Cooperative Spectrum Sensing in the Vehicular Environment: An Experimental Evaluation

Kremo, Haris; Altintas, Onur; Tanaka, Hideaki; Kitamura, Masayuki; Inage, Kei; Fujii, Takeo

doi:10.1109/vtcspring.2014.7022866

Cited by 7 publications

(14 citation statements)

References 5 publications

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“…3). This provided us with a range of signal-to-noise ratios (SNRs) for testing of detection performance of individual sensors and different combining algorithms, reported in [3]. However, a strong signal is preferable to improve accuracy of propagation modeling, and for that reason here we use measurements from the two channels given in Table I.…”

Section: Methodsmentioning

confidence: 99%

“…5. The car route west of the tower is selected intentionally for the detection experiments in [3], but it is less favorable for the propagation related analysis. Therefore, we use only the data for distances between the relay tower and the cars shorter than 570 m, marked with the right vertical line in Figs.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the averaging involves measurements reported over all vehicles and all six passes of the trajectory. This also smooths out systematic error across cars which remains even after careful calibration described in [3]. As a consistence check, we performed averaging over 5λ and 20λ as well, and it appropriately produced slightly different results.…”

Section: Path Lossmentioning

confidence: 99%

“…This work is an extension of [3], in which we used mobile energy detectors operating in the UHF TV band to implement and evaluate cooperative spectrum sensing in the vehicular environment. Although we process the data post-mortem (long time after experiments are finished) it is straightforward to implement processing in a manner depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of First and Second Order Statistics of Large Scale Fading Using Vehicular Sensors

Kremo¹,

Nakagawa

Altintas³

et al. 2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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We propose and demonstrate a framework for statistical modeling of propagation using spectrum sensors mounted on moving vehicles. To illustrate the concept, we deploy sensors on four vehicles to collect signal strength measurements together with corresponding locations on a suburban road. From the data we build a model for large scale fading, that is, the path loss and shadowing, as well as the shadowing correlation of a TV transmitter on two UHF channels. Depending on a specific band to which it is applied, such a model can be used for different purposes like coverage planning, to improve accuracy of a white space spectrum database, for selection of relay nodes in an ad-hoc or a mesh network, selection of spectrum sensors with significant diversity gain for cooperative sensing, etc. For instance, obtained numerical results imply that sufficient decorrelation of signal strength can be observed in the experimental area if the sensors are separated by more than 20 m.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Path Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterization of First and Second Order Statistics of Large Scale Fading Using Vehicular Sensors

Kremo¹,

Nakagawa

Altintas³

et al. 2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

Self Cite

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“…Cooperative spectrum sensing (CSS) has been extensively investigated in efforts to improve the detection performance via the diversity gain of cooperative secondary users (SUs) in CVNs [10][11][12][13][14]. These papers have shown that CSS can achieve spatial diversity gains under the assumption that the collaborative SUs are proactive.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Robust and Low‐Complexity Cooperative Spectrum Sensing via Low‐Rank Matrix Recovery in Cognitive Vehicular Networks

Liu

Zeng

Guo

2018

Wireless Communications and Mobile Computing

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In cognitive vehicular networks (CVNs), many envisioned applications related to safety require highly reliable connectivity. This paper investigates the issue of robust and efficient cooperative spectrum sensing in CVNs. We propose robust cooperative spectrum sensing via low-rank matrix recovery (LRMR-RCSS) in cognitive vehicular networks to address the uncertainty of the quality of potentially corrupted sensing data by utilizing the real spectrum occupancy matrix and corrupted data matrix, which have a simultaneously low-rank and joint-sparse structure. Considering that the sensing data from crowd cognitive vehicles would be vast, we extend our robust cooperative spectrum sensing algorithm to dense cognitive vehicular networks via weighted low-rank matrix recovery (WLRMR-RCSS) to reduce the complexity of cooperative spectrum sensing. In the WLRMR-RCSS algorithm, we propose a correlation-aware selection and weight assignment scheme to take advantage of secondary user (SU) diversity and reduce the cooperation overhead. Extensive simulation results demonstrate that the proposed LRMR-RCSS and WLRMR-RCSS algorithms have good performance in resisting malicious SU behavior. Moreover, the simulations demonstrate that the proposed WLRMR-RCSS algorithm could be successfully applied to a dense traffic environment.

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Vehicular Communications Over TV White Spaces in the Presence of Secondary Users

2019

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The lack of available spectrum for wireless communications is a threat to the successful deployment of applications designed for intelligent transportation systems (ITSs). The ITS services should be available to a high number of road users and have a fast response time. The interworking between radio access networks is one way to increase spectrum availability. In particular, the joint operation of the dedicated short-range communication (DSRC) technology and TV white spaces (TVWS) has been proposed to increase the dissemination distance for safety messages in vehicular networking. However, previous works have often assumed that the only restriction on the opportunistic access of TVWS is the presence of a TV transmitter (i.e., the primary user). Other secondary users, such as the popular White-Fi networks to be deployed in TV bands, are omitted from the analysis of opportunistic channel access over TVWS. This is despite several proposals in the literature that use secondary networks for purposes other than vehicular networking over TVWS. In this paper, we analyze the opportunistic use of TVWS when other fixed users, such as White-Fi networks, are present. We estimate channel access opportunities and introduce a new metric, the channel availability for opportunistic vehicular access (CAFOVA), which relates the channel occupancy of the White-Fi network, the speed of the vehicle, and the channel verification distance. The results show that there are opportunities for vehicular access even when a White-Fi network occupies the TVWS. Vehicles may use these opportunities for transmission, instead of spending time looking for a new available TVWS and establishing a new link with another vehicle. Therefore, even when a White-Fi network occupies the same TVWS, it may be possible to exploit dynamic spectrum access to extend the available spectrum for vehicular communications.INDEX TERMS TV primary user, TV white spaces, vehicular dynamic spectrum access, White-Fi network.The associate editor coordinating the review of this manuscript and approving it for publication was Arun Prakash.active road safety, cooperative traffic efficiency, and information and entertainment (also known as Infotainment). As shown in Table 1, each category has different latency, coverage, and data rate requirements, but all categories are subject to performance challenges due to the different node speeds and network densities encountered in vehicular scenarios.Channel availability is a fundamental requirement for the successful operation of vehicular applications, but the lack of available spectrum for wireless technologies is a threat for current and future applications designed for ITS. For example, in the presence of traffic congestion, the sending of a critical warning message may fail when concurrent transmissions

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Cooperative Spectrum Sensing in the Vehicular Environment: An Experimental Evaluation

Cited by 7 publications

References 5 publications

Characterization of First and Second Order Statistics of Large Scale Fading Using Vehicular Sensors

Characterization of First and Second Order Statistics of Large Scale Fading Using Vehicular Sensors

Robust and Low‐Complexity Cooperative Spectrum Sensing via Low‐Rank Matrix Recovery in Cognitive Vehicular Networks

Vehicular Communications Over TV White Spaces in the Presence of Secondary Users

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