Summary of Offline Reinforcement Learning and Sequence Modeling For Downlink Link Adaptation, by Samuele Peri et al.
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation
by Samuele Peri, Alessio Russo, Gabor Fodor, Pablo Soldati
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Link adaptation (LA) is crucial in modern wireless communication systems to dynamically adjust transmission rates according to time- and frequency-varying radio link conditions. However, factors like user mobility, fast fading, imperfect channel quality information, and aging of measurements make LA modeling challenging. Recent research introduced online reinforcement learning (RL) approaches as an alternative to rule-based algorithms. Yet, RL-based methods face deployment challenges due to potential degradation in real-time performance during training in live networks. To address this, offline RL is proposed as a candidate to learn LA policies with minimal network operation effects. We propose three LA designs based on batch-constrained deep Q-learning, conservative Q-learning, and decision transformer. Our results show that offline RL algorithms can match the performance of state-of-the-art online RL methods when data is collected with a proper behavioral policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wireless communication systems need to adjust how much information they send based on changing radio conditions. This is called link adaptation (LA). But it’s hard to make LA work because there are many things that can affect the signal, like people moving and the signal getting weaker or stronger. Some researchers use a type of machine learning called reinforcement learning (RL) to help with LA. However, RL can be tricky to use in real-world networks because it needs training data, which can slow down the network. This paper suggests using a different kind of RL that doesn’t need as much training data, so it won’t slow down the network. We came up with three ideas for how this could work and tested them. Our results show that these new methods can be just as good as older ones. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning * Transformer
