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* Co-Principal Investigator in the Leading Institution: [[https://www.yangyi.ece.vt.edu/index.html|Yang Yi]] (ECE at VT) | * Co-Principal Investigator in the Leading Institution: [[https://www.yangyi.ece.vt.edu/index.html|Yang Yi]] (ECE at VT) | ||
* Principal Investigator in the Collaborative Institution: [[https://people-ece.vse.gmu.edu/~ztian1/|Zhi Tian]] (ECE at George Mason University) | * Principal Investigator in the Collaborative Institution: [[https://people-ece.vse.gmu.edu/~ztian1/|Zhi Tian]] (ECE at George Mason University) | ||
+ | * Graduate Student in Virginia Tech: Nima Mohammadi and Honghao Zheng | ||
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===== Research Breakdown ===== | ===== Research Breakdown ===== | ||
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**IDEA** covers the following research components: | **IDEA** covers the following research components: | ||
* Analog/mixed-signal neuromorphic computing hardware: SNN-aided device designs including multiplexing neural encoding, computing-in-memory, and efficient training for resource constrained secondary radios to enable on-board intelligence at ultra-low power consumption and compact design areas; | * Analog/mixed-signal neuromorphic computing hardware: SNN-aided device designs including multiplexing neural encoding, computing-in-memory, and efficient training for resource constrained secondary radios to enable on-board intelligence at ultra-low power consumption and compact design areas; | ||
* Improving spectrum utilization and coexistence through learning: tailored integration of model-free DRL and domain knowledge of spectrum sharing network with improved sample efficiency to increase spectrum utilization in realistic scenarios, along with judiciously designed DSA actions for coexistence; | * Improving spectrum utilization and coexistence through learning: tailored integration of model-free DRL and domain knowledge of spectrum sharing network with improved sample efficiency to increase spectrum utilization in realistic scenarios, along with judiciously designed DSA actions for coexistence; | ||
* Spectrum sensing through concise statistical modeling and learning: efficient spectrum sensing techniques that exploit the inherent structural information of statistics to accurately extract discriminative higher-order statistical features of various signal sources within a short sensing time; | * Spectrum sensing through concise statistical modeling and learning: efficient spectrum sensing techniques that exploit the inherent structural information of statistics to accurately extract discriminative higher-order statistical features of various signal sources within a short sensing time; | ||
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The following thrusts are organized to address the above mentioned research components: | The following thrusts are organized to address the above mentioned research components: |