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

• URL: http://arxiv.org/abs/2503.17756v1
• Date: Sat, 22 Mar 2025 12:36:23 GMT
• Title: Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment
• Authors: Abdullah Al-Khatib, Abdullah Ahmed, Klaus Moessner, Holger Timinger,
• Abstract summary: bandwidth reservation requests often face challenges such as price fluctuations and fairness issues.<n>This research aims to minimize resource costs by finding the best price among multiple MNOs.
• Score: 1.1749564892273827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches [4.364595470673757]
  Portfolio Beam Search (PBS) is a simple-yet-effective alternative to Beam Search (BS)<n>We develop an uncertainty-aware diversification mechanism, which we integrate into a sequential decoding algorithm at inference time.<n>We empirically demonstrate the effectiveness of PBS on the D4RL benchmark, where it achieves higher returns and significantly reduces outcome variability.
  arXiv  Detail & Related papers  (2025-02-13T15:51:46Z)
• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• Revenue Management without Demand Forecasting: A Data-Driven Approach for Bid Price Generation [25.53238782264327]
  We present a data-driven approach to revenue management which eliminates the need for demand forecasting and optimization techniques. We utilize a neural network algorithm to project bid price estimations into the future. Our results show that our data-driven methodology stays near a theoretical optimum (1% revenue gap) for a wide-range of settings.
  arXiv  Detail & Related papers  (2023-04-14T21:10:13Z)
• Job Scheduling in Datacenters using Constraint Controlled RL [0.0]
  We apply Proportional-Integral-Derivative (PID) Lagrangian methods in Deep Reinforcement Learning to job scheduling problem in the green datacenter environment. Experiments demonstrate improved performance compared to scheduling policies without the PID Lagrangian methods.
  arXiv  Detail & Related papers  (2022-11-10T04:43:14Z)
• FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations [52.85536740465277]
  FIRE is a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. We show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
  arXiv  Detail & Related papers  (2022-09-28T19:49:39Z)
• Offline Reinforcement Learning for Road Traffic Control [12.251816544079306]
  We build a model-based learning framework, A-DAC, which infers a Markov Decision Process (MDP) from dataset with pessimistic costs built in to deal with data uncertainties. A-DAC is evaluated on a complex signalized roundabout using multiple datasets varying in size and in batch collection policy.
  arXiv  Detail & Related papers  (2022-01-07T09:55:21Z)
• An Experimental Design Perspective on Model-Based Reinforcement Learning [73.37942845983417]
  In practical applications of RL, it is expensive to observe state transitions from the environment. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process.
  arXiv  Detail & Related papers  (2021-12-09T23:13:57Z)
• Dynamic Network-Assisted D2D-Aided Coded Distributed Learning [59.29409589861241]
  We propose a novel device-to-device (D2D)-aided coded federated learning method (D2D-CFL) for load balancing across devices. We derive an optimal compression rate for achieving minimum processing time and establish its connection with the convergence time. Our proposed method is beneficial for real-time collaborative applications, where the users continuously generate training data.
  arXiv  Detail & Related papers  (2021-11-26T18:44:59Z)
• Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network [0.0]
  Integration of renewable energy sources and emerging loads like electric vehicles to smart grids brings more uncertainty to the distribution system management. Demand Side Management (DSM) is one of the approaches to reduce the uncertainty. Some applications like Nonintrusive Load Monitoring (NILM) can support DSM, however they require accurate forecasting on high resolution data. This is challenging when it comes to single loads like one residential household due to its high volatility.
  arXiv  Detail & Related papers  (2021-09-26T05:20:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.