The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Cognitive radio (CR)-based Internet of Things (IoT) system is an effective step toward a world of smart technology. Many frameworks have been proposed to build CR-based IoT systems. The CR-based IoT frameworks are the key points on which this survey focuses. Efficient spectrum sensing and sharing are the main functional components of the CR-based IoT. Reviews of recent SS and sharing approaches are presented in this survey. This survey classifies the SS and sharing approaches and discusses the merits and limitations of those approaches. Moreover, this survey discusses the design factors of the CR-based IoT and the criteria by which the proper SS and access approaches are selected. Furthermore, the survey explores the integration of newly emerging technologies with the CR-based IoT systems. Finally, the survey highlights some emerging challenges and concludes with suggesting future research directions and open issues.

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Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed user, called primary user, is not occupying it. The spectrum sensing approaches can be classified as blind and knowledge aided approaches. This tutorial summarizes blind spectrum sensing (BSS) approaches that require no prior knowledge of the licensed user’s signal characteristics, specifically for an interweave cognitive radio network model. The tutorial provides a thorough background, major implementations, and limitations of the BSS approaches, which are energy detector approach, maximum to minimum eigenvalue approach, maximum eigenvalue approach, covariance absolute value approach, and covariance Frobenius norm approach. Moreover, the tutorial compares these approaches based on performance metrics and complexity requirements. Furthermore, for a higher interference protection, the combination of two different spectrum sensing approaches, namely two-stage detection technique is presented and discussed. Besides, the tutorial discusses the challenges and possible future research directions. The fundamental objective of this tutorial is to provide insightful views and design aspects of BSS approach to researchers. For this purpose, the tutorial includes pseudo codes and simulation examples to illustrate more about the practical aspects of the above-mentioned approaches.

Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

This paper presents two-stage detectors for spectrum sensing in cognitive radio networks. The first stage consists of multiple energy detectors, where each energy detector (ED) has a single antenna with fixed threshold for making a local binary decision. If required, the second stage comprised of ED with adaptive double threshold is invoked. The detection performance of the proposed scheme is compared with a cyclostationary-based sensing method and adaptive spectrum sensing scheme. The numerical results show that the proposed scheme improves detection performance and outperforms the cyclostationary-based sensing method and adaptive spectrum sensing by 12.3% and 14.4% at signal-to-noise ratio (SNR) setting of as low as -8 dB, respectively. Performance was also measured in terms of sensing time and scheme has smaller sensing time than cyclostationary detection and adaptive spectrum sensing scheme in the order of 4.3 and 0.1 ms at -20-dB SNR, respectively. Furthermore, the scheme was analyzed in conjunction with cooperative spectrum sensing (CSS), where each cognitive radio (CR) user used two-stage spectrum sensing detectors for local detection and sent detection decisions to the fusion center to give the final decision based on hard decision OR rule. It is further found that the proposed two-stage spectrum sensing scheme with CSS achieves spectrum detection performance in the order of 0.9 for an SNR value of as low as -19 dB as the number of CR users (K) is 10.

Cooperative Spectrum Sensing Based on Two-Stage Detectors With Multiple Energy Detectors and Adaptive Double Threshold in Cognitive Radio Networks

OFDM system with cyclostationary feature detection spectrum sensing

Cognitive radio is the key technology for future wireless communication. Spectrum sensing is one of the most important functions in cognitive radio (CR) applications. It involves the detection of primary user (PU) transmissions on a preassigned frequency band. PU licensed band can be sensed via appropriate spectrum sensing techniques. In this paper, we consider three basic spectrum sensing techniques of transmitter detection: Matched filter detection, Energy detection, and Cyclostationary feature detection. Using simulations, a comparative analysis of the three techniques has been carried out in terms of probability of false alarm Pf, probability of detection alarm Pd, and probability of miss detection Pm. Finally, Numerical result shows that at low signal to noise ratio (SNR), cyclostationary feature detection outperforms other two techniques, thus have some difficulties like implementation is complex, long observation time, etc. For simulation we used MATLAB software.

Comparative Performance Evaluation of Spectrum Sensing Techniques for Cognitive Radio Networks

In cognitive radio networks, spectrum sensing is used to sense the unused spectrum in an opportunistic manner. In this paper, we propose an energy detector utilizing adaptive double threshold (ED_ADT) for spectrum sensing, which improves detection performance as well as overcomes sensing failure problem. The detection threshold is made adaptive to the fluctuation of the received signal power in each local detector of secondary user. Numerical results show that proposed ED_ADT scheme outperforms conventional energy detector by 12.8% at –8 dB signal-to-noise ratio (SNR) in terms of probability of detection alarm (P d). While utilizing cooperative spectrum sensing (CSS) with ED_ADT scheme, it is further found that adaptive double threshold improves detection performance around 26.8% and 7.6% as compared to CSS with single threshold and hierarchical with quantization method at –10 dB SNR, respectively, under the case when a small number of sensing nodes are used in spectrum sensing.

Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks

A mobile ad hoc network (MANET) consists of mobile wireless nodes. The communication between these mobile nodes is carried out without any centralized control. The ease of deployment and the infrastructure less nature of Mobile Ad hoc Networks (MANETs) make them highly desirable for the present day multi media communications. Traditional routing protocols may not suffice for real time communications it depends upon the conditions and our requirements. Though there has been considerable research in this area. In this paper, we are analyzing the performance of reactive routing protocol via increasing number of nodes and observing its effect on Quality of Service (QoS) of Mobile Adhoc Network. As we know routing protocols make an important role for improving QoS in Mobile Adhoc Network. The QoS depends upon several parameters like end-end delay, throughput, date drop and network load. The reactive routing protocol which we are considering is AODV for this scenario with MCHG [1] [2]. Here we are observing performance of Routing Protocol via enhancing the network size on the basis of following parameters: delay, throughput, traffic sent, traffic received, data dropped and network load. Network simulation tool used in simulation is OPNET Modeler (Ver. 14.0). Finally, this paper conducts simulation experiments in the conditions where we can improve QoS of MANET Network performance.

Performance of AODV Routing Protocol with Increasing the MANET Nodes and Its Effects on QoS of Mobile Ad Hoc Networks

Cognitive radio (CR) is a regulated technique for opportunistic access of idle resources. In CR, spectrum sensing is one of the key functionalities. It is used to sense the unused spectrum in an opportunistic manner. In this paper, we have proposed two-stage detectors for spectrum sensing in cognitive radio networks (CRN). The first stage consists of multiple energy detectors (MED), where each energy detector (ED) is having single antenna with fixed threshold (MED&#x5f;FT) for making a local binary decision, and if required, the second stage comprised of ED with adaptive double threshold (ED&#x5f;ADT) is invoked. The detection performance of the proposed scheme is compared with cyclostationary-based sensing method and adaptive spectrum sensing scheme. Numerical results show that the proposed scheme improves detection performance and outperforms the cyclostationary-based sensing method and adaptive spectrum sensing by 12.3&#x25; and 14.4&#x25; at signal to noise ratio (SNR) setting of as low as &#x2212;8&#x2009;dB, respectively. Performance was also measured in terms of sensing time. It is shown that the proposed scheme yields smaller sensing time than cyclostationary detection and adaptive spectrum sensing scheme in the order of 4.3&#x2009;ms and 0.1&#x2009;ms at &#x2212;20&#x2009;dB SNR, respectively.

https://journals.sagepub.com/doi/pdf/10.1155/2013/656495

Ashish Bagwari

Papers

Comparative Performance Evaluation of Spectrum Sensing Techniques for Cognitive Radio Networks

Cooperative Spectrum Sensing Based on Two-Stage Detectors With Multiple Energy Detectors and Adaptive Double Threshold in Cognitive Radio Networks

Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks

Performance of AODV Routing Protocol with Increasing the MANET Nodes and Its Effects on QoS of Mobile Ad Hoc Networks

Two-Stage Detectors with Multiple Energy Detectors and Adaptive Double Threshold in Cognitive Radio Networks