# Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2402.12260 · HTML: https://mlsanity.com/paper/2402.12260/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Meta-Learning for Multi-objective Reinforcement Learning](https://mlsanity.com/paper/1811.03376/) (2018)
- [Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems](https://mlsanity.com/paper/2105.02741/) (2021)
- [Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective](https://mlsanity.com/paper/1908.02047/) (2019)
- [Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks](https://mlsanity.com/paper/1912.07981/) (2019)
- [Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks](https://mlsanity.com/paper/2105.03125/) (2021)
- [Age of Information for Multicast Transmission with Fixed and Random Deadlines in IoT Systems](https://mlsanity.com/paper/2003.08304/) (2020)
- [Multi-Objective Energy Efficient Resource Allocation and User Association for In-band Full Duplex Small-Cells](https://mlsanity.com/paper/2007.01117/) (2020)
- [Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems](https://mlsanity.com/paper/1911.08165/) (2019)
- [Energy-Efficient Hybrid Precoding Design for Integrated Multicast-Unicast Millimeter Wave Communications with SWIPT](https://mlsanity.com/paper/2002.04171/) (2020)
- [Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning](https://mlsanity.com/paper/2004.04640/) (2020)
- [Energy Efficiency Maximization in the Uplink Delta-OMA Networks](https://mlsanity.com/paper/2102.13359/) (2021)
- [Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks](https://mlsanity.com/paper/2209.13006/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks](https://mlsanity.com/paper/2405.11331/) (2024)
- [From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks](https://mlsanity.com/paper/2605.25531/) (2026)
- [Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation](https://mlsanity.com/paper/2606.22146/) (2026)

## Abstract (arXiv)

This paper considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider non-orthogonal multi-modal information dissemination, which is based on superposed message transmission from RSU and successive interference cancellation (SIC) at vehicles. The formulated problem is a multi-objective mixed-integer nonlinear programming problem; thus, a Pareto-optimal front is very challenging to obtain. First, we leverage the weighted-sum approach to decompose the multi-objective problem into a set of multiple single-objective sub-problems corresponding to each predefined objective preference weight. Then, we develop a hybrid deep Q-network (DQN)-deep deterministic policy gradient (DDPG) model to solve each optimization sub-problem respective to predefined objective-preference weight. The DQN optimizes the decoding order, while the DDPG solves the continuous power allocation. The model needs to be retrained for each sub-problem. We then present a two-stage meta-multi-objective reinforcement learning solution to estimate the Pareto front with a few fine-tuning update steps without retraining the model for each sub-problem. Simulation results illustrate the efficacy of the proposed solutions compared to the existing benchmarks and that the meta-multi-objective reinforcement learning model estimates a high-quality Pareto frontier with reduced training time.