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About

Julian Cheng is a prominent researcher whose work sits at the dynamic intersection of wireless communications, indoor positioning, and autonomous robotic systems. His research spans visible light positioning (VLP), reconfigurable intelligent surfaces (RIS), and the Internet of Robotic Things (IoRT), areas where he has made substantial contributions to solving real-world connectivity and localization challenges. Cheng's highly cited survey on indoor visible light positioning systems (52 citations) has established itself as a foundational reference, comprehensively mapping the fundamentals, applications, and challenges of VLP technology for emerging use cases like virtual reality and autonomous navigation. His work on federated deep reinforcement learning for RIS-assisted multi-robot communication (34 citations) addresses critical signal degradation and mobility challenges in dynamic indoor environments, offering elegant solutions through intelligent surface coordination. Extending this vision further, Cheng has pioneered deep reinforcement learning frameworks for joint trajectory and communication optimization in robotic systems, advancing both IoRT architectures and multi-agent coordination in smart factory settings. His body of work reflects a consistent drive to integrate artificial intelligence with next-generation communications infrastructure, making him an influential voice for researchers navigating the rapidly evolving landscape of intelligent wireless systems and autonomous robotics.