Vehicle-to-everything (V2X) networks support a variety of safety, entertainment, and commercial applications. This is realized by applying the principles of the Internet of Vehicles (IoV) to facilitate connectivity among vehicles and between vehicles and roadside units (RSUs). Network congestion management is essential for IoVs and it represents a significant concern due to its impact on improving the efficiency of transportation systems and providing reliable communication among vehicles for the timely delivery of safety-critical packets. This paper introduces a framework for proactive congestion management for IoV networks. We generate congestion scenarios and a data set to predict the congestion using LSTM. We present the framework and the packet congestion dataset. Simulation results using SUMO with NS3 demonstrate the effectiveness of the framework for forecasting IoV network congestion and clustering/prioritizing packets employing recurrent neural networks.
Proactive TCP mechanism to improve Handover performance in Mobile Satellite and Terrestrial Networks
正在出现的用于卫星系统的Geo Mobile Radio（GMR-1）的标准化在上层协议层与地面GSM（全球移动通信系统）具有很强的相似性，TCP（传输控制协议）是其中之一。这种空间段技术以及地面技术的特征在于通信特性和覆盖范围的周期性变化，当移动节点（MN）的连接改变随机性时，导致正在进行的呼叫的终止。虽然规定提供有效的通信基础设施，这种混合空间和地面网络必须确保端到端的网络性能，使MN可以在这些网络之间无缝移动。然而，从连接性的角度来看，当前的TCP性能还没有被设计用于多无线电MN中的移动性事件。传统上，TCP采用一套拥塞控制算法（慢启动、拥塞避免、快速拥塞、快速恢复）来探测连接路径上的当前可用带宽，这些算法需要多次往返才能找到正确的传输速率（即拥塞窗口），并适应切换引起的连接性突变。虽然有一些协议，如移动IP（MIP）和主机身份协议（HIP），以保持连接的连续性的移动性事件，TCP性能工程的关注较少。TCP被实现为操作系统中的单独组件，因此通常不知道移动性事件或多无线电通信的性质。本文旨在提高TCP在移动卫星和地面网络中的通信性能。
Proactive URLLC Adaptation for Connected Vehicles Through ML-Based Channel Prediction
National Laboratory of Wireless Communications (WiLab), CNIT（无线通信国家实验室）；Università di Bologna（博洛尼亚大学）；Università Mediterranea di Reggio Calabria（雷焦卡拉布里亚地中海大学）；Heisenberg Research Center, Huawei Technologies Duesseldorf GmbH（海森堡研究中心，华为技术杜塞尔多夫有限公司）
Graduate School of Engineering Nagaoka University of Technology Nagaoka, Niigata, Japan；Graduate School of Engineering Nagaoka University of Technology Nagaoka, Niigata, Japan k\
From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control
University of Naples Federico II（那不勒斯费德里科二世大学）；New York University（纽约大学）；University of Southern California（南加州大学）；University of Trento（特伦托大学）；Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)（加泰罗尼亚电信技术中心）
