A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario

• URL: http://arxiv.org/abs/2202.01949v1
• Date: Fri, 4 Feb 2022 02:59:16 GMT
• Title: A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario
• Authors: Federico Mason, Matteo Drago, Tommaso Zugno, Marco Giordani, Mate Boban and Michele Zorzi
• Abstract summary: We propose the design of a new entity, implemented at the RAN-level, that implements PQoS functionalities. Specifically, we focus on the design of the reward function of the learning agent, able to convert estimates into appropriate countermeasures if requirements are not satisfied. We demonstrate via ns-3 simulations that our approach achieves the best trade-off in terms of Quality of Experience (QoE) performance of end users in a teledriving-like scenario.
• Score: 18.54699818319184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly. In this context, Reinforcement Learning (RL) has come out as a promising approach to perform accurate predictions, and optimize the efficiency and adaptability of wireless networks. Along these lines, in this paper we propose the design of a new entity, implemented at the RAN-level that, with the support of an RL framework, implements PQoS functionalities. Specifically, we focus on the design of the reward function of the learning agent, able to convert QoS estimates into appropriate countermeasures if QoS requirements are not satisfied. We demonstrate via ns-3 simulations that our approach achieves the best trade-off in terms of QoS and Quality of Experience (QoE) performance of end users in a teleoperated-driving-like scenario, compared to other baseline solutions.

Related papers

• QoS prediction in radio vehicular environments via prior user information [54.853542701389074]
  We evaluate ML tree-ensemble methods to predict in the range of minutes with data collected from a cellular test network. Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles.
  arXiv  Detail & Related papers  (2024-02-27T17:05:41Z)
• Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing [57.497038270123774]
  We propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing. We analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
  arXiv  Detail & Related papers  (2024-01-19T11:04:14Z)
• Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems [62.25108152764568]
  Many web applications rely on solving optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks. We consider the performance of prediction and decision-making in a unified system. We provide a comprehensive categorization of current approaches and integrate existing experimental scenarios.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
  We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
  arXiv  Detail & Related papers  (2023-11-05T12:03:58Z)
• Human-in-the-loop online just-in-time software defect prediction [6.35776510153759]
  We propose Human-In-The-Loop (HITL) O-JIT-SDP that integrates feedback from SQA staff to enhance the prediction process. We also introduce a performance evaluation framework that utilizes a k-fold distributed bootstrap method along with the Wilcoxon signed-rank test. These advancements hold the potential to significantly enhance the value of O-JIT-SDP for industrial applications.
  arXiv  Detail & Related papers  (2023-08-25T23:40:08Z)
• Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks [16.930875340427765]
  We design a reinforcement learning agent to implement Predictive Quality of Service (PQoS) in vehicular networks. Our framework is able to identify the optimal level of compression to send automotive data under low latency and reliability constraints.
  arXiv  Detail & Related papers  (2023-02-22T10:35:00Z)
• Low Complexity Adaptive Machine Learning Approaches for End-to-End Latency Prediction [0.0]
  This work is the design of efficient, low-cost adaptive algorithms for estimation, monitoring and prediction. We focus on end-to-end latency prediction, for which we illustrate our approaches and results on data obtained from a public generator provided after the recent international challenge on GNN.
  arXiv  Detail & Related papers  (2023-01-31T10:29:11Z)
• Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems [29.362590041588454]
  We present possible methods to enable predictive (PQoS) in autonomous systems. We discuss which use cases will particularly benefit from network prediction. Then, we shed light on the challenges in the field that are still open for future research.
  arXiv  Detail & Related papers  (2021-09-20T08:55:51Z)
• QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning [2.3857747529378917]
  We propose a QA-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the satisfaction rate of users served by the network.
  arXiv  Detail & Related papers  (2021-07-14T09:18:39Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Cross Learning in Deep Q-Networks [82.20059754270302]
  We propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods. Our algorithm builds on double Q-learning, by maintaining a set of parallel models and estimate the Q-value based on a randomly selected network.
  arXiv  Detail & Related papers  (2020-09-29T04:58:17Z)

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.