Cognitive Radio is an innovative technology that allows the secondary unlicensed users to share the spectrum with licensed primary users to utilize the spectrum. For maximum utilization of spectrum, in cognitive radio network spectrum sensing is an important issue. Cognitive user under extreme shadowing and channel fading can‟t sense the primary licensed user signal correctly and thus to improve the performance of spectrum sensing, collaboration between secondary unlicensed users is required. In collaborative spectrum sensing the observation of each secondary user is received by a base station acting as a central entity, where a final conclusion about the presence or absence of the primary user signal is made using a particular decision and fusion rule. Due to spatially correlated shadowing the collaborative spectrum sensing performance decreases, and thus optimum secondary users must be selected to, not only improve spectrum sensing performance but also lessen the processing overhead of the central entity. A particular situation is depicted in the project where according to some performance parameters, first those optimum secondary users that have enough spatial separation and high average received SNR are selected using Genetic Algorithm, and then collaboration among these optimum secondary users is done to evaluate the performance. The collaboration of optimal secondary user providing high probability of detection and low probability of false alarm, for sensing the spectrum is compared with the collaboration of all the available secondary users in that radio environment. At the end a conclusion has been made that collaboration of selected optimum secondary users provides better performance, then the collaboration of all the secondary users available. / Cognitive Radio is an innovative technology that allows the secondary unlicensed users to share the spectrum with licensed primary users to utilize the spectrum. For maximum utilization of spectrum, in cognitive radio network spectrum sensing is an important issue. Cognitive user under extreme shadowing and channel fading can‟t sense the primary licensed user signal correctly and thus to improve the performance of spectrum sensing, collaboration between secondary unlicensed users is required. In collaborative spectrum sensing the observation of each secondary user is received by a base station acting as a central entity, where a final conclusion about the presence or absence of the primary user signal is made using a particular decision and fusion rule. Due to spatially correlated shadowing the collaborative spectrum sensing performance decreases, and thus optimum secondary users must be selected to, not only improve spectrum sensing performance but also lessen the processing overhead of the central entity. A particular situation is depicted in the project where according to some performance parameters, first those optimum secondary users that have enough spatial separation and high average received SNR are selected using Genetic Algorithm, and then collaboration among these optimum secondary users is done to evaluate the performance. The collaboration of optimal secondary user providing high probability of detection and low probability of false alarm, for sensing the spectrum is compared with the collaboration of all the available secondary users in that radio environment. At the end a conclusion has been made that collaboration of selected optimum secondary users provides better performance, then the collaboration of all the secondary users available.

Cognitive radio refers to the technology that the devices can intelligently access unused frequency resources which are originally reserved for legacy services in order to increase the spectrum utilization. At the mean time, the legacy services should not be affected by the access of cognitive radio devices. The common problems in cognitive radio are how to find unused frequency resources (spectrum sensing) and how to access them (spectrum accessing). This dissertation focuses on the robust methods of spectrum sensing as well as spectrum accessing strategies with the statistics of channel availabilities. The first part of the thesis studies non-parametric robust hypothesis testing problem to eliminate the effect of the uncertainty and instability introduced by non-stationary noise, which is constantly observed in communication systems. An empirical likelihood ratio test with density function constraints is proposed. This test outperforms many popular goodness-of-fit tests, including the robust Kolmogorov-Smirnov test and the Cramér-von Mises test, etc. Examples using spectrum sensing data with real-world noise samples are provided to show their performance. The second part focuses on channel idle time distribution based spectrum accessing strategies. Through the study of the real-world wireless local area network traffic, it is identified that the channel idle time distribution can be modeled using hyper-exponential distribution. With this model, the performance of a single cognitive radio, or the secondary user, is studied when the licensed user, or the primary user, does not react to interference. It is also shown that with the complete information of the hyper-exponential distribution, the secondary user can achieve a desirable performance. But when the model exhibits uncertainty and time non-stationarity, which would happen for any kind of wireless traffic, the secondary user suffers from huge performance loss. A strategy that is robust to the uncertainty is proposed. The performance of this strategy is demonstrated using experimental data. Another aspect of the problem is when the PU is reactive. In this case, a spectrum accessing strategy is devised to avoid large-duration interference to the PU. Additionally, the spectrum accessing strategies are also extended to the cognitive radio networks with multiple secondary users. A decentralized MAC protocol is devised which reaches a total secondary capacity performance close to the optimal. A discussion of the engineering aspects with practical consideration of spectrum sensing and accessing is given at the end. / text

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.

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

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.

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.

The successful implementation and employment of various cognitive radio services are largely dependent on the spectrum sensing performance of the cognitive radio terminals. Previous works on detection of cognitive radio have suggested the necessity of user cooperation in order to be able to detect at low signal-to-noise ratios experienced in practical situations. This report provides a brief overview of the impact of different fusion strategies on the spectrum hole detection performance of a fusion center in a distributed detection environment. Different decision or detection rule and fusion strategies, like single sensor scenario, counting rule, and linear decision metric, were used to analyze their influence on the spectrum sensing performance of the cognitive radio network. We consider a system of cognitive radio users who cooperate with each other in trying to detect licensed transmissions. Assuming that the cooperating nodes use identical energy detectors, we model the received signals as correlated log-normal random variables and study the problem of fusing the decisions made by the individual nodes. The cooperating radios were assumed to be designed in such a way that they satisfy the interference probability constraint individually. The interference probability constraint was also met at the fusion center. The simulation results strongly suggests that even when the observations at the individual sensors are moderately correlated, it is important not to ignore the correlation between the nodes for fusing the local decisions made by the secondary users. The thesis mainly focuses on the performance measurement of linear decision combiner in detecting primary users in a cognitive radio network.

In this thesis, spectrum sensing techniques are investigated for cognitive radio (CR) networks in order to improve the sensing and transmission performance of secondary networks. Specifically, the detailed exploration comprises of three areas, including single-node spectrum sensing based on eigenvalue-based detection, cooperative spectrum sensing under random secondary networks and full-duplex (FD) spectrum sensing and sharing techniques. In the first technical chapter of this thesis, eigenvalue-based spectrum sensing techniques, including maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection and the generalized likelihood ratio test (GLRT) eigenvalue detector, are investigated in terms of total error rates and achievable throughput. Firstly, in order to consider the benefits of primary users (PUs) and secondary users (SUs) simultaneously, the optimal decision thresholds are investigated to minimize the total error rate, i.e. the summation of missed detection and false alarm rate. Secondly, the sensing-throughput trade-off is studied based on the GLRT detector and the optimal sensing time is obtained for maximizing the achievable throughput of secondary communications when the target probability of detection is achieved. In the second technical chapter, the centralized GLRT-based cooperative sensing technique is evaluated by utilizing a homogeneous Poisson point process (PPP). Firstly, since collaborating all the available SUs does not always achieve the best sensing performance under a random secondary network, the optimal number of cooperating SUs is investigated to minimize the total error rate of the final decision. Secondly, the achievable ergodic capacity and throughput of SUs are studied and the technique of determining an appropriate number of cooperating SUs is proposed to optimize the secondary transmission performance based on a target total error rate requirement. In the last technical chapter, FD spectrum sensing (FDSS) and sensing-based spectrum sharing (FD-SBSS) are investigated. There exists a threshold pair, not a single threshold, due to the self-interference caused by the simultaneous sensing and transmission. Firstly, by utilizing the derived expressions of false alarm and detection rates, the optimal decision threshold pair is obtained to minimize total error rate for the FDSS scheme. Secondly, in order to further improve the secondary transmission performance, the FD-SBSS scheme is proposed and the collision and spectrum waste probabilities are studied. Furthermore, different antenna partitioning methods are proposed to maximize the achievable throughput of SUs under both FDSS and FD-SBSS schemes.

abstract: This thesis investigates three different resource allocation problems, aiming to achieve two common goals: i) adaptivity to a fast-changing environment, ii) distribution of the computation tasks to achieve a favorable solution. The motivation for this work relies on the modern-era proliferation of sensors and devices, in the Data Acquisition Systems (DAS) layer of the Internet of Things (IoT) architecture. To avoid congestion and enable low-latency services, limits have to be imposed on the amount of decisions that can be centralized (i.e. solved in the ``cloud") and/or amount of control information that devices can exchange. This has been the motivation to develop i) a lightweight PHY Layer protocol for time synchronization and scheduling in Wireless Sensor Networks (WSNs), ii) an adaptive receiver that enables Sub-Nyquist sampling, for efficient spectrum sensing at high frequencies, and iii) an SDN-scheme for resource-sharing across different technologies and operators, to harmoniously and holistically respond to fluctuations in demands at the eNodeB' s layer. The proposed solution for time synchronization and scheduling is a new protocol, called PulseSS, which is completely event-driven and is inspired by biological networks. The results on convergence and accuracy for locally connected networks, presented in this thesis, constitute the theoretical foundation for the protocol in terms of performance guarantee. The derived limits provided guidelines for ad-hoc solutions in the actual implementation of the protocol. The proposed receiver for Compressive Spectrum Sensing (CSS) aims at tackling the noise folding phenomenon, e.g., the accumulation of noise from different sub-bands that are folded, prior to sampling and baseband processing, when an analog front-end aliasing mixer is utilized. The sensing phase design has been conducted via a utility maximization approach, thus the scheme derived has been called Cognitive Utility Maximization Multiple Access (CUMMA). The framework described in the last part of the thesis is inspired by stochastic network optimization tools and dynamics. While convergence of the proposed approach remains an open problem, the numerical results here presented suggest the capability of the algorithm to handle traffic fluctuations across operators, while respecting different time and economic constraints. The scheme has been named Decomposition of Infrastructure-based Dynamic Resource Allocation (DIDRA). / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Cognitive Radio (CR) aims to access the wireless spectrum in an opportunistic manner while the licensed user is not using it. To accurately determine the licensed user's existence, spectrum sensing procedure is vital to CR system. Energy detection-based spectrum sensing techniques is favourable due to its simplicity and low complexity. In addition, to improve the detection performance, cooperative spectrum sensing technique exploits multi-user diversity and mitigates detection uncertainty. In this thesis, we investigate several energy detection based cooperative spectrum sensing techniques.First, the closed-form analysis for the Equal Gain Combining based Soft Decision Combining (EGC-SDC) scheme, in which all CR users forward its observation to the fusion center, is derived. In order to reduce the communication overhead between CR users and the fusion center, we proposed quantized cooperative spectrum sensing technique, in which CR users quantize its local observation before forwarding to the fusion center. Next, the Double Threshold scheme, where some users only forward its local decision while other users forward its observation, is considered and analyzed. To further reduce the communication overhead, we also proposed that quantization is applied to the users who forward its observation. Later on, three sequential cooperative spectrum sensing schemes in time-varying channel are considered. By aggregating past local observations from previous sensing slots, CR users can improve the detection performance. The Weighted Sequential Energy Detector (SED) scheme simply takes fixed number of past local observations, while the other two schemes, Two-Stage SED and Differential SED, adaptively determine the number of observations, based on its decision towards primary user's existence.Simulation results show that the analysis on EGC-SDC scheme is accurate and the quantized cooperative spectrum sensing technique can improve the performance and approach the detection performance of EGC-SDC scheme with much less bandwidth requirement. Also, the Double Threshold scheme can help improve the detection performance over the conventional technique. Furthermore, the analysis on Double Threshold provides a closed-form for the probability of false alarm and detection. Additionally, the sequential spectrum sensing schemes are shown to improve the detection performance and enable CR system to work in scenarios that the conventional technique can not accommodate.