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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 ===== | ||
| - | ### | ||
| **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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| * Thrust 2: Accelerating Learning-based DRL to Improve Spectrum Utilization; | * Thrust 2: Accelerating Learning-based DRL to Improve Spectrum Utilization; | ||
| * Thrust 3: Spectrum Sensing and Interference Control for Active and Passive Radio Coexistence. | * Thrust 3: Spectrum Sensing and Interference Control for Active and Passive Radio Coexistence. | ||
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| ===== Publication ===== | ===== Publication ===== | ||
swift.1682023450.txt.gz · Last modified: 2023/04/20 16:44 by lingjialiu
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