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- W4328011877 abstract "Mobile devices generate numerous non-divisible and delay-sensitive tasks in 5G system, which bring a huge amount of network traffic. As Mobile edge computing can effectively alleviate computational pressure of terminal devices by offloading some tasks to edge servers, it is of great value to investigate an appropriate computation offloading scheme to reduce the sum processing delay and the task dropped rate in the 5G system. In order to deal with the problem, by adopting long short-term memory (LSTM), dueling double DQN (D3QN) and DQNReg, the problem is formulated into a long-term optimization problem in this paper. Based on non-preemptive priority scheduling and deep reinforcement learning (DQN), an innovatively distributed offloading algorithm named NPPS-D3QNReg is proposed, which can be used for adjusting the execution order of the tasks according to their deadlines. Finally, simulation results demonstrate that our proposed algorithm can effectively reduce the sum delay and task dropped rate." @default.
- W4328011877 created "2023-03-22" @default.
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- W4328011877 date "2022-12-09" @default.
- W4328011877 modified "2023-10-17" @default.
- W4328011877 title "A Distributed Approach for 5G Computation Offloading based on Non-Preemptive Priority Scheduling and Deep Reinforcement Learning" @default.
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- W4328011877 doi "https://doi.org/10.1109/iccc56324.2022.10065765" @default.
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