Cloud-Aided Cognitive Ambient Backscatter Wireless Sensor Networks

Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a primary signal, without requiring any type of power-consuming active components or other signal conditioning units. In such a network, the pe...

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Published inIEEE access Vol. 7; pp. 57399 - 57414
Main Authors Darsena, Donatella, Gelli, Giacinto, Verde, Francesco
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Ambient backscatter
Backscatter
Backscattering
Channel estimation
Cloud computing
cloud radio access network
cognitive radio
Computer architecture
data rate maximization
energy harvesting
Energy storage
interference suppression
Microprocessors
Modulation
Nodes
Optimization
passive and semi-passive sensors
performance analysis
Power consumption
Power management
Sensors
State (computer science)
Wireless communication
Wireless communications
Wireless networks
Wireless sensor networks
Online AccessGet full text
ISSN2169-3536
2169-3536
DOI10.1109/ACCESS.2019.2914001

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Abstract Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a primary signal, without requiring any type of power-consuming active components or other signal conditioning units. In such a network, the performance of the backscatter system can be severely degraded by channel estimation errors and co-channel direct-link interference (DLI) from the primary system. To overcome these shortcomings, we consider a cloud radio access network (C-RAN) architecture, where both the primary and secondary edge nodes are connected to a cloud processor via high-speed links. In this centralized architecture, secondary edge nodes provide network access to ambient backscatter passive and semi-passive sensors with communication capabilities, and the problem of acquiring channel state information and suppressing the DLI is managed by the cloud processor. In particular, we assess the performance of the secondary backscatter sensor transmission in a realistic system setup, which takes into account training-based channel estimation, practical modulation constraints, and imperfect DLI suppression. In addition, we formulate and solve an optimization problem aimed at maximizing the transmission rate of the secondary transmission, subject to limits on channel estimation error, average symbol error rate, power consumption, and energy storage capabilities of the backscatter sensor. The validity of our analysis and the performance of the secondary system based on the proposed designs are corroborated through the Monte Carlo simulations.
AbstractList Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a primary signal, without requiring any type of power-consuming active components or other signal conditioning units. In such a network, the performance of the backscatter system can be severely degraded by channel estimation errors and co-channel direct-link interference (DLI) from the primary system. To overcome these shortcomings, we consider a cloud radio access network (C-RAN) architecture, where both the primary and secondary edge nodes are connected to a cloud processor via high-speed links. In this centralized architecture, secondary edge nodes provide network access to ambient backscatter passive and semi-passive sensors with communication capabilities, and the problem of acquiring channel state information and suppressing the DLI is managed by the cloud processor. In particular, we assess the performance of the secondary backscatter sensor transmission in a realistic system setup, which takes into account training-based channel estimation, practical modulation constraints, and imperfect DLI suppression. In addition, we formulate and solve an optimization problem aimed at maximizing the transmission rate of the secondary transmission, subject to limits on channel estimation error, average symbol error rate, power consumption, and energy storage capabilities of the backscatter sensor. The validity of our analysis and the performance of the secondary system based on the proposed designs are corroborated through the Monte Carlo simulations.
Author Gelli, Giacinto
Darsena, Donatella
Verde, Francesco
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Snippet Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a...
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SubjectTerms Ambient backscatter
Backscatter
Backscattering
Channel estimation
Cloud computing
cloud radio access network
cognitive radio
Computer architecture
data rate maximization
energy harvesting
Energy storage
interference suppression
Microprocessors
Modulation
Nodes
Optimization
passive and semi-passive sensors
performance analysis
Power consumption
Power management
Sensors
State (computer science)
Wireless communication
Wireless communications
Wireless networks
Wireless sensor networks
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Title Cloud-Aided Cognitive Ambient Backscatter Wireless Sensor Networks
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