Spectrum Sensing Techniques For Cognitive Radio Networks

In this chapter, we present the state of the art of the spectrum sensing techniques for cognitive radio networks as well and their comparisons. The rest of the chapter is organized as below: Section I.1, Section I.2, and Section I.3 present the spectrum management problem and the cognitive radio cycle as well as the compressive sensing solution; Section II.1 describes the spectrum sensing model; Section II.2 presents the existing spectrum sensing techniques, including energy, autocorrelation, Euclidian distance, wavelet, and matched filter based sensing. Finally, a conclusion is given at the end of the chapter

A Trade-off Analysis of Energy Detectors and Partitioned Search for Primary Detection

Cognitive radios aim to coexist in the unused spectrum bands which are licensed to primary users without harming the primary transmission/reception. For a cognitive radio, it is important to detect the band in which the primary is operating as fast as possible and with high reliability in order to adapt its transmission. In this work, we propose P-partitioning method in combination with energy detectors for the search of the band that the primary user is operating. In the P-partitioning method, the spectrum bands are categorized into P groups and the group that the primary band belongs to is detected in a recursive fashion. The energy detector operates on each group and the test statistics is the total energy received in the bands belonging to the group. The proposed search technique has detection time PlogP(N), where N is the number of bands in the spectrum. When P = N, the proposed scheme is equivalent to linear search with detection time N. We study the performance of the proposed scheme for a single non-cooperative radio and also for multiple cooperating radios. For a single cognitive radio, we provide an upper bound on the probability of correct detection which presents two different regimes of operation. In the low SNR regime, although it is counter-intuitive the partitioning improves the probability of detection. This is due an averaging effect when the signal energy in different bands are accumulated to obtain the energy contribution from a group. In the high SNR regime, performance degrades with partitioning. In addition, we observe that user cooperation improves the performance in the high SNR regimes

Security and Privacy Challenges in Cognitive Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted a lot of interest in the research community due to their potential applicability in a wide range of real-world practical applications. However, due to the distributed nature and their deployments in critical applications without human interventions and sensitivity and criticality of data communicated, these networks are vulnerable to numerous security and privacy threats that can adversely affect their performance. These issues become even more critical in cognitive wireless sensor networks (CWSNs) in which the sensor nodes have the capabilities of changing their transmission and reception parameters according to the radio environment under which they operate in order to achieve reliable and efficient communication and optimum utilization of the network resources. This chapter presents a comprehensive discussion on the security and privacy issues in CWSNs by identifying various security threats in these networks and various defense mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs are categorized under different classes based on their natures and targets, and corresponding to each attack class, appropriate security mechanisms are also discussed. Some critical research issues on security and privacy in CWSNs are also identified.Comment: 36 pages, 4 figures, 2 tables. The book chapter is accepted for publication in 201

Cognitive Radios: A Survey of Methods for Channel State Prediction

This paper discusses the need for Cognitive Radio ability in view of the physical scarcity of wireless spectrum for communication. A background of the Cognitive Radio technology is presented and the aspect of 'channel state prediction' is focused upon. Hidden Markov Models (HMM) have been traditionally used to model the wireless channel behavior but it suffers from certain limitations. We discuss few techniques of channel state prediction using machine-learning methods and will extend the Conditional Random Field (CRF) procedure to this field.Comment: 10 pages, 5 figure

Compressive sensing for dynamic spectrum access networks: Techniques and tradeoffs

We explore the practical costs and benefits of CS for dynamic spectrum access (DSA) networks. Firstly, we review several fast and practical techniques for energy detection without full reconstruction and provide theoretical guarantees. We also define practical metrics to measure the performance of these techniques. Secondly, we perform comprehensive experiments comparing the techniques on real signals captured over the air. Our results show that we can significantly compressively acquire the signal while still accurately determining spectral occupancy.Comment: 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum, May 201

5G-CORNET: Platform as a Service

Practical testing of the latest wireless communications standards requires the availability of flexible radio frequency hardware, networking and computing resources. We are providing a Cloud-based infrastructure which offers the necessary resources to carry out tests of the latest 5G standards. The testbed provides a Cloud-based Infrastructure as a Service. The research community can access hardware and software resources through a virtual plat-form that enables isolation and customization of experiments. In other words, researchers have control over the preferred experimental architecture and can run concurrent experiments on the same testbed. This paper introduces the resources that can be used to develop 5G testbeds and experiments.Comment: IEEE 5G World Forum 201

Convolutional Radio Modulation Recognition Networks

We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio

Spectrum Monitoring Using Energy Ratio Algorithm For OFDM-Based Cognitive Radio Networks

This paper presents a spectrum monitoring algorithm for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios by which the primary user reappearance can be detected during the secondary user transmission. The proposed technique reduces the frequency with which spectrum sensing must be performed and greatly decreases the elapsed time between the start of a primary transmission and its detection by the secondary network. This is done by sensing the change in signal strength over a number of reserved OFDM sub-carriers so that the reappearance of the primary user is quickly detected. Moreover, the OFDM impairments such as power leakage, Narrow Band Interference (NBI), and Inter-Carrier Interference (ICI) are investigated and their impact on the proposed technique is studied. Both analysis and simulation show that the \emph{energy ratio} algorithm can effectively and accurately detect the appearance of the primary user. Furthermore, our method achieves high immunity to frequency-selective fading channels for both single and multiple receive antenna systems, with a complexity that is approximately twice that of a conventional energy detector

A Survey on Legacy and Emerging Technologies for Public Safety Communications

Effective emergency and natural disaster management depend on the efficient mission-critical voice and data communication between first responders and victims. Land Mobile Radio System (LMRS) is a legacy narrowband technology used for critical voice communications with limited use for data applications. Recently Long Term Evolution (LTE) emerged as a broadband communication technology that has a potential to transform the capabilities of public safety technologies by providing broadband, ubiquitous, and mission-critical voice and data support. For example, in the United States, FirstNet is building a nationwide coast-to-coast public safety network based of LTE broadband technology. This paper presents a comparative survey of legacy and the LTE-based public safety networks, and discusses the LMRS-LTE convergence as well as mission-critical push-to-talk over LTE. A simulation study of LMRS and LTE band class 14 technologies is provided using the NS-3 open source tool. An experimental study of APCO-25 and LTE band class 14 is also conducted using software-defined radio, to enhance the understanding of the public safety systems. Finally, emerging technologies that may have strong potential for use in public safety networks are reviewed.Comment: Accepted at IEEE Communications Surveys and Tutorial

Diffusion of Cooperative Behavior in Decentralized Cognitive Radio Networks with Selfish Spectrum Sensors

This work investigates the diffusion of cooperative behavior over time in a decentralized cognitive radio network with selfish spectrum-sensing users. The users can individually choose whether or not to participate in cooperative spectrum sensing, in order to maximize their individual payoff defined in terms of the sensing false-alarm rate and transmit energy expenditure. The system is modeled as a partially connected network with a statistical distribution of the degree of the users, who play their myopic best responses to the actions of their neighbors at each iteration. Based on this model, we investigate the existence and characterization of Bayesian Nash Equilibria for the diffusion game. The impacts of network topology, channel fading statistics, sensing protocol, and multiple antennas on the outcome of the diffusion process are analyzed next. Simulation results that demonstrate how conducive different network scenarios are to the diffusion of cooperation are presented for further insight, and we conclude with a discussion on additional refinements and issues worth pursuing