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- W4385819699 abstract "Real-time traffic has stringent requirements in terms of latency, and deadline guarantees on packet delivery play a vital role in real-time IoT applications. Deadline-aware wireless scheduling of real-time traffic has been a long-standing open problem, despite significant efforts using analytical methods. Departing from the conventional approaches, this work studies deadline-aware traffic scheduling by taking an offline reinforcement learning (RL) approach to train scheduling algorithms, ready to be used for online scheduling. To address the challenges therein, we propose a Network-Aided Offline RL (NA-ORL) framework for deadline-aware scheduling, by making use of the fact that the network dynamics follows a well-defined physics model. Specifically, in NA-ORL the initialization of the scheduling policy is obtained through behavior cloning with a good model-based scheduling algorithm, and the network-aided actor-critic (A-C) method is utilized to train a better scheduling policy with carefully designed states and reward function, thanks to its nature of policy improvement. Building on NA-ORL, we further devise a Network-Aided Offline Meta-RL (NA-MRL) algorithm to deal with the non-stationary network dynamics. Extensive experimental results demonstrate that the proposed NA-ORL and NA-MRL algorithms can achieve better performance over Adaptive Mixing over Non-Dominated links (AMIX-ND) and Largest-Deficit-First (LDF), in various scenarios for the deadline-aware wireless scheduling." @default.
- W4385819699 created "2023-08-15" @default.
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- W4385819699 date "2023-01-01" @default.
- W4385819699 modified "2023-09-26" @default.
- W4385819699 title "Scheduling Real-time Wireless Traffic: A Network-aided Offline Reinforcement Learning Approach" @default.
- W4385819699 doi "https://doi.org/10.1109/jiot.2023.3304969" @default.
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