QoS prediction in radio vehicular environments via prior user information

• URL: http://arxiv.org/abs/2402.17689v1
• Date: Tue, 27 Feb 2024 17:05:41 GMT
• Title: QoS prediction in radio vehicular environments via prior user information
• Authors: Noor Ul Ain, Rodrigo Hernang\'omez, Alexandros Palaios, Martin Kasparick and S{\l}awomir Sta\'nczak
• Abstract summary: 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.
• Score: 54.853542701389074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving, platooning, cooperative maneuvering, teleoperated driving, and smart navigation. These and other use cases often rely on specific quality of service (QoS) levels for communication. Recently, the area of predictive quality of service (QoS) has received a great deal of attention as a key enabler to forecast communication quality well enough in advance. However, predicting QoS in a reliable manner is a notoriously difficult task. In this paper, we evaluate ML tree-ensemble methods to predict QoS in the range of minutes with data collected from a cellular test network. We discuss radio environment characteristics and we showcase how these can be used to improve ML performance and further support the uptake of ML in commercial networks. 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. Moreover, we are extending prior art by showing how longer prediction horizons can be supported.

Related papers

• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration [4.2371435508360085]
  In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent. Our trajectory prediction task is aimed at all the detected surrounding vehicles. To effectively integrate the multi-source data from both sensor and communication technologies, we propose a deep learning framework called MSMA.
  arXiv  Detail & Related papers  (2024-07-31T03:26:14Z)
• On the road to more accurate mobile cellular traffic predictions [0.0]
  We employ highway flow and average speed variables together with a cellular network traffic metric to predict the short-term future load on a cell. This is in sharp contrast to prior art that mainly studies urban scenarios. The learning structure can be used at a cell or edge level, and can find application in both federated and centralised learning.
  arXiv  Detail & Related papers  (2023-05-24T15:18:46Z)
• 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)
• Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies [56.77079930521082]
  We have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage.
  arXiv  Detail & Related papers  (2022-12-20T15:26:39Z)
• Utilizing Background Knowledge for Robust Reasoning over Traffic Situations [63.45021731775964]
  We focus on a complementary research aspect of Intelligent Transportation: traffic understanding. We scope our study to text-based methods and datasets given the abundant commonsense knowledge. We adopt three knowledge-driven approaches for zero-shot QA over traffic situations.
  arXiv  Detail & Related papers  (2022-12-04T09:17:24Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Federated Learning in Vehicular Networks [41.89469856322786]
  Federated learning (FL) framework has been introduced as an efficient tool with the goal of reducing transmission overhead. In this paper, we investigate the usage of FL over centralized learning (CL) in vehicular network applications to develop intelligent transportation systems. We identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management.
  arXiv  Detail & Related papers  (2020-06-02T06:32:59Z)
• ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots [65.33650222396078]
  We develop a parking lot environment and collect a dataset of human parking maneuvers. We compare a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment
  arXiv  Detail & Related papers  (2020-04-21T20:46:32Z)
• Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning [15.493225546165627]
  Age of Information (AoI) is a metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI.
  arXiv  Detail & Related papers  (2020-04-04T05:19:25Z)

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.