A Survey on Spectrum Occupancy Measurement for Cognitive Radio

Das, Deepa; Das, Susmita

doi:10.1007/s11277-015-2921-1

Cited by 35 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two main factors that cause spectrum scarcity are as follows: (a) frequency bands are allocated to licensed users based on the traditional fixed spectrum assignment policy and (b) a huge volume of real-time data is generated and transferred over a wireless medium in a dynamic environment. The authors in [4] showed that most bands are still vacant and suitable for secondary usage.…”

Section: Introductionmentioning

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

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: Introductionmentioning

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

“…A CR is an intelligent software defined radio (SDR) that reconfigures its network parameters based on the network environment [9]. Preliminary work has shown that much of the licensed bands allocated to certain users are underutilized [10]. Unlicensed users, also called secondary users (SUs) can opportunistically access these underutilized radio frequency bands whenever the licensed users or primary users (PUs) are absent.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Spectrum Decision in Cognitive Vehicular Network Based on Support Vector Machine

Chembe

Ahmedy

Noor

et al. 2019

MJCS

View full text Add to dashboard Cite

Spectrum sensing is one of the core functionality of a cognitive radio system. Accurate identification of primary system signal can prevent interference and provide maximum protection to licensed users. However, many challenges associated with spectrum sensing still exist. Examples include shadowing, multipath fading of the primary user signal, hidden primary user problem and many more. To circumvent the challenges faced by sensing device, the cooperative decision from participating devices has been proposed in the literature. In this paper, we suggest a cooperative decision based on support vector machine (SVM) for spectrum sensing in vehicular communication environment to mitigate shadowing and multipath fading. Spectrum sensing can be formulated as a pattern recognition problem with sensing results grouped into two distinct classes corresponding to the presence or absence of a primary user signal. Therefore, we employ SVM at the fusion centre to classify the primary signal correctly. The roadside units along the highway are selected to act as the fusion centre where sensing decision is made. The performance of the proposed scheme is evaluated using receiver operation characteristic curves in MATLAB. We compare the probability of detection for the proposed cooperative scheme to hard fusion rule. The simulation result shows that SVM based cooperative decision performed better than hard fusion rule.With an increase in the number of emerging applications and services developed for wireless technologies, demand for bandwidth has been growing steadily. For example, it is envisioned that by 2020, there will be more than 50 billion devices connected to the Internet mostly through wireless communication [3]. This poses a major challenge on the already scarce radio frequency spectrum which is a finite resource. For instance, the Federal Communications Commission (FCC) in the USA has allocated almost all the channels in the communicable frequency bands to licensed users [4]. The unlicensed spectrum channels are reserved for Industrial, Scientific and Medical (ISM) usage and shared among different radio technologies such as Bluetooth, WiFi, Microwave ovens and emerging radio technologies. ITS applications have been allocated 75MHz at the 5.9GHz band for dedicated short range communication (DSRC) for V2V and V2I communications [5]. The 75MHz is divided into 7 channels with 10MHz each. 5MHz is reserved as guard band channel. Regardless, these channels can become congested, particularly during peak hours or accident scenarios, when the number of vehicles contending for the same channels increases [6]. In such situations, the delivery of delay sensitive safety and emergency messages becomes difficult.

show abstract

“…It has raised the demand of extra spectrum bands to solve the problems of spectrum scarcity. Due to static spectrum band allocation policy by the government agencies, some of the allocated spectrum bands are underutilized such as some portion of TV VHF/UHF bands, so-called TV white spaces [1][2][3][4]. To solve the problems of spectrum scarcity, US Federal Communication Commission (FCC) has suggested to use the underutilized spectrums and spectrum potions which are not in use, called vacant spectrum bands or white spaces [3].…”

Section: Introductionmentioning

confidence: 99%

A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

Kumar

Prakash

Tripathi

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

When a mobile network changes its point of attachments in Cognitive Radio (CR) vehicular networks, the Mobile Router (MR) requires spectrum handoff. Network Mobility (NEMO) in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM) methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

show abstract

A Survey on Spectrum Occupancy Measurement for Cognitive Radio

Cited by 35 publications

References 30 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

Cooperative Spectrum Decision in Cognitive Vehicular Network Based on Support Vector Machine

A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

Contact Info

Product

Resources

About