# Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment

arXiv: https://arxiv.org/abs/2503.17756 · HTML: https://mlsanity.com/paper/2503.17756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey](https://mlsanity.com/paper/1608.03475/) (2016)
- [The Secretary Problem with Predictions](https://mlsanity.com/paper/2306.08340/) (2023)
- [Heterogeneous Task Offloading and Resource Allocations via Deep Recurrent Reinforcement Learning in Partial Observable Multi-Fog Networks](https://mlsanity.com/paper/2007.10581/) (2020)
- [Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles](https://mlsanity.com/paper/2009.12509/) (2020)
- [Dynamic Pricing for Revenue Maximization in Mobile Social Data Market with Network Effects](https://mlsanity.com/paper/1808.04039/) (2018)
- [Futures-Based Resource Trading and Fair Pricing in Real-Time IoT Networks](https://mlsanity.com/paper/1909.13337/) (2019)
- [Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach](https://mlsanity.com/paper/2101.00775/) (2021)
- [Let' s Trade in The Future! A Futures-Enabled Fast Resource Trading Mechanism in Edge Computing-Assisted UAV Networks](https://mlsanity.com/paper/2101.00778/) (2021)
- [Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks](https://mlsanity.com/paper/2306.14156/) (2023)

## Built on by (most cited first, 1 of 1)

- [Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks](https://mlsanity.com/paper/2606.05208/) (2026)

## Abstract (arXiv)

Onsite bandwidth reservation requests often face challenges such as price fluctuations and fairness issues due to unpredictable bandwidth availability and stringent latency requirements. Requesting bandwidth in advance can mitigate the impact of these fluctuations and ensure timely access to critical resources. In a multi-Mobile Network Operator (MNO) environment, vehicles need to select cost-effective and reliable resources for their safety-critical applications. This research aims to minimize resource costs by finding the best price among multiple MNOs. It formulates multi-operator scenarios as a Markov Decision Process (MDP), utilizing a Deep Reinforcement Learning (DRL) algorithm, specifically Dueling Deep Q-Learning. For efficient and stable learning, we propose a novel area-wise approach and an adaptive MDP synthetic close to the real environment. The Temporal Fusion Transformer (TFT) is used to handle time-dependent data and model training. Furthermore, the research leverages Amazon spot price data and adopts a multi-phase training approach, involving initial training on synthetic data, followed by real-world data. These phases enable the DRL agent to make informed decisions using insights from historical data and real-time observations. The results show that our model leads to significant cost reductions, up to 40%, compared to scenarios without a policy model in such a complex environment.