# Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

arXiv: https://arxiv.org/abs/1908.02047 · HTML: https://mlsanity.com/paper/1908.02047/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles](https://mlsanity.com/paper/2101.00191/) (2021)
- [Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks](https://mlsanity.com/paper/2510.24546/) (2025)
- [Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing](https://mlsanity.com/paper/2312.10418/) (2023)
- [Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach](https://mlsanity.com/paper/2402.12260/) (2024)
- [Collaborative Optimization of the Age of Information under Partial Observability](https://mlsanity.com/paper/2312.12977/) (2023)
- [Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning](https://mlsanity.com/paper/2209.08947/) (2022)
- [Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing](https://mlsanity.com/paper/2409.16832/) (2024)
- [Transformer Actor-Critic for Efficient Freshness-Aware Resource Allocation](https://mlsanity.com/paper/2602.22774/) (2026)

## Abstract (arXiv)

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.