Reinforcement Learning for Efficient and Tuning-Free Link Adaptation

Wireless links adapt the data transmission parameters to the dynamic channel state - this is called link adaptation . Classical link adaptation relies on tuning parameters that are challenging to configure for optimal link performance. Recently, reinforcement learning has been proposed to automate l...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 21; H. 2; S. 768 - 780
Hauptverfasser: Saxena, Vidit, Tullberg, Hugo, Jalden, Joakim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation
adaptive modulation and coding
Algorithms
Channel estimation
Channels
Data transmission
Fading
Fading channels
Interference
Learning
Mathematical models
outer loop link adaptation
Parameters
Reinforcement learning
Sampling
Signal to noise ratio
thompson sampling
Throughput
Tuning
Wireless communication
Wireless networks
ISSN:1536-1276, 1558-2248, 1558-2248
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Zusammenfassung:Wireless links adapt the data transmission parameters to the dynamic channel state - this is called link adaptation . Classical link adaptation relies on tuning parameters that are challenging to configure for optimal link performance. Recently, reinforcement learning has been proposed to automate link adaptation, where the transmission parameters are modeled as discrete arms of a multi-armed bandit. In this context, we propose a latent learning model for link adaptation that exploits the correlation between data transmission parameters. Further, motivated by the recent success of Thompson sampling for multi-armed bandit problems, we propose a latent Thompson sampling (LTS) algorithm that quickly learns the optimal parameters for a given channel state. We extend LTS to fading wireless channels through a tuning-free mechanism that automatically tracks the channel dynamics. In numerical evaluations with fading wireless channels, LTS improves the link throughout by up to 100% compared to the state-of-the-art link adaptation algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1536-1276
1558-2248
1558-2248
DOI:10.1109/TWC.2021.3098972