-stage spectrum detection strategy that allows the spectrum detector to quickly detect and skip though most of busy channels and spend most of its time on channels that are more likely to be idle. Second, we consider the case where multiple SUs jointly try to locate idle channels within a given sensing time, which itself is divided into a number of sensing slots. We propose a cooperative spectrum search strategy that specifies the channel to be sensed by each SU in each slot in such a way to maximize the expected number of identified idle channels. Third, we consider a primary network that operates in a synchronous time-framed fashion. We assume that the occupancy state of each primary channel over different time frames follows a discrete-time Markov process. We propose a cooperative sensing strategy that decides which channel should be sensed by which SU in each frame. The goal is to maximize a utility function that accounts for both the number of detected idle channel-frames and the number of miss-detected busy channel-frames. We present analytical and numerical results to demonstrate the effectiveness of the proposed sensing strategies in increasing identified time-frequency spectrum opportunities and/or reducing interference with licensed systems.

Radio spectrum is scarce in wireless communication. While there is an increasing demand for spectrum due to the substantial growth of wireless communication systems, extensive measurements observe that conventional static spectrum allocation policies introduce significant inefficiency in spectrum utilization. To achieve higher spectrum efficiency, cognitive radio networks have emerged as a revolutionary technology by allowing unlicensed (secondary) users to utilize licensed bands opportunistically without harming licensed (primary) users. In this thesis, we seek to design a new framework that addresses three important issues in cognitive radio networks simultaneously: protection of primary users, incentives for primary networks to share their spectrum and the performance guarantee for secondary users. Leveraging the idea of Value at Risk from economics, in our solution, primary networks maximize their profits by charging secondary users for opportunistic spectrum access, while in the meantime secondary users impose utility constraints to manage the risks and guarantee performance probabilistically.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 120-126).

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 101-105).