Cognitive radio (CR) is a class of wireless communication technologies that have the ability to learn from the surrounding radio environment and the intelligence to adapt communication resources to enhance quality of service. The problem of acquiring information from a CR's radio environment is called spectrum sensing, which can take on many forms. In particular, this thesis concerns the determination of whether a spectrum band (or channel) is in a busy or idle state. The binary nature of a channels availability means that spectrum sensing can be cast as a hypothesis testing problem. While an abundant literature exists on spectrum sensing as a signal detection problem, this thesis treats spectrum sensing differently, and features the following elements: 1) the system is equipped with an arbitrary number of sensors; 2) sensing is performed over multiple channels; 3) each channels availability is modelled by random periods of busy and idle times corresponding to packet transmission; and 4) the optimization criteria minimizes detection delay subject to a reliability constraint. A related spectrum sensing problem formulation based on the use of a single sensor has been proposed in the recent literature. The previous research employs an optimization framework based on modeling channel uses as an on-off process via partially observable Markov decision processes (POMDP). This thesis generalizes previous results from single-sensor to multiple-sensor spectrum sensing, i.e., detecting idle periods with multiple sensors. In addition, an alternative reduced-complexity algorithm is proposed. For both proposed detectors, the performances are evaluated based on Monte Carlo simulation with calculated confidence intervals, and the results show that 1) adding sensors generally improves the system performance by reducing detection delay (improved agility); 2) the application of previously existing quickest detection methods result in error floors complicating test design. Finally, performance assessment using a channel model derived experimentally from the wireless local area network (WLAN) traffic is conducted and compared to that obtained using a geometrically-distributed channel traffic model. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-11-17 10:35:56.751

In this thesis we investigate the problem of robustness and scalability w.r.t. estimating the position of randomly deployed motes/nodes of a Wireless Sensor Network (WSN) without the help of Global Positioning System (GPS) devices. We propose a few applications of range independent localization algorithms that allow the sensors to actively determine their location with high resolution without increasing the complexity of the hardware or any additional device setup. In our first application we try to present a localized and centralized cooperative spectrum sensing using RF sensor networks. This scheme collaboratively sense the spectrum and localize the whole network efficiently and with less difficulty. In second application we try to focus on how efficiently we can localize the nodes, to detect underwater threats, without the use of beacons. In third application we try to focus on 3-Dimensional localization for LTE systems. Our performance evaluation shows that these schemes lead to a significant improvement in localization accuracy compared to the state-of-art range independent localization schemes, without requiring GPS support.

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government agencies. With the explosive emergence of wireless applications, the demands for the RF spectrum are constantly increasing. On the other hand, it has been reported that localised temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an innovative technology designed to improve spectrum utilisation by exploiting those spectrum opportunities. This ability is dependent upon spectrum sensing, which is one of most critical components in a cognitive radio system. A significant challenge is to sense the whole RF spectrum at a particular physical location in a short observation time. Otherwise, performance degrades with longer observation times since the lagging response to spectrum holes implies low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing technique is prime important. In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband spectrum sensing. The key features of the MASS system are, 1) low implementation complexity, 2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack of time synchronisation. The conditions under which recovery of the full spectrum is unique are presented using compressive sensing (CS) analysis. The MASS system is applied to both centralised and distributed cognitive radio networks. When the spectra of the cognitive radio nodes have a common spectral support, using one low-rate ADC in each cognitive radio node can successfully recover the full spectrum. This is obtained by applying a hybrid matching pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP). Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary users from a small number of measurements without ever reconstructing the full spectrum. To achieve a better detection performance, a data fusion strategy is developed for combining sensing data from all cognitive radio nodes. Theoretical bounds on detection performance are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise (AWGN), Rayleigh fading, and log-normal fading channels. In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency, good data compression capability, and are applicable to distributed cognitive radio networks.

As the demand for wireless communication systems grows, the need for spectrum grows accordingly. However, a large portion of the usable spectrum has already been exclusively licensed to various entities. This exclusive allocation method encourages spectrum to be left unused if the licensee has no need for that spectrum. In order to better utilize spectrum and formulate new approaches for greater spectrum use efficiency, it is imperative to possess a thorough understanding about how wireless spectrum behaves over time, frequency, and space. In this thesis, a practical, scalable, and low-cost wideband distributed spectrum sensing system is designed, implemented, and tested. The proposed system is made up of a collection of nodes that use general purpose, off-the-shelf computer hardware as well as a collection of inexpensive software-defined radio (SDR) equipment in order to collect and analyze spectrum data that varies across time, frequency, and space. The spectrum data the proposed system collects is the power present at a given frequency. The tools needed to analyze the gathered data are also created, including a periodogram and spectrogram function, which visualize average spectrum use over a period of time and as spectrum use varies with time, respectively. The proposed system also facilitates the testing of a spatio-spectrum characterization method using real data. This method has only been simulated up to this point. The characterization technique allows for spatially varying spectrum measurements to be visualized using heat maps.

This thesis investigates different aspects of spectrum sensing in cognitive radio (CR) technology. First a probabilistic inference approach is presented which models the decision fusion in cooperative sensing as a probabilistic inference problem on a factor graph. This approach allows for modeling and accommodating the uncertainties and correlations in the cooperative sensing system. A constraint in the cognitive radios is the lack of knowledge about the primary signal and channel gain statistics at the secondary users. Therefore, a practical composite hypothesis approach is proposed which does not require any prior knowledge or estimates of these unknown parameters. Detection delay is an important performance measure in spectrum sensing. Quickest detection aiming to minimize detection delay has been studied in other contexts, and we apply it here to spectrum sensing. To combat the destructive channel conditions such as fading, various cooperative schemes based on the cumulative sum (CUSUM) algorithm are considered in this thesis. Furthermore, cooperative quickest sensing with imperfectly known parameters is investigated and a new solution is derived, which does not require any parameter estimation or iterative algorithm. In cognitive radios, there is a fundamental trade-off between the achievable throughput by the CRs and the level of protection for the primary user. In this thesis, this trade-off is formulated for the quickest sensing-based CRs. By throughput analysis, it is shown that for the same protection level to the primary user, the quickest sensing approach results in significantly higher average throughput compared to that of the conventional block sensing approach.

This thesis investigates different aspects of spectrum sensing in cognitive radio (CR) technology. First a probabilistic inference approach is presented which models the decision fusion in cooperative sensing as a probabilistic inference problem on a factor graph. This approach allows for modeling and accommodating the uncertainties and correlations in the cooperative sensing system. A constraint in the cognitive radios is the lack of knowledge about the primary signal and channel gain statistics at the secondary users. Therefore, a practical composite hypothesis approach is proposed which does not require any prior knowledge or estimates of these unknown parameters. Detection delay is an important performance measure in spectrum sensing. Quickest detection aiming to minimize detection delay has been studied in other contexts, and we apply it here to spectrum sensing. To combat the destructive channel conditions such as fading, various cooperative schemes based on the cumulative sum (CUSUM) algorithm are considered in this thesis. Furthermore, cooperative quickest sensing with imperfectly known parameters is investigated and a new solution is derived, which does not require any parameter estimation or iterative algorithm. In cognitive radios, there is a fundamental trade-off between the achievable throughput by the CRs and the level of protection for the primary user. In this thesis, this trade-off is formulated for the quickest sensing-based CRs. By throughput analysis, it is shown that for the same protection level to the primary user, the quickest sensing approach results in significantly higher average throughput compared to that of the conventional block sensing approach.

In this thesis, the problem of spectrum sensing in cognitive radio communication networks is considered. This thesis has developed a robust decision fusion scheme that can perform well when the interference caused by other PUs is present. Specifically, the proposed detection scheme is based on fusing the local decisions from energy detectors (EDs) and cyclostationarity- based detectors (CDs). Our proposed fusion scheme is different from other power spectrum sensing technology being developed so far in that other fusion technology are based on fusing the local decisions from the same type of detectors. Our proposed fusion scheme can take the advantage of both EDs and CDs. We compare the proposed scheme with the schemes fusing the same type of detectors, and the results confirm that the proposed scheme is more robust against the possible interference.

With the rapid proliferation of new wireless communication devices and services, the demand for the radio spectrum is increasing at a rapid rate, which leads to making the spectrum more and more crowded. The limited available spectrum and the inefficiency in the spectrum usage have led to the emergence of cognitive radio (CR) and dynamic spectrum access (DSA) technologies, which enable future wireless communication systems to exploit the empty spectrum in an opportunistic manner. To do so, future wireless devices should be aware of their surrounding radio environment in order to adapt their operating parameters according to the real-time conditions of the radio environment. From this viewpoint, spectrum sensing is becoming increasingly important to new and future wireless communication systems, which is designed to monitor the usage of the radio spectrum and reliably identify the unused bands to enable wireless devices to switch from one vacant band to another, thereby achieving flexible, reliable, and efficient spectrum utilisation. This thesis focuses on issues related to local and cooperative spectrum sensing for CR networks, which need to be resolved. These include the problems of noise uncertainty and detection in low signal to noise ratio (SNR) environments in individual spectrum sensing. In addition to issues of energy consumption, sensing delay and reporting error in cooperative spectrum sensing. In this thesis, we investigate how to improve spectrum sensing algorithms to increase their detection performance and achieving energy efficiency. To this end, first, we propose a new spectrum sensing algorithm based on energy detection that increases the reliability of individual spectrum sensing. In spite of the fact that the energy detection is still the most common detection mechanism for spectrum sensing due to its simplicity. Energy detection does not require any prior knowledge of primary signals, but has the drawbacks of threshold selection, and poor performance due to noise uncertainty especially at low SNR. Therefore, a new adaptive optimal energy detection algorithm (AOED) is presented in this thesis. In comparison with the existing energy detection schemes the detection performance achieved through AOED algorithm is higher. Secondly, as cooperative spectrum sensing (CSS) can give further improvement in the detection reliability, the AOED algorithm is extended to cooperative sensing; in which multiple cognitive users collaborate to detect the primary transmission. The new combined approach (AOED and CSS) is shown to be more reliable detection than the individual detection scheme, where the hidden terminal problem can be mitigated. Furthermore, an optimal fusion strategy for hard-fusion based cognitive radio networks is presented, which optimises sensing performance. Thirdly, the need for denser deployment of base stations to satisfy the estimated high traffic demand in future wireless networks leads to a significant increase in energy consumption. Moreover, in large-scale cognitive radio networks some of cooperative devices may be located far away from the fusion centre, which causes an increase in the error rate of reporting channel, and thus deteriorating the performance of cooperative spectrum sensing. To overcome these problems, a new multi-hop cluster based cooperative spectrum sensing (MHCCSS) scheme is proposed, where only cluster heads are allowed to send their cluster results to the fusion centre via successive cluster heads, based on higher SNR of communication channel between cluster heads. Furthermore, in decentralised CSS as in cognitive radio Ad Hoc networks (CRAHNs), where there is no fusion centre, each cognitive user performs the local spectrum sensing and shares the sensing information with its neighbours and then makes its decision on the spectrum availability based on its own sensing information and the neighbours’ information. However, cooperation between cognitive users consumes significant energy due to heavy communications. In addition to this, each CR user has asynchronous sensing and transmission schedules which add new challenges in implementing CSS in CRAHNs. In this thesis, a new multi-hop cluster based CSS scheme has been proposed for CRAHNs, which can enhance the cooperative sensing performance and reduce the energy consumption compared with other conventional decentralised cooperative spectrum sensing modes.

Under the contemporary spectrum usage regulations, radio frequency bands are allocated statically to licensed users in a large geographical area and over a long period of time. Recent investigations have revealed that such static spectrum allocation has led to very poor usage of the overall spectrum. Cognitive radio has emerged as a new communication paradigm to improve the utilization of the radio spectrum. It is defined as an intelligent wireless communication system that allows coexistence of unlicensed users with the licensed ones as long as the perceived interference at the licensed user is capped below some acceptable level. In addition, the users in this system adopt efficient communication protocols to enhance spectral efficiency. We employ cooperative mechanisms wherein multiple users cooperate in order to accomplish the following tasks: 1) Cooperative spectrum sensing: In this task, the licensed users do not actively engage. Instead, the unlicensed users passively monitor the activity of the licensed users and transmit only during their absence. 2) Cooperative spectrum management: The licensed and unlicensed users can benefit from cooperation with each other, e.g., they can assist each other in transmission via relaying. In this fashion, they can save power or bandwidth and therefore, the whole network can accommodate more users. \end{itemize} In the first part of this thesis, we focus on cooperative spectrum sensing. We first study the performance of the optimal distributed detectors as the number of samples increases and identify the conditions under which the highest or lowest asymptotic performance is achieved. For each condition, we study several suboptimal detectors and obtain novel asymptotic expressions for their performance. We then consider distributed detection of an Orthogonal Frequency-Division Multiplexing (OFDM) signal source. We propose different optimal and suboptimal frequency-domain detectors and derive closed form expressions for their performance. These frequency-domain detectors, despite their lower computational complexity, outperform the state-of-the-art time-domain detectors. Finally, we consider distributed spectrum sensing in mixture-Nakagami fading channels. We propose several novel detectors that significantly outperform the traditional detectors. In all these cases, we prove that the suboptimal detectors are asymptotically optimal, i.e., their performance converges to the Uniformly Most Powerful (UMP) tests as the number of samples increases. In the second part of the thesis, we focus on cooperative spectrum management. We study the problem of cooperative relay selection and power allocation and determine the conditions, in terms of channel gains and network geometry, under which such cooperation leads to an increase in rate, or a reduction in power and bandwidth usage. Lastly, we propose cooperative protocols that exploit these results and greatly enhance spectrum efficiency. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2013-06-04 18:23:24.845

Cognitive radio is a low-cost communication system, which can choose the available frequencies and waveforms automatically on the premise of avoiding interfering the licensed users. The spectrum sensing is the key enabling technology in cognitive radio networks. It is able to fill voids in the wireless spectrum and can dramatically increase spectral efficiency.   In this thesis, the author use matlab to simulate the received signals from the cognitive radio networks and an energy detector to detect whether the spectrum is being used. The report also compares the theoretical value and the simulated result and then describes the relationship between the signal to noise ratio (SNR) and the detections. At last, the method, energy detection and simulation and result are discussed which is considered as the guidelines for the future work.