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Deep Reinforcement Learning Based Adaptive Modulation With Outdated CSI
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DOI：10.1109/LCOMM.2021.3098419.png)
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摘要
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The problem of adaptive modulation with outdated channel state information (CSI) is considered. Best existing approach to tackle this problem relies on using a (non-)linear auto-regressive moving average (ARMA) model to predict current CSI from outdated values. This approach is valid only if the wireless channel variations over time behave in a linear or smooth enough nonlinear fashion, which is not necessarily the case. We propose a deep reinforcement learning based adaptive modulation (DRL-AM) approach that can handle this limitation. While DRL-AM is more complex than (non-)linear AR(MA), it performs significantly better as corroborated via numerical results on real channel measurements. Furthermore, compared to capacity-achieving codes, complexity is moved from receiver to transmitter making this approach suitable for receiving nodes with limited resources such as internet of things (IoT) devices.
Keyword:
Modulation
Throughput
Transmitters
Receivers
Reinforcement learning
Wireless communication
Channel estimation
Deep reinforcement learning
adaptive modulation
auto-regressive model
Wiener filter
outdated CSI
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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