Summary of Joint Optimization Of Age Of Information and Energy Consumption in Nr-v2x System Based on Deep Reinforcement Learning, by Shulin Song et al.
Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning
by Shulin Song, Zheng Zhang, Qiong Wu, Qiang Fan, Pingyi Fan
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers focus on improving wireless communication technology for autonomous vehicles. The development of 5G New Radio (NR) technology enables reliable and low-latency vehicle communication, crucial for applications like autonomous driving. To address challenges in resource collisions and age of information (AOI), the authors employ interference cancellation methods combining NR-V2X with Non-Orthogonal multiple access (NOMA). They formulate an optimization problem to minimize energy consumption and AoI, using Deep Reinforcement Learning (DRL) to compute optimal transmission power and interval. The proposed algorithm is tested through extensive simulations, demonstrating its performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars possible by improving wireless communication technology. It’s like sending texts between vehicles quickly and accurately. Right now, there are problems with this communication, so the researchers found a way to fix it using something called NR-V2X and NOMA. They used special math to figure out how to send messages in a way that uses less energy and is faster. They tested their idea on computers and it worked well. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
