Cloud-Aided Cognitive Ambient Backscatter Wireless Sensor Networks
Open accessAbstract
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.
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Ambient backscattering, in which small passive devices transmit data by reflecting electromagnetic waves from existing RF transmitters, is a promising solution.
“In this sense, a promising solution can be ambient backscattering [157], where small passive devices are able to transmit data by reflecting electromagnetic waves transmitted by existing RF transmitters.”
From Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review (2023) · cites this paper as [157]
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- [72]72. Since 2005, she has been an Assistant Professor with the Department of Engineering, Parthenope University, Italy. Her research activities lie in the areas of statistical signal processing, digital communications, and communication systems. Her current research inter- ests include equalization, channel identification, narrowband-interference suppression for multicarrier systems, space-time processing for coopera- tive communications systems and cognitive communications systems, and software-defined networks. She has been serving as an Associate Editor for the IEEE COMMUNICATIONS LETTERS, since 2016, and IEEE ACCESS, since 2018.
- [74]74. FRANCESCO VERDE (M'10-SM'14) was born in Santa Maria Capua Vetere, Italy, in 1974. He received the Dr.Eng. degree (summa cum laude) in electronic engineering from the Second University of Naples, Italy, in 1998, and the Ph.D. degree in information engineering from the Uni- versity of Napoli Federico II, in 2002. Since 2002, he has been with the University of Napoli Federico II. He first served as an Assistant Professor of signal theory and mobile communications and since 2011, he has been serving as an Asso- ciate Professor of telecommunications with the Department of Electrical Engineering and Information Technology. His research interests include orthogonal/non-orthogonal multiple-access techniques, space-time process- ing for cooperative/cognitive communications, wireless systems optimiza- tion, and software-defined networks. He is also an elected member of the IoT Special Internet Group (SIG) of the IEEE Signal Processing Society for the term 2018-2020. He has been involved in several technical program committees of the major IEEE conferences in signal processing and wireless communications. He was an Associate Editor of the IEEE TRANSACTIONS ON SIGNAL PROCESSING, from 2010 to 2014, and the IEEE SIGNAL PROCESSING LETTERS, from 2014 to 2018, and the Guest Editor of the EURASIP Journal on Advances in Signal Processing, in 2010, and Sensors (MDPI), in 2018.
- [75]75. He has been serving as an Associate Editor for the IEEE TRANSACTIONS ON COMMUNICATIONS, since 2017, and a Senior Area Editor for the IEEE SIGNAL PROCESSING LETTERS, since 2018.
Cited by
Sorted by citations
Darsena, D., Gelli, G., Iudice, I., & Verde, F. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3223297 · Cited by 71 · see citing works
Self-citation
- Authors
- Darsena, D., Gelli, G., & Verde, F.
- Affiliations
- Parthenope University of Naples (IT)
- University of Naples Federico II (IT)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 57399-57414
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2943400766
- Cited by
- 36
- References
- 63
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 10 · 2020: 12 · 2019: 3
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutial research information system (Parthenope University of Naples) (submittedVersion)
- INFM-OAR (INFN Catania) (submittedVersion, other-oa)
- Funders
- “Information and Electrical Technologies for e-Health and Industry 4.0”
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Harvesting in Wireless Networks
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Keywords
- cloud radio access network, wireless sensor networks, channel estimation error, training-based channel estimation, symbol error rate, energy storage constraints, power consumption
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