Performance Evaluation of a Spectrum-Sensing Technique for Cognitive Radio Applications in B-VHF Communication Systems

Fantacci, R.; Tani, Andrea

doi:10.1109/tvt.2008.2005776

Cited by 28 publications

(19 citation statements)

References 11 publications

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“…Also, orthogonal frequency division multiplexing (OFDM) modulation parameters for aeronautical communication are presented. In [24], energy detector based spectrum sensing techniques are presented for broadband-VHF (B-VHF) aeronautical communication systems. In [25], the novel idea of time-limited leases (TLLs), where the equipment manufacturer can use an embedded software to enable the radio device to transmit at a particular frequency and transmission power based on the licensing agreement with the user.…”

Section: A Related Workmentioning

confidence: 99%

“…In contrast to [6] and [22], which focuses only on the UAV based networks, this paper presents a detailed survey of various aeronautical communication systems and their spectrum utilization trends In [21] and [24], issues in the interweave mode of cognitive radio are discussed. On the other hand in this work, all three operating modes, including underlay and overlay modes, are considered, and an exhaustive survey of the cognitive radio for aeronautical communication systems along with the open problems in the physical layer of the network are presented.…”

Section: A Related Workmentioning

confidence: 99%

“…Moreover, NonContinuous Interferometry Orthogonal Frequency Division Multiplex (NCI-OFDM) as it can efficiently mitigate the peakto-average power ratio problem and achieves better bit error rate (BER) in broadband multi-carrier communication systems. In [24], the noncooperative spectrum sensing technique for B-VHF overlay system is investigated. The spectrum sensing is based on the Welch windowed periodogram which falls under the class of energy detectors.…”

Section: A Cognitive Radio For Air-ground (A/g) Communicationmentioning

confidence: 99%

“…Finding the right balance between the sensing time and throughput is a tremendous challenge due to the very strict criteria for the probability of missed detection in aeronautical systems. Spectrum sensing from aeronautical communication systems are carried out in [105] and [24].…”

Section: B Challenges In Spectrum Sensingmentioning

confidence: 99%

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Cognitive Radio for Aeronautical Communications: A Survey

et al. 2016

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Novel Air Traffic Management (ATM) strategies are proposed through the Next Generation Air Transportation (NextGen) and Single European Sky for ATM Research (SESAR) projects to improve the capacity of the airspace and to meet the demands of the future air traffic. The implementation of the proposed solutions leads to increasing use of wireless data for aeronautical communications. Another emerging trend is the unmanned aerial vehicles. The unmanned aerial systems (UASs) need reliable wireless data link and dedicated spectrum allocation for its operation. On-board broadband connectivity also needs dedicated spectrum to satisfy the quality of service (QoS) requirements of the users. With the growing demand, the aeronautical spectrum is expected to be congested. However, the studies revealed that the aeronautical spectrum is underutilized due to the static spectrum allocation strategy. The aeronautical communication systems such as air-air and airground communication systems, inflight infotainment systems, wireless avionics intra-communications (WAIC), and UAS can benefit significantly from the introduction of cognitive radio based transmission schemes. This article summarizes the current trends in aeronautical spectrum management followed by the major applications and contributions of cognitive radio in solving the spectrum scarcity crisis in the aeronautical domain. Also, to cope with the evolving technological advancement, researchers have prioritized the issues in the case of cognitive radio that needs to be addressed depending on the domain of operation. The proposed cognitive aeronautical communication systems should also be compliant with the Aeronautical Radio Incorporated (AR-INC) and Aerospace Recommended Practice (ARP) standards. An overview of these standards and the challenges that need immediate attention to make the solution feasible for a largescale operation, along with the future avenues of research is also furnished.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Cognitive Radio For Air-ground (A/g) Communicationmentioning

confidence: 99%

Section: B Challenges In Spectrum Sensingmentioning

confidence: 99%

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Cognitive Radio for Aeronautical Communications: A Survey

et al. 2016

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“…Therefore, one of the first steps to be accomplished by a cognitive user is spectrum sensing: analysis of the received electromagnetic transmissions to search for unoccupied spectrum bands (spectrum holes). One of the ways to accomplish this is energy detection/spectral analysis and consequent (statistical) hypothesis testing [4]- [6], [8], [9]. Based on the received signal x(t), the cognitive user has to decide if the primary user (PU) is present or not.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radios over Frequency Selective Channels in White Noise

Tugnait

2011

2011 IEEE International Conference on Communications (ICC)

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Cognitive radio allows for usage of licensed frequency bands by unlicensed users when the licensed spectrum bands are unoccupied. Therefore, one of the first critical steps to be accomplished by a cognitive user is spectrum sensing: search for unoccupied spectrum bands (spectrum holes). A popular approach is that of energy detection whose implementation requires accurate knowledge of noise power. The performance of the energy detector deteriorates rapidly in the presence of noise power uncertainty. Recently several time-domain approaches relying on the generalized likelihood ratio test (GLRT) paradigm have been proposed for multiple antenna spectrum sensing which obviate the need for the knowledge of the noise variance, unlike the energy detector. That is, multiple antennas at the receivers are needed to tackle the problem of unknown noise power. In this paper we investigate a single-antenna method for signal detection in white noise based on analysis of the power spectral density (PSD) of the received signal relying on the bandlimited nature of the signal to be detected. Our proposed approach is also based on GLRT but exploits the fact that while noise is white, the signal is colored; it does not require knowledge of the noise power. A performance analysis of the proposed detector is carried out. Simulation examples are provided in support of the proposed approach.

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On seamless and high‐bandwidth connectivity for cognitive multi‐unmanned aerial vehicle‐assisted networks

Hasan

Ejaz

Zghaibeh

et al. 2020

Trans Emerging Tel Tech

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Unmanned aerial vehicles (UAVs) can be useful in many different scenarios including disaster management. UAVs can immediately reach the disaster area and collect data that can help relief and rescue activities. Nevertheless, these advantages can be further improved when multiple UAVs coordinate for data collection from different vantage points simultaneously. However, seamless, high‐bandwidth communication between UAVs is required for this coordination. Nonetheless, due to the scarcity of bandwidth, a UAV network operating on an unlicensed ISM band may not be feasible. In this article, we follow a conceptual approach in which UAVs can access the licensed spectrum opportunistically. However, it is highly likely that various UAVs may have different sets of approved channels available and are unable to coordinate. Therefore, UAVs are divided into clusters to establish coordination in this scenario based on the availability of a common channel between different UAVs. Clustering based on a single common channel may still not be practical, due to the sporadic availability of common channels that often causes reclustering. Therefore, we present a multi‐UAV clustering scheme in which clusters of UAVs are established with more common channels to prevent repeated reclustering by having one main channel and the rest as backup channels. However, this may reduce the size of the cluster which can be addressed by limiting the number of backup channels. In this article, we present two variants of the proposed scheme: Multi‐UAV clustering‐I and multi‐UAV clustering‐II. The multi‐UAV clustering‐I scheme attempts to cluster UAVs with as many common channels as possible, whereas the multi‐UAV clustering‐II scheme attempts to limit the number of common channels to two. In addition, we propose a method for choosing the main channel from the different channels, where these channels are rated on the basis of bandwidth capacity and the channel with the highest rank is selected as the main channel. The remaining and the rest of the channels are kept as a backup. Simulation results demonstrate smooth and high‐bandwidth communication among UAVs.

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Performance Evaluation of a Spectrum-Sensing Technique for Cognitive Radio Applications in B-VHF Communication Systems

Cited by 28 publications

References 11 publications

Cognitive Radio for Aeronautical Communications: A Survey

Cognitive Radio for Aeronautical Communications: A Survey

Spectrum Sensing for Cognitive Radios over Frequency Selective Channels in White Noise

On seamless and high‐bandwidth connectivity for cognitive multi‐unmanned aerial vehicle‐assisted networks

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