The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Noncoherent Detections for Ambient Backscatter System
Abstract
Ambient backscatter, an emerging communication mechanism where battery-free devices communicate with each other via backscattering ambient radio frequency (RF) signals, has achieved much attention recently because of its desirable application prospects in the Internet of Things. In this paper, we formulate a practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown. For the first time, a maximum likelihood detector is derived based on the joint probability density function of received signal vectors. In order to ease availability of prior knowledge of the ambient RF signal and reduce computational complexity of the algorithm, we design a joint-energy detector and derive its corresponding detection threshold. The analytical bit error rate (BER) and BER-based outage probability are also obtained in a closed form, which helps with designing system parameters. An estimation method to obtain detection-required parameters and comparison of computational complexity of the detectors are presented as complementary discussions. Simulation results are provided to corroborate theoretical studies.
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Cites
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Cited by
Ambient Backscatter Communications: A Contemporary Survey
Van Huynh, N., Hoang, D. T., Lu, X., Niyato, D., Wang, P., & Kim, D. I. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2841964 · Cited by 932
Modulation in the Air: Backscatter Communication Over Ambient OFDM Carrier
Yang, G., Liang, Y.-C., Zhang, R., & Pei, Y. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2017.2772261 · Cited by 323
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
Liang, Y.-C., Zhang, Q., Larsson, E. G., & Li, G. Y. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3023139 · Cited by 319
Cloud-Aided Cognitive Ambient Backscatter Wireless Sensor Networks
Darsena, D., Gelli, G., & Verde, F. (2019) · IEEE Access · doi:10.1109/access.2019.2914001 · Cited by 36
Metadata
- Authors
- Qian, J., Gao, F., Wang, G., Jin, S., & Zhu, H.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2017-03
- Volume
- 16
- Issue
- 3
- Pages
- 1412-1422
- ISSN
- 1536-1276 (ISSN-L)
- OpenAlex
- W2560844389
- Cited by
- 283
- References
- 23
- FWCI
- 17.99
- Type
- journal article
- Language
- en
- Open access
- closed
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