On the Delay to Reliably Detect Channel Availability in Cooperative Vehicular Environments

Borota, Dusan; Ivkovic, Goran; Vuyyuru, Rama; Altintas, Onur; Seskar, Ivan; Spasojević, Predrag

doi:10.1109/vetecs.2011.5956717

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…The scheme uses prior knowledge of channel availability probability and Bayesian inference to predict free channels in the future. Other cooperating sensing schemes proposed based on hard fusion rule includes [33] and [34]. In hard fusion, only one bit (1 or 0) is sent to the fusion center for determining the PU occupant state.…”

Section: Related Sensing Schemes In Cvnmentioning

confidence: 99%

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

Chembe

Ahmedy

Noor

et al. 2019

MJCS

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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.

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Section: Related Sensing Schemes In Cvnmentioning

confidence: 99%

“…In Fig. 6 and 7, we compare the performance of SVM based model to hard fusion rule from literature [33] [17] [34]. In the hard combining scheme, each vehicle senses the channel of interest and sends the binary output to the RSU.…”

Section: Fig 5: Detection Probability With Different Number Of Vehiclesmentioning

confidence: 99%

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

Chembe

Ahmedy

Noor

et al. 2019

MJCS

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“…The data sensed by the vehicles is sent to RSUs that forward the gathered data further to a central processing unit. The delay and reliability of spectrum sensing in vehicular environments is discussed in [74]. They argue that cooperative spectrum sensing can be used to tackle the sensing limitations of a single vehicle as it exploits spatial diversity.…”

Section: Spectrum Sensingmentioning

confidence: 99%

Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

Singh¹,

Rawat

Bonnin³

2014

J Wireless Com Network

150

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International audienceWith growing interest in using cognitive radio (CR) technology in wireless communication systems for vehicles, it is envisioned that future vehicles will be CR-enabled. This paper discusses CR technologies for vehicular networks aimed at improving vehicular communication efficiency. CR for vehicular networks has the potential of becoming a killer CR application in the future due to a huge consumer market for vehicular communications. This paper surveys novel approaches and discusses research challenges related to the use of cognitive radio technology in vehicular ad hoc networks. We review how CR technologies such as dynamic spectrum access, adaptive software-defined radios, and cooperative communications will enhance vehicular communications and, hence, present the potential of transforming vehicle communication in terms of efficiency and safety. Our work is different from existing works in that we provide recent advances and open research directions on applying cognitive radio in vehicular ad hoc networks (CR-VANETs) focusing on architecture, machine learning, cooperation, reprogrammability, and spectrum management as well as QoE optimization for infotainment applications. A taxonomy of recent advances in cognitive radio for vehicular networks is also provided. In addition, several challenges and requirements have been identified. The research on applying CR in vehicular networks is still in its early stage, and there are not many experimental platforms due to their complex setup and requirements. Some related testbeds and research projects are provided at the end

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“…[11] investigates cooperative spectrum sensing performance in a vehicular networks to detect available channels, to utilize more spectrum usage and an efficient congestion control mechanism. Moreover, [12] analyzes spectrum decision challenges in CRAHNs (Cognitive Radio Ad-Hoc Networks) and proposes a spectrum decision mechanism to make a decision about channel usage.…”

Section: Introductionmentioning

confidence: 99%

Robust and continuous connectivity maintenance for vehicular dynamic spectrum access networks

Bozkaya

Canberk

2015

Ad Hoc Networks

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On the Delay to Reliably Detect Channel Availability in Cooperative Vehicular Environments

Cited by 12 publications

References 18 publications

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

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

Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

Robust and continuous connectivity maintenance for vehicular dynamic spectrum access networks

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