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This research aims to minimize long-term resource consumption in network slicing through adaptive reconfiguration using Deep Reinforcement Learning.

Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning With Large Action Space

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This research aims to minimize long-term resource consumption in network slicing through adaptive reconfiguration using Deep Reinforcement Learning.
Investigated network slice reconfiguration within a core network to enhance resource efficiency.
Proposed an intelligent network slice reconfiguration algorithm (INSRA) utilizing Branching Dueling Q-network.
Conducted extensive simulation experiments to evaluate INSRA's performance against benchmark algorithms.
INSRA minimizes long-term resource consumption significantly compared to benchmark algorithms.
Achieved higher resource efficiency, indicating effective performance in dynamic traffic scenarios.

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Randomized trial investigates network slice reconfiguration, suggesting improved resource efficiency using intelligent algorithms.

Wei et al. (2020) studied this question.

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