Lingjia Liu's Webpage

• PI in the Leading Institution: Lingjia Liu (ECE at VT)
• PI in the Collaborative Institution: Robert Calderbank (ECE/CS at Duke)
• PI in the Collaborative Institution: Yuejie Chi (ECE at CMU)

The explosive growth of mobile data traffic is in part a response to the proliferation of mobile access services in recent years. However, not all mobile users are able to enjoy stable and reliable broadband connections due to limited network capacities and limited coverage areas. Therefore, in next generation (NextG) mobile broadband networks it is necessary to integrate terrestrial and non-terrestrial networks to democratize wireless access, by providing seamless wireless coverage and supporting heterogeneous service requirements. To meet this goal, this project will develop the fundamental research necessary to integrate and operate terrestrial and non-terrestrial networks, termed Ground and Air Integrated Networks (GAINs). The research project is highly interdisciplinary at the interface of machine learning and wireless networks, providing graduate and undergraduate students with the skills needed to thrive in either community, as well as to bridge them either in academia or in industry. Software and hardware testbeds will provide proof of concept demonstrations for academic, industry and government partners.
The overarching objective of this research program is to develop fundamental enabling communication and computing technologies for resilient and intelligent Ground and Air Integrated Networks (GAINs) based on waveform design, real-time machine learning, resource scheduling, distributed computing and learning. This research program makes the sparse representation of the propagation environment visible to machine learning algorithms by designing signals and controlling networks in the delay-Doppler domain, rather than the time-frequency domain. The research program is streamlined into four interconnected research thrusts: 1) Waveform design to enable machine learning; 2) multi-agent reinforcement learning-enabled resilient scheduling for terrestrial networks; 3) distributed and resilient computing in GAINs; and 4) proof-of-concept development and system evaluation. A new suite of distributed and resilient machine learning algorithms that are communication-efficient and heterogeneity-aware will be tailored to information processing in GAINs at the speed of the next generation (NextG) networks.

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.