Deep Reinforcement Learning Based Policy for Power Efficient Dynamic Subcarrier Assignment in OFDMA-PONs

机译：基于深度强化学习的OFDMA-PON中功率有效动态子载波分配策略

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摘要

The paper proposes a deep reinforcement learning (DRL) based policy for power efficient dynamic subcarrier assignment in OFDMA-PONs. The simulation results show it can reaches the near-optimal traffic delay with a significant power saving.

机译：本文提出了一种基于深度强化学习（DRL）的策略，用于OFDMA-PON中的功率高效动态子载波分配。仿真结果表明，它可以达到接近最佳的通信延迟，并且可以节省大量功率。

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《Asia Communications and Photonics Conference》|2019年|1-3|共3页
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作者
Bin Chen; Min Zhu; Jiahua Gu; Tianyu Shen; Xueqi Ren; Chenglin Shi;
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energy conservation; frequency division multiple access; learning (artificial intelligence); OFDM modulation; passive optical networks; synchronisation; telecommunication power management; telecommunication traffic;

机译：节能;频分多址;学习（人工智能）; OFDM调制;无源光网络;同步;电信功率管理;电信流量;

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Deep Reinforcement Learning Based Policy for Power Efficient Dynamic Subcarrier Assignment in OFDMA-PONs

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