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<fs large>**NSF/ECCS-2128594: Collaborative Research: SWIFT: Intelligent Dynamic Spectrum Access (IDEA): An Efficient Learning Approach to Enhancing Spectrum Utilization and Coexistence**</fs>
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===== Personnel =====

  * Principal Investigator in the Leading Institution: Lingjia Liu (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)


===== Abstract =====
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With the growing importance of wireless connectivity for social and economic interaction, there have been rising demands for greater spectrum use by both primary and secondary active radios, amidst the critical requirement of quiet spectrum for scientific exploration by receiver-only passive systems. To improve the overall spectrum utilization of the wireless ecosystem, this project develops a holistic Intelligent Dynamic spEctrum Access (IDEA) framework that can substantially enhance the spectrum utilization, energy-efficiency, and coexistence capability of spectrum sharing networks. In IDEA, enabling technical innovations across multiple disciplines are synergistically developed, including neuromorphic design of energy-efficient computing hardware at the device and circuit level, and artificial intelligence for spectrum sensing and dynamic access at the network level. The spectrum and interference management in IDEA conscientiously treats the coexistence constraints imposed by passive services, in support of scientific and societal returns from remote sensing investments. The outcomes of this research are expected to broadly impact next-generation wireless networks and Internet of Things applications with high traffic demands, such as autonomous driving, smart cities and remote sensing.\\
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The goal of this project is to develop an intelligent dynamic spectrum access framework with unprecedented spectrum utilization efficiency and agility to support spectrum coexistence. The developed IDEA network platform supports heterogeneous devices from both primary and secondary active radios as well as passive radios. Key technical innovations are developed across the network to substantially enhance system-level spectrum utilization and active-passive radio coexistence. Specifically, analog/mixed-signal spiking neural network (SNN)-based neuromorphic computing hardware is designed to provide on-board intelligence at ultra-low power for resource-constrained secondary active radios. Model-free deep reinforcement learning is integrated with wireless domain knowledge and the SNN platform to accelerate learning-based spectrum access and coexistence. Advanced spectrum monitoring techniques are developed to quickly detect and characterize various signal emitters in both active and passive services. Finally, software and hardware testbeds are developed for system level evaluation and tradeoff optimization. The IDEA framework not only empowers efficient spectrum access in highly dynamic wireless environments, but also facilitates holistic system design and optimization across devices and circuits, sensing and communications, and networking.
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===== Research Breakdown =====
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**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;
  * 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;

The following thrusts are organized to address the above mentioned research components:
  * Thrust 1: Energy-Efficient Spiking Neural Networks Design and Optimization;
  * Thrust 2: Accelerating Learning-based DRL to Improve Spectrum Utilization;
  * Thrust 3: Spectrum Sensing and Interference Control for Active and Passive Radio Coexistence.


===== Publication =====
Lingjia Liu's Webpage

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