NSF PAR Search | NSF Public Access Repository

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection

https://doi.org/10.1109/TSP.2023.3310895

Feres, Carlos; Levy, Bernard C.; Ding, Zhi (January 2024, IEEE Transactions on Signal Processing)

Full Text Available
Bayesian Decision Making Via Over-the-Air Soft Information Aggregation

https://doi.org/10.1109/ICC45041.2023.10278705

Feres, Carlos; Levy, Bernard C.; Ding, Zhi (May 2023, IEEE)

Full Text Available
Over-the-Air Collaborative Learning in Joint Decision Making

https://doi.org/10.1109/GLOBECOM48099.2022.10001286

Feres, Carlos; Levy, Bernard C.; Ding, Zhi (December 2022, 2022 IEEE Global Telecommunications Conference)

We propose an over-the-air learning framework for collaborative decision making in wireless sensor networks. The low complexity framework leverages low-latency sensor transmission for a decision server to coordinate measurement sensors for hypothesis testing through over-the-air aggregation of sensor data over a multiple-access channel. We formulate several collaborative over-the-air hypothesis testing problems under different practical protocols for collaborative learning and decision making. We develop hypothesis tests for these network protocols and deployment scenarios including channel fading. We provide performance benchmark for both basic likelihood ratio test and generalized likelihood ratio test under different deployment conditions. Our results clearly demonstrate gain provided by increasing number of collaborative sensors.
more » « less
Full Text Available
Over-the-Air Collaborative Learning in Joint Decision Making

Feres, Carlos; Levy, Bernard C.; Ding, Zhi (January 2022, IEEE Global Communications Conference)

We propose an over-the-air learning framework for collaborative decision making in wireless sensor networks. The low complexity framework leverages low-latency sensor transmission for a decision server to coordinate measurement sensors for hypothesis testing through over-the-air aggregation of sensor data over a multiple-access channel. We formulate several collaborative over-the-air hypothesis testing problems under different practical protocols for collaborative learning and decision making. We develop hypothesis tests for these network protocols and deployment scenarios including channel fading. We provide performance benchmark for both basic likelihood ratio test and generalized likelihood ratio test under different deployment conditions. Our results clearly demonstrate gain provided by increasing number of collaborative sensors.
more » « less
Full Text Available

Search for: All records