Towards A Large-Scale Cognitive Radio Network Testbed: Spectrum Sensing, System Architecture, and Distributed Sensing

Qiu, Robert C.; Zhang, Changchun; Hu, Zhen; Wicks, Michael C.

doi:10.4304/jcm.7.7.552-566

Cited by 14 publications

(6 citation statements)

References 67 publications

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“…In [6], the design issues and research challenges in the physical layer, MAC layer, network layer, transport layer and application layer are presented. Systematic analysis quantifying the benefits of adopting DSA technology by CR enabled UAS is carried out in [111], [125] which is benchmarked against the baseline method that resorts to manual spectrum planning and fixed frequency allocation. Also, the performance analysis of 802.11a wireless links from UAV to ground nodes has been presented in [126] while the incorporation of wireless relay communications with UAV has been discussed in [127].…”

Section: Cognitive Radio For Uasmentioning

confidence: 99%

“…However, the VHF radio spectrum allocated for aeronautical communications which are 19 MHz wide and spread over [118][119][120][121][122][123][124][125][126][127] MHz band provides only 760 amplitude modulated (AM)-radio channels with 25 KHz of spectral spacing. With the increase in demand for more voice channels, the 25 KHz channels are further divided into three sub-channels of 8.…”

Section: Introductionmentioning

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: Cognitive Radio For Uasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive Radio for Aeronautical Communications: A Survey

et al. 2016

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“…The classification and suppression method for interference was proposed based on the modulation of signals, and achieved the anti-interference performance of frequency-hopping systems [27]. However, most of these previous literatures suppressed the particular interference by the classification and transformation analysis, they rely gravely on interference detection and characteristic parameters [28], which restrict the effectiveness and robustness in applications. Therefore, we seek the processing method for interference with transformation and classification simultaneously in this paper.…”

Section: B Interference Processingmentioning

confidence: 99%

Classification Methods With Signal Approximation for Unknown Interference

Wang

Huang

et al. 2020

IEEE Access

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There has been growing interest in the classification of interference types in communication systems, especially under large samples and unknown interference, which severely restrict anti-jamming performance of the system. In this paper, we present two signal approximation algorithms for classification restricted different conditions, and the transform learning label consistency (TLLC) is embedded into the evaluation owing to the imperfect performance for classification and feature library. First, the interference signals are converted into the signal feature space, and then the interference processing and feature extraction are conducted based on the Hilbert signal space theory. Second, the projection approximation (PA) for signal approximation is used to approximate the unknown interference, and the restricted projection property is demonstrated as well. Furthermore, in order to ease the restrictions, the sparse approximation (SA) for interference signals is demonstrated. Moreover, an unsupervised learning method and the unknown interference classifier are proposed based on the self-organizing map (SOM) neural network. Based on l1 minimization functions, we improve the accuracy of TLLC with sparse approximation, which is more suitable for general interference signals. Finally, the simulation results demonstrate that, compared with the traditional classification method, the proposed method improves the classification accuracy of known interference by 3.44%. In this case, the overall accuracy is close to that of the supervised learning method, and the speed of processing interference is increased by more than 10 times. When the SNR reaches 5 dB, the accuracy of unknown interference classification exceeds 94%. Finally, yet importantly, owing to the imperfect performance for classification and feature library at present, we acquire the final accuracy for them at 92.23% by intervening measures, and the time availability also has been obtained advantages on signal processing.

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“…Large sample work in multivariate analysis has traditionally assumed that N L , the number of observations per variable, is large. Today, it is common for L to be large or even huge, and so N L may be moderate to small and in extreme cases less than one [34]. In such case, an appropriate covariance matrix estimation is essentially needed.…”

Section: B Sample Covariance Matrixmentioning

confidence: 99%

Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis

Lin

Qiu

Browning

2015

IEEE Trans. Veh. Technol.

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Spectrum sensing is a fundamental component of cognitive radio. How to promptly sense the presence of primary users is a key issue to a cognitive radio network. The time requirement is critical in that violating it will cause harmful interference to the primary user, leading to a system-wide failure. The motivation of our work is to provide an effective spectrum sensing method to detect primary users as soon as possible. In the language of streaming based real-time data processing, short-time means small-sized data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmid's concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV signal, show that this proposed method can operate either using smaller-sized data or working under lower SNR environment. ). J. P. Browning is with Sensors Directorate, AFRL/RYMD,

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Towards A Large-Scale Cognitive Radio Network Testbed: Spectrum Sensing, System Architecture, and Distributed Sensing

Cited by 14 publications

References 67 publications

Cognitive Radio for Aeronautical Communications: A Survey

Cognitive Radio for Aeronautical Communications: A Survey

Classification Methods With Signal Approximation for Unknown Interference

Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis

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