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Langtian Qin

PhD Student in Computer Science (CS)
University of California, Irvine
qlt315@126.com / langtiq@uci.edu


Short Bio

I am a Computer Science PhD student at UC Irvine (UCI), advised by Prof. Marco Levorato. I obtained my M.S. in Electrical Engineering at University of Science and Technology of China (USTC) in 2024, advised by Prof. Hancheng Lu and Prof. Feng Wu. Prior to this, I received my B.E. in Information Engineering from Xidian University (XDU) in 2021.

My research interests lie in wireless communication and networking systems, with a focus on theoretical analysis, optimization and system implementation of next-generation wireless architectures like Cell-Free & User-Centric MIMO and Wireless Mobile Edge Computing (MEC), etc.

News

Selected Publications

  1. TMC
    Towards Decentralized Task Offloading and Resource Allocation in User-Centric MEC
    Langtian Qin, Hancheng Lu, Yuang Chen, Baolin Chong, Feng Wu
    IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 11807-11823, Dec. 2024
    PDF Code

  2. TCCN
    Energy-Efficient Blockchain-enabled User-Centric Mobile Edge Computing
    Langtian Qin, Hancheng Lu, Yuang Chen, Zhuojia Gu, Dan Zhao, Feng Wu
    IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 4, pp. 1452-1466, Aug. 2024.
    PDF Code

  3. TMC
    Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC
    Langtian Qin, Hancheng Lu, Yao Lu, Chenwu Zhang, Feng Wu
    IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 6455-6469, May 2024.
    PDF Code

  4. ComMag
    When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization
    Langtian Qin, Hancheng Lu, Feng Wu
    IEEE Communications Magazine, vol. 61, no. 1, pp. 114-120, Jan. 2023.
    PDF Code

  5. TWC
    Streaming 360-degree VR Video with Statistical QoS Provisioning in mmWave Networks from Delay and Rate Perspectives
    Yuang Chen, Hancheng Lu, Langtian Qin, Chang Wu, Chang Wen Chen
    IEEE Transactions on Wireless Communications, 2025.
    PDF

  6. TCOM
    On the Distribution of SINR for Cell-Free Massive MIMO Systems
    Baolin Chong, Fengqian Guo, Hancheng Lu, Langtian Qin
    IEEE Transactions on Communications, vol. 73, no. 2, pp. 832-845, Feb. 2025.
    PDF

  7. TWC
    Performance Optimization on Cell-Free Massive MIMO-Aided URLLC Systems With User Grouping
    Baolin Chong, Hancheng Lu, Langtian Qin, Zhenyu Xue, Fengqian Guo
    IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 13977-13992, Oct. 2024.
    PDF

  8. TCOM
    Cross-Layer Optimization for Statistical QoS Provision in C-RAN with Finite-Length Coding
    Chang Wu, Hancheng Lu, Yuang Chen, Langtian Qin
    IEEE Transactions on Communications, vol. 72, no. 6, pp. 3393-3407, Jun. 2024.
    PDF

  9. TWC
    Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective
    Yuang Chen, Hancheng Lu, Langtian Qin, Chenwu Zhang, Chang Wen Chen
    IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 8044-8058, Jul. 2024.
    PDF

  10. ComMag
    When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective
    Yuang Chen, Hancheng Lu, Langtian Qin, Yansha Deng, Arumugam Nallanathan
    IEEE Communications Magazine, vol. 62, no. 6, pp. 90-96, Jun. 2024.
    PDF

Projects

  1. UCMEC
    Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing (@USTC)
    To break through the transmission and computing capability bottleneck of cellular-based mobile edge computing (MEC), we propose a novel MEC framework called User-Centric MEC (UCMEC), which can provide users with efficient, reliable, low-cost user-centric wireless transmission and edge computing services. To further exploit the benefits of UCMEC, we propose various algorihms to optimize the task offloading and resource allocation strategies in UCMEC under different scenarios.

  2. mmWave
    Bridging the Sim-to-real Gap in AI-Driven Networks Using Hybrid Simulation (@UCSD)
    To overcome the simulation-to-reality gap, we use deep reinforcement learning-based models to configure the network simulator, letting the simulator accurately reflect the performance trace of the mmWave network testbed. Based on the enhanced simulator, we aim to explore hybrid simulation to enable efficient network control like interference management, base station association, routing, etc.

  3. URLLC
    Statistical QoS Provisioning Analysis and Optimization in xURLLC (@USTC)
    We investigate the fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and lowlatency communication (xURLLC) from the perspective of stochastic network calculus (SNC). By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of some critical performance metrics in xURLLC networks. Based on the proposed theoretical framework, we design several low-complexity algorithms to optimize the xURLLC network performance metrics like energy efficiency, delay violation probability, etc.

Awards

Services

Teaching Assistant

Conference Reviewer

Journal Reviewer