Liwen Jing

Shenzhen Technology University, Hong Kong University of Science and Technology, Peng Cheng Laboratory

Papers

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About

Liwen Jing is a researcher whose work spans wireless communications, signal processing, and intelligent sensing systems, with a particular focus on bridging foundational algorithmic theory and real-world applications. Jing's most-cited contribution, "GBRAMP" (2021, 13 citations), advances compressed sensing by introducing a generalized backtracking regularized adaptive matching pursuit algorithm, offering improved efficiency in signal reconstruction — a building block for modern communication and imaging systems. In wireless communications, Jing has explored adaptive OFDM techniques tailored for challenging waveguide environments such as tunnels, leveraging subcarrier delay spread to enhance reliability for mobile robots and vehicles operating in constrained channels (2018, 7 citations). Expanding into robotics and IoT, Jing contributed cloud-robot-based indoor and outdoor positioning systems, work that gained practical urgency during the COVID-19 pandemic, supporting autonomous navigation in hospitals and campuses (2021, 3 citations). Most recently, Jing has pushed into generative AI-driven sensing, pioneering WiFi-based high-resolution indoor imaging — a novel reframing of passive WiFi sensing as an image generation problem with profound implications for robotics and smart environments. Across these domains, Jing demonstrates a consistent drive toward intelligent, adaptive systems for real-world deployment.