Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC

Decentralized sequential change detection using physical layer fusion

The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors make noisy observations of a parameter that changes from an initial state to a final state at a random time where the random change time has a geometric distribution. The sensors amplify and forward the observations over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. The optimal transmission strategy at each stage is shown to be the one that maximizes a certain Ali-Silvey distance between the distributions for the hypotheses before and after the change. Simulations demonstrate that the proposed analog technique has lower detection delays when compared with existing schemes. Simulations further demonstrate that the energy-constrained formulation enables better use of the total available energy than the power-constrained formulation in the change detection problem.Comment: 10 pages, two-column, 10 figures, revised based on feedback from reviewers, accepted for publication in IEEE Trans. on Wireless Communication

Distributed Detection in Energy Harvesting Wireless Sensor Networks

A conventional wireless sensor networks (WSN), consisting of sensors powered by nonrechargeable batteries, has a strictly limited lifetime. Energy harvesting (EH) from the environment is a promising solution to address the energy constraint problem in conventional WSNs, and to render these networks to self-sustainable networks with perpetual lifetimes. In EH-powered WSNs, where sensors are capable of harvesting and storing energy, power control is necessary to balance the rates of energy harvesting and energy consumption for data transmission. In addition, wireless communication channels change randomly in time due to fading. These together prompt the need for developing new power control strategies for an EH-enabled transmitter that can best exploit and adapt to the random energy arrivals and time-varying fading channels. We consider parallel structure EH-powered WSNs tasked with solving a binary distributed detection problem. Sensors process locally their observations, adapt their transmission according to the battery and fading channel states, and transmit their data symbols to the fusion center (FC) over orthogonal fading channels. We study adaptive transmission schemes that optimize detection performance metrics at the FC, subject to certain battery and transmit power constraints. In the first part, modeling the random energy arrival as a Poisson process, we propose a novel transmit power control strategy that is parameterized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals and we find the jointly optimal quantization thresholds and the scale factors such that detection metric at the FC is maximized. We have assumed that the battery operates at the steady-state and the energy arrival and channel models are independent and identically distributed across transmission blocks. In the second part, we assume the battery is not at the steady-state and both the channel and the energy arrival are modeled as homogeneous finite-state Markov chains. Therefore, the power control optimization problem at hand becomes a multistage stochastic optimization problem and can be solved via the Markov decision process (MDP) framework. This is the first work that develops MDP-based channel-dependent power control policy for distributed detection in EH-powered WSNs

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model

Low Latency Anomaly Detection with Imperfect Models

The problem of anomaly detection deals with detecting abrupt changes/anomalies in the distribution of sequentially observed data in a stochastic system. This problem applies to many applications, such as signal processing, intrusion detection, quality control, medical diagnosis, etc. A low latency anomaly detection algorithm, which is based on the framework of quickest change detection (QCD), aims at minimizing the detection delay of anomalies in the sequentially observed data while ensuring satisfactory detection accuracy. Moreover, in many practical applications, complete knowledge of the post-change distribution model might not be available due to the unexpected nature of the change. Hence, the objective of this dissertation is to study low latency anomaly detection or QCD algorithms for systems with imperfect models such that any type of abnormality in the system can be detected as quickly as possible for reliable and secured system operations. This dissertation includes the theoretical foundations behind these low latency anomaly detection algorithms along with real-world applications. First, QCD algorithms are designed for detecting changes in systems with multiple post-change models under both Bayesian and non-Bayesian settings. Next, a QCD algorithm is studied for real-time detection of false data injection attacks in smart grids with dynamic models. Finally, a QCD algorithm for detecting wind turbine bearing faults is developed by analyzing the statistical behaviors of stator currents generated by the turbines. For all the proposed algorithms, analytical bounds of the system performance metrics are derived using asymptotic analysis and the simulation results show that the proposed algorithms outperform existing algorithms

A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

Spectrum Sensing and Multiple Access Schemes for Cognitive Radio Networks

Increasing demands on the radio spectrum have driven wireless engineers to rethink approaches by which devices should access this natural, and arguably scarce, re- source. Cognitive Radio (CR) has arisen as a new wireless communication paradigm aimed at solving the spectrum underutilization problem. In this thesis, we explore a novel variety of techniques aimed at spectrum sensing which serves as a fundamental mechanism to find unused portions of the electromagnetic spectrum. We present several spectrum sensing methods based on multiple antennas and evaluate their receiving operating characteristics. We study a cyclostationary feature detection technique by means of multiple cyclic frequencies. We make use of a spec- trum sensing method called sequential analysis that allows us to significantly decrease the time needed for detecting the presence of a licensed user. We extend this scheme allowing each CR user to perform the sequential analysis algorithm and send their local decision to a fusion centre. This enables for an average faster and more accurate detection. We present an original technique for accounting for spatial and temporal cor- relation influence in spectrum sensing. This reflects on the impact of the scattering environment on detection methods using multiple antennas. The approach is based on the scattering geometry and resulting correlation properties of the received signal at each CR device. Finally, the problem of spectrum sharing for CR networks is addressed in or- der to take advantage of the detected unused frequency bands. We proposed a new multiple access scheme based on the Game Theory. We examine the scenario where a random number of CR users (considered as players) compete to access the radio spec- trum. We calculate the optimal probability of transmission which maximizes the CR throughput along with the minimum harm caused to the licensed users’ performance