Yuefeng Ji

Beijing University of Posts and Telecommunications

Papers

Total Citations

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About

Yuefeng Ji is a leading researcher at the forefront of intelligent networking and optical communication systems, with a focus on enabling next-generation industrial and robotic applications. His work bridges the gap between adaptive routing protocols and deterministic network performance. In a highly cited 2019 study (35 citations), Ji proposed a novel Reward Function Learning scheme for Q-learning-based Geographic routing (RFLQGeo), significantly improving data delivery efficiency in highly dynamic unmanned robotic networks (URNs) by addressing the challenges of node mobility and environmental change. More recently, his 2023 work (25 citations) tackles the critical need for time-sensitive industrial flows, introducing a time-aware deterministic bandwidth allocation scheme for Time-Division Multiplexing Passive Optical Networks (TDM-PON). This innovation supports the rise of cloud-based programmable logic controllers (cloud PLCs) in flexible manufacturing. By combining reinforcement learning with optical access network design, Ji’s contributions are pivotal for the industrial Internet, ensuring both intelligent adaptability and ultra-reliable, low-latency communication for future automated systems.