Historical Spectrum Sensing Data Mining for Cognitive Radio Enabled Vehicular Ad-Hoc Networks

Huang, Xin-Lin; Wu, Jun; Li, Wenfeng; Zhang, Zhifeng; Zhu, Fusheng; Wu, Minghao

doi:10.1109/tdsc.2015.2453967

Cited by 62 publications

(24 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DSA has been applied to vehicular networks e.g. using the TV white space band [17] or exploiting historical spectrum sensing data [18]. In heterogeneous networking, all users are considered primary users and they are all equipped with multiple RATs that are assigned specific channels and spectrum bands.…”

Section: State Of the Artmentioning

confidence: 99%

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Sepulcre

Gozálvez

2021

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Connected and automated vehicles will enable advanced traffic safety and efficiency applications thanks to the dynamic exchange of information between vehicles, and between vehicles and infrastructure nodes. Connected vehicles can utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, a widespread deployment of connected vehicles and the introduction of connected automated driving applications will notably increase the bandwidth and scalability requirements of vehicular networks. This paper proposes to address these challenges through the adoption of heterogeneous V2V communications in multi-link and multi-RAT vehicular networks. In particular, the paper proposes the first distributed (and decentralized) context-aware heterogeneous V2V communications algorithm that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select its communications technology taking into account its application requirements and the communication context conditions. This study demonstrates the potential of heterogeneous V2V communications, and the capability of the proposed algorithm to satisfy the vehicles' application requirements while approaching the estimated upper bound network capacity.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Sepulcre

Gozálvez

2021

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…The proposed scheme exploits the spatial and temporal diversities for spectrum sensing and access in a cognitive vehicular environment. In [35], a spectrum sensing which predicts availability channel is proposed. The scheme uses prior knowledge of channel availability probability and Bayesian inference to predict free channels in the future.…”

Section: Related Sensing Schemes In Cvnmentioning

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

“…Once a spectrum occupancy prediction is requested, all the edges originating from the starting point are listed and then the destination is predicted according to the approximately calculated maximum weights. Huang et al [174] propose a Bayesian inference-based prediction algorithm for spotting the specific channel that is most likely to be available for the CR-aided vehicular ad-hoc networks, where the most critical challenges are the high-speed mobility of vehicles and the dynamically-fluctuating channel availability. As a further advance, P. Thakur et al [175] propose a proactive spectrum prediction technique, where the emergence of PU is predicted before its true emergence, in order to avoid dropping even a single packet.…”

Section: Spectrum Inference For Mobilitymentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

View full text Add to dashboard Cite

Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

show abstract

Historical Spectrum Sensing Data Mining for Cognitive Radio Enabled Vehicular Ad-Hoc Networks

Cited by 62 publications

References 33 publications

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

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

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Contact Info

Product

Resources

About