Wireless tomography, Part I: A novel approach to remote sensing

Qiu, Robert C.; Wicks, Michael C.; Li, L.; Hu, Zhen; Hou, Shujie; Chen, P.; Browning, James P.

doi:10.1109/wdd.2010.5592613

Cited by 20 publications

(9 citation statements)

References 320 publications

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“…In the future, more potential applications can be implemented on the cognitive radio network testbed, such as smart grid [17], [39], [40], and wireless tomography [41], [42].…”

Section: Discussionmentioning

confidence: 99%

Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

Chen

Guo

et al. 2011

IEEE Trans. Veh. Technol.

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Abstract-As a part of the effort toward building a cognitive radio network testbed, we have demonstrated real-time spectrum sensing. Spectrum sensing is the cornerstone of cognitive radio. However, current hardware platforms for cognitive radio introduce time delays that undermine the accuracy of spectrum sensing. The time delay, named response delay, incurred by hardware and software can be measured at two antennas colocated at a secondary user (SU), the receiving antenna and the transmitting antenna. In this paper, minimum response delays are experimentally quantified and reported based on two hardware platforms, the universal software radio peripheral 2 (USRP2) and the small form factor software defined radio development platform (SFF SDR DP). The response delay has negative impact on the accuracy of spectrum sensing.

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“…In the future, more potential applications can be implemented on the cognitive radio network testbed, such as smart grid [17], [39], [40], and wireless tomography [41], [42].…”

Section: Discussionmentioning

confidence: 99%

Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

Chen

Guo

et al. 2011

IEEE Trans. Veh. Technol.

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“…Distributed cognitive sensing has materialized by intrusion detection using machine learning, joint spectrum sensing and localization [15], distributed aspect synthetic aperture radar, wireless tomography [16], [17], [18], closed-loop wide-band cognitive sensing [6], mobile crowdsensing [19], and so on. The following experiments are really initial results to demonstrate the motivated applications.…”

Section: Cognitive Radio Network As Sensors: Experiments and Lesmentioning

confidence: 99%

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

Qiu

Zhang

et al. 2012

JCM

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This paper presents a comprehensive review of the cognitive radio network (CRN) testbed built at TTU. Our goals are (1) to use our CRN testbed as a data acquisition tool; (2) to use random matrix theory to model the collect data and apply the new models in the context of quantum information. We attempt to achieve a balance between experimental work and theoretical work. We first spell out the vision and concrete tasks for our research in the near future. Second, we review our latest results in an more accessible manner than the conference version.

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“…Beamforming, coherent tomography, and SAR are just a few of the applications that we intend to use the USRPs for, all of which require a way to accurately measure phase information [11] [12] [13] [14]. This means that we need to ensure that when using multiple USRP nodes, all nodes begin sampling and continue to sample in unison.…”

Section: B Phase Error For Usrp Receiver Pairsmentioning

confidence: 99%

Evaluation of phase error in a software-defined radio network testbed

Corum

Bonior

Qiu

et al. 2012

2012 Proceedings of IEEE Southeastcon

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When building a sensor network testbed each system component must be completely understood both in its operation and as a possible source of error. We seek to quantify phase error in the USRP-N210 software-defined radio in order to determine how accurately this error can be calibrated out of a network of USRP-N210 based sensor nodes. We measure the frequency stability of a single tone created using a USRP-N210. We also measure the phase difference between two receivers which have been synchronized and are receiving the same signal.

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Wireless tomography, Part I: A novel approach to remote sensing

Cited by 20 publications

References 320 publications

Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

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

Evaluation of phase error in a software-defined radio network testbed

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