Summary of On Enhancing Network Throughput Using Reinforcement Learning in Sliced Testbeds, by Daniel Pereira Monteiro et al.
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds
by Daniel Pereira Monteiro, Lucas Nardelli de Freitas Botelho Saar, Larissa Ferreira Rodrigues Moreira, Rodrigo Moreira
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper introduces the Enhanced Mobile Broadband (eMBB)-Agent, a novel approach that employs Reinforcement Learning (RL) to enhance network slicing throughput for Service-Level Agreements (SLAs). The eMBB-Agent uses Deep Q-Networks (DQN) to analyze transmission variables and adjust reception windows. This is achieved by proposing actions within a discrete space to optimize network slicing performance. The paper presents experimental results examining the impact of factors such as channel error rate, DQN model layers, and learning rate on model convergence and achieved throughput. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to make mobile networks work better for people who need fast and reliable connections. They created something called the Enhanced Mobile Broadband (eMBB)-Agent that uses artificial intelligence to help network slices meet their performance goals. The eMBB-Agent looks at how data is being transmitted and makes decisions to improve it. This paper shows that this new approach can work well in different situations, like when there are errors on the channel. |
Keywords
» Artificial intelligence » Reinforcement learning
