Lingjia Liu's Webpage

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<fs x-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 ====

During the last two decades, the use of Radio Frequency (RF) spectrum has increased tremendously due to the ever growing demand for wireless connectivity. The existing network technologies that support the current wireless data demand are expected to increase their capacity significantly in the next decade, calling for spectrum and energy efficient communication strategies. There are two popular approaches to efficiently utilize the RF spectrum: One is the cognitive radio networks which allow mobile users to share the spectrum that has been primarily allocated to other services such as television broadcasting, global position system (GPS), radar, weather forecasting, etc., provided that the mobile users impose limited interference to existing services. Another approach is to enhance the mobile broadband networks via expanded bandwidth, massive Multiple-Input Multiple-Output (MIMO) systems, and densified heterogeneous networks (HetNets). However, both approaches have limitations and have different impacts on spectrum efficiency and energy efficiency. In addition, current hardware platforms exhibit formidable challenges in supporting high computational complexity and low power consumption. This project introduces a novel network architecture and its application-specific hardware optimization using neuromorphic computing devices. The new wireless network architecture allows mobile users to perform spatio-temporal spectrum sensing and actively search for dynamic spectrum access (DSA) opportunities to enable short-range and local communications. Meanwhile, neuromorphic computing devices that mimic bio-neurological processes will be designed to tackle the high computational complexity of the new dynamic spectrum access approach with extremely low power consumption. In this way, we will be able to enable our nation's next-generation wireless communications and networking that are intelligent, spectrum-efficient, and energy-efficient in a dynamic spectrum environment. The developed concepts and technologies will also help achieve National Broadband Plan which targets at significant improvements in the efficiency of RF spectrum utilization. The project has an extensive education and outreach plan which includes designing new course components on energy-efficient communications, analog neuron circuits, and computational intelligence for wireless networks, joint training of graduate and undergraduate researchers between the two collaborative institutions, and outreach to telecommunication industry and underrepresented students through seminars and diversity programs.

Short-range and local communications are extremely beneficial for spectrum and energy efficiency. The research objective of the project is to 1) design DSA-enabled HetNets to enable short-range/local spectrum access to improve the spectrum and energy efficiency, and 2) leverage neuromorphic computing architecture to efficiently solve the associated resource allocation problems with extremely high energy efficiency. To achieve the goal, the project is organized into four interconnected research thrusts. Thrust 1 focuses on spatio-temporal spectrum sensing with MIMO transceivers. Thrust 2 investigates cooperative communications and resource allocation for DSA-enabled HetNets. Thrust 3 studies neuromorphic computing based hardware design for DSA-enabled HetNets. Thrust 4 develops and evaluates the hardware-software test-bed. The proposed paradigm shift from centralized base-station-controlled approach to the decentralized approach will revolutionize the future wireless network design, where the individual users will play stronger role in spectrum access and drastically change the network topology by utilizing neuromorphic computing devices. The hardware-software co-design methodologies developed in this project can be readily applied to other related fields: computer communication networks, cyber security, and energy-harvesting communications, etc.
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==== Publication ====

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  * H. Song, L. Liu, S. Pudlewski, and E. Bentley, "Random Network Coding Enabled Routing in Swarm Unmanned Aerial Vehicle Networks", accepted to 2019 //IEEE Global Commun. Conf.// (GLOBECOM).

  * B. Shang, L. Liu, J. Ma, and P. Fan, "Unmanned Aerial Vehicle (UAV) Meets Vehicle-to-Everything in Secure Communications", accepted to //IEEE Commun. Mag.//. 

  * A. Akhtar, J. Ma, R. Shafin, J. Bai, L. Li, Z. Li, and L. Liu, "Low Latency Scalable Point Cloud Communication in VANETs using V2I Communication", accepted to 2019 //IEEE Intl Conf. on Commun.// (ICC).

  * H. Song, L. Liu, H. Chang, J. Ashdown, and Y. Yi, "Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum," accepted to 2019 //IEEE Conf. on Computer Commun. Workshops// (INFOCOM WKSHPS).

  * F. Mahmood, E. Perrins and L. Liu, "Energy-Efficient Wireless Communications: From Energy Modeling to Performance Evaluation," accepted to //IEEE Trans. Veh. Technol.//, 2019.

  * C. Sahin, L. Liu, E. Perrins, and L. Ma, "Delay-Sensitive Communications over IR-HARQ: Modulation, Coding Latency, and Reliability", Special Issue on Ultra-Reliable Low-Latency Communications in Wireless Networks, //IEEE J. Sel. Area Commun.//, vol. 37, no. 4, pp. 749 - 764, April 2019.

  * First release of our code on Brain-Inspired Computing Meets MIMO-OFDM can be found [[https://github.com/JohnJohnZhou/ESN_MIMO_v1|here]].\\

  * H. Jiang, H. He, L. Liu and Y. Yi, "Q-Learning for Non-Cooperative Channel Access Game of Cognitive Radio Networks," 2018 //Intl Joint Conf. on Neural Netw.// (IJCNN), Rio de Janeiro, 2018, pp. 1-7.

  * R. Atat, L. Liu, J. Wu, G. Li, C. Ye and Y. Yang, "Big Data Meet Cyber-Physical Systems: A Panoramic Survey," //IEEE Access//, vol. 6, pp. 73603-73636, 2018.

  * R. Atat, J. Ma, H. Chen, U. Lee, J. Ashdown and L. Liu, "Cognitive relay networks with energy and mutual-information accumulation," 2018 //IEEE Conf. on Computer Commun. Workshops// (INFOCOM WKSHPS), Honolulu, HI, April 2018, pp. 640-644.

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Lingjia Liu's Webpage

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