Summary of Deep-reinforcement-learning-based Aoi-aware Resource Allocation For Ris-aided Iov Networks, by Kangwei Qi et al.
Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks
by Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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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 A proposed Reconfigurable Intelligent Surface (RIS)-assisted internet of vehicles (IoV) network enhances link quality in wireless communication environments. The paper introduces an age of information (AoI) model and payload transmission probability model to improve V2I and V2V link timeliness and stability. A Markov decision process (MDP) problem is constructed, where a base station serves as an agent using the soft actor-critic (SAC) algorithm to allocate resources and control phase-shift for vehicles. The proposed AoI-aware joint vehicular resource allocation and RIS phase-shift control scheme outperforms other algorithms in terms of convergence speed, cumulative reward, AoI performance, and payload transmission probability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new technology called Reconfigurable Intelligent Surfaces (RIS) that helps with wireless communication. They’re using this tech to improve the way vehicles talk to each other and to infrastructure. To do this, they’re creating a model that shows how information gets older over time, which helps them decide when to send messages. They’re also using a special kind of computer program to help the base station make good decisions about where to send resources. The results show that their method works better than other methods. |
Keywords
» Artificial intelligence » Probability
