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Award ID contains: 2009001

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  1. ; ; ; ; (, IEEE transactions on wireless communications)
    Free, publicly-accessible full text available April 1, 2026
  2. ; ; ; (, IEEE Transactions on Wireless Communications)
    Free, publicly-accessible full text available January 1, 2026
  3. ; ; ; ; (, IEEE)
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  4. ; ; ; ; (, IEEE Transactions on Wireless Communications)
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  5. ; ; (, IEEE Transactions on Machine Learning in Communications and Networking)
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  6. ; ; (, IEEE Transactions on Signal Processing)
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  7. ; ; (, IEEE Wireless Communications Letters)
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  8. ; ; (, IEEE)
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  9. ; (, IEEE Transactions on Wireless Communications)
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  10. ; ; (, 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. 
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