Summary of Non-orthogonal Age-optimal Information Dissemination in Vehicular Networks: a Meta Multi-objective Reinforcement Learning Approach, by A. A. Habob et al.
Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach
by A. A. Habob, H. Tabassum, O. Waqar
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A vehicular network is designed to provide timely updates on physical processes to vehicles, while minimizing age-of-information (AoI) and transmit power consumption. The authors develop a non-orthogonal multi-modal information dissemination approach using superposed message transmission from roadside units (RSUs) and successive interference cancellation (SIC) at vehicles. This multi-objective mixed-integer nonlinear programming problem is decomposed into single-objective sub-problems using the weighted-sum approach, which are then solved by a hybrid deep Q-network (DQN)-deep deterministic policy gradient (DDPG) model. The DQN optimizes decoding order and DDPG solves continuous power allocation. A two-stage meta-multi-objective reinforcement learning solution is proposed to estimate the Pareto front with reduced training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are working on a way for cars to get important information from roadside units while using as little energy as possible. They’re doing this by sending multiple messages at once and then having the cars figure out which ones are important. The problem is complicated because it’s trying to balance two things: getting the information quickly and using the right amount of energy. The scientists use a special computer program to solve this problem, and they test it to see how well it works. |
Keywords
* Artificial intelligence * Multi modal * Reinforcement learning
