Spectrum sensing in cognitive radio:use of cyclo-stationary detector

Cognitive radio allows unlicensed users to access licensed frequency bands through dynamic spectrum access so as to reduce spectrum scarcity. This requires intelligent spectrum sensing techniques like co-operative sensing which makes use of information from number of users. This thesis investigates the use of cyclo-stationary detector and its simulation in MATLAB for licensed user detection. Cyclo-stationary detector enables operation under low SNR conditions and thus saves the need for consulting more number of users. Simulation results show that implementing co-operative spectrum sensing help in better performance in terms of detection. The cyclo-stationary detector is used for performance evaluation for Digital Video Broadcast-Terrestrial (DVB-T) signals. Generally, DVB-T is specified in IEEE 802.22 standard (first standard based on cognitive radio) in VHF and UHF TV broadcasting spectrum. The thesis is further extended to find the number of optimal users in a scenario to optimize the detection probability and reduce overhead leading to better utilization of resources. The gradient descent algorithm and the particle swarm optimization (PSO) technique are put to use to find an optimum value of threshold. The performance for both these schemes is evaluated to find out which fares better

Spectrum Adaptation in Cognitive Radio Systems with Operating Constraints

The explosion of high-data-rate-demanding wireless applications such as smart-phones and wireless Internet access devices, together with growth of existing wireless services, are creating a shortage of the scarce Radio Frequency (RF) spectrum. However, several spectrum measurement campaigns revealed that current spectrum usage across time and frequency is inefficient, creating the artificial shortage of the spectrum because of the traditional exclusive command-and-control model of using the spectrum. Therefore, a new concept of Cognitive Radio (CR) has been emerging recently in which unlicensed users temporarily borrow spectrum from the licensed Primary Users (PU) based on the Dynamic Spectrum Access (DSA) technique that is also known as the spectrum sharing concept. A CR is an intelligent radio system based on the Software Defined Radio platform with artificial intelligence capability which can learn, adapt, and reconfigure through interaction with the operating environment. A CR system will revolutionize the way people share the RF spectrum, lowering harmful interference to the licensed PU of the spectrum, fostering innovative DSA technology and giving people more choices when it comes to using the wireless-communication-dependent applications without having any spectrum congestion problems. A key technical challenge for enabling secondary access to the licensed spectrum adaptation is to ensure that the CR does not interfere with the licensed incumbent users. However, incumbent user behavior is dynamic and requires CR systems to adapt this behavior in order to maintain smooth information transmission. In this context, the objective of this dissertation is to explore design issues for CR systems focusing on adaptation of physical layer parameters related to spectrum sensing, spectrum shaping, and rate/power control. Specifically, this dissertation discusses dynamic threshold adaptation for energy detector spectrum sensing, spectrum allocation and power control in Orthogonal Frequency Division Multiplexing-(OFDM-)based CR with operating constraints, and adjacent band interference suppression techniques in turbo-coded OFDM-based CR systems

Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes

Enhancing Spectrum Utilization in Dynamic Cognitive Radio Systems

Cognitive radio (CR) is regarded as a viable solution to enabling flexible use of the frequency spectrum in future generations of wireless systems by allowing unlicensed secondary users (SU) to access licensed spectrum under the specific condition that no harmful interference be caused to the licensed primary users (PU) of the spectrum. In practical scenarios, the knowledge of PU activity is unknown to CRs and radio environments are mostly imperfect due to various issues such as noise uncertainty and multipath fadings. Therefore, important functionalities of CR systems are to efficiently detect availability of radio spectrum as well as to avoid generating interference to PUs, by missing detection of active PU signals. Typically, CR systems are expected to be equipped with smart capabilities which include sensing, adapting, learning, and awareness concerned with spectrum opportunity access, radio environments, and wireless communications operations, such that SUs equipped with CRs can efficiently utilize spectrum opportunities with high quality of services. Most existing researches working on CR focus on improving spectrum sensing through performance measures such as the probabilities of PU detection and false alarm but none of them explicitly studies the improvement in the actual spectrum utilization. Motivated by this perspective, the main objective of the dissertation is to explore new techniques on the physical layer of dynamic CR systems, that can enhance actual utilization of spectrum opportunities and awareness on the performance of CR systems. Specifically, this dissertation investigates utilization of spectrum opportunities in dynamic scenarios, where a licensed radio spectrum is available for limited time and also analyzes how it is affected by various parameters. The dissertation proposes three new methods for adaptive spectrum sensing which improve dynamic utilization of idle radio spectrum as well as the detection of active PUs in comparison to the conventional method with fixed spectrum sensing size. Moreover, this dissertation presents a new approach for optimizing cooperative spectrum sensing performance and also proposes the use of hidden Markov models (HMMs) to enabling performance awareness for local and cooperative spectrum sensing schemes, leading to improved spectrum utilization. All the contributions are illustrated with numerical results obtained from extensive simulations which confirm their effectiveness for practical applications

Amateur radio sensing technique using a combination of energy detection and waveform classification

A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects

Primary User Emulation Detection in Cognitive Radio Networks

Cognitive radios (CRs) have been proposed as a promising solution for improving spectrum utilization via opportunistic spectrum sharing. In a CR network environment, primary (licensed) users have priority over secondary (unlicensed) users when accessing the wireless channel. Thus, if a malicious secondary user exploits this spectrum access etiquette by mimicking the spectral characteristics of a primary user, it can gain priority access to a wireless channel over other secondary users. This scenario is referred to in the literature as primary user emulation (PUE). This dissertation first covers three approaches for detecting primary user emulation attacks in cognitive radio networks, which can be classified in two categories. The first category is based on cyclostationary features, which employs a cyclostationary calculation to represent the modulation features of the user signals. The calculation results are then fed into an artificial neural network for classification. The second category is based on video processing method of action recognition in frequency domain, which includes two approaches. Both of them analyze the FFT sequences of wireless transmissions operating across a cognitive radio network environment, as well as classify their actions in the frequency domain. The first approach employs a covariance descriptor of motion-related features in the frequency domain, which is then fed into an artificial neural network for classification. The second approach is built upon the first approach, but employs a relational database system to record the motion-related feature vectors of primary users on this frequency band. When a certain transmission does not have a match record in the database, a covariance descriptor will be calculated and fed into an artificial neural network for classification. This dissertation is completed by a novel PUE detection approach which employs a distributed sensor network, where each sensor node works as an independent PUE detector. The emphasis of this work is how these nodes collaborate to obtain the final detection results for the whole network. All these proposed approaches have been validated via computer simulations as well as by experimental hardware implementations using the Universal Software Radio Peripheral (USRP) software-defined radio (SDR) platform