Non-cooperative Spectrum Sensing in Context of Primary User Detection: A Review

Nadeem, Aamir; Khan, Murad; Han, Kijun

doi:10.1080/02564602.2016.1162117

Cited by 8 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main task of SS is channel selection and vacant primary spectrum identification. Spectrum sensing is performed via three primary approaches, namely, cooperative, non-cooperative, and interference-based detection [173]. They are described as follows:…”

Section: Crmentioning

confidence: 99%

“…It is also known as primary transmitter detection. Non-cooperative detection methods include energy detection, matched filter detection, cyclostationary feature detection, wavelet-based detection, and covariance matrix-based detection [173]. Non-cooperative detection methods incur a small overhead; however, they depend on the network infrastructure, which may not be available at all places and may be affected by noise, interference, and the problem of hidden PUs.…”

Section: B: Non-cooperative Ssmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

View full text Add to dashboard Cite

Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

show abstract

Section: Crmentioning

confidence: 99%

Section: B: Non-cooperative Ssmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Now the research into spectrum sensing technology can be divided into non-cooperative spectrum sensing and cooperative spectrum sensing. The non-cooperative spectrum sensing technology is the main spectrum sensing technology in CWSN which can be summarized as follows [11,12]: energy detectors [13,14,15,16], feature detection [17,18,19,20], and matched filter detection [21,22,23]. The approaches are summarized in Table 1, which shows the algorithm, its limitation and the relevant recent references [24].…”

Section: Related Workmentioning

confidence: 99%

Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

Chen

Yin

et al. 2019

Sensors

View full text Add to dashboard Cite

The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from −19dB to −12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs.

show abstract

“…synchronization message, presenter, spectral scattering sequences, training and pilot patterns [28]. In non-cooperative detection, CR-IoT users do not need to exchange sensing information with other CR-IoT users [29]. In this method, the performance of spectrum detection is reduced due to concealed terminal issues, multi-path fading, and shadow impact [30].…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Miah

et al. 2021

Preprint

View full text Add to dashboard Cite

Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT) networks for utilization of the licensed spectrum accurately. However, the performance of the conventional Energy Detector (ED) method is compromised in a noise-uncertain environment owing to interference constraints, i.e. the CR-IoT user interference with the licensed Primary User (PU) on the same licensed band. To overcome this drawback, we proposed an energy efficient Cooperative Spectrum Sensing (CSS) for a CR-IoT network with interference constraints using a novel ED method. In this method, each CR-IoT user is capable of spectrum sensing that makes both the local decision and the weight factor based on the sequential approach; we calculate the weight factor against each CR-IoT user based on the Kullback Leibler Divergence award score. After the local decision and the weight factor are made, each CR-IoT user transmits its measured both the local decisions, and the weight factor to a Fusion Center (FC), which is made a final decision about the PU activities based on the hard fusion rule. The simulation results demonstrates that the proposed ED method obtains an improved detection performance, an enhanced sum rate, a spectral efficiency, an energy efficiency, and a lower global error probability when compared to other conventional ED methods under time varying environments.

show abstract

Non-cooperative Spectrum Sensing in Context of Primary User Detection: A Review

Cited by 8 publications

References 45 publications

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Contact Info

Product

Resources

About