Related papers: NR-V2X Quality of Service Prediction Through Machi…

Neural Networks (NNs) trained through supervised learning struggle with managing edge-case scenarios common in real-world driving due to the intractability of exhaustive datasets covering all edge-cases, making knowledge-driven approaches,…

The next generation wireless networks need efficient mechanisms for data dissemination that should support users with better Quality of Service (QoS). Nevertheless, the existing solutions are unable to handle this demand and require either…

This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and…

Reliable modeling of block error rate in vehicle-to-everything wireless networks is critical for designing robust communication systems under dynamic mobility and diverse channel conditions. Traditional machine learning approaches, such as…

The Vehicle to Vehicle and Vehicle to Infrastructure V2X communication systems are one of the main topics in research domain. Its performance evaluation is an important step before their on board integration into vehicles and its probable…

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an…

In recent years, vehicles became able to establish connections with other vehicles and infrastructure units that are located in the roadside. In the near future, the vehicular network will be expanded to include the communication between…

Training Mixture-of-Experts (MoE) models introduces sparse and highly imbalanced all-to-all communication that dominates iteration time. Conventional load-balancing methods fail to exploit the deterministic topology of Rail architectures,…

The delivery of high-quality, low-latency video streams is critical for remote autonomous vehicle control, where operators must intervene in real time. However, reliable video delivery over Fourth/Fifth-Generation (4G/5G) mobile networks is…

Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining…

The provision of reliable connectivity is envisioned as a key enabler for future autonomous driving. Anticipatory communication techniques have been proposed for proactively considering the properties of the highly dynamic radio channel…

With the emerging of the fifth generation (5G) mobile communication systems, millimeter wave transmissions are believed to be a promising solution for vehicular networks, especially in vehicle to vehicle (V2V) communications. In millimeter…

Next-generation wireless cellular networks are expected to provide unparalleled Quality-of-Service (QoS) for emerging wireless applications, necessitating strict performance guarantees, e.g., in terms of link-level data rates. A critical…

In the emerging high mobility Vehicle-to-Everything (V2X) communications using millimeter Wave (mmWave) and sub-THz, Multiple-Input Multiple-Output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies,…

Cellular Vehicle-to-Everything (C-V2X) is a frontier in the evolution of distributed communication introduced in 3GPP release 14 to advanced use cases. While research efforts continue to optimize the accessible bandwidth for transportation…

Smart cities need to connect physical devices as a network to improve the efficiency of city operations and services. Intelligent Transportation System (ITS) is one of the key components in smart cities, due to its capability of supporting…

Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number of instances used for training the model, an approach…

C-V2X (Cellular Vehicle-to-Everything) is the important enabling technology for autonomous driving and intelligent transportation systems. It evolves from LTE (Long Term Evolution)-V2X to NR (New Radio)-V2X, which will coexist and be…

Cross-validation plays a fundamental role in Machine Learning, enabling robust evaluation of model performance and preventing overestimation on training and validation data. However, one of its drawbacks is the potential to create data…

Extended Reality (XR)-aided teleoperation has shown its potential in improving operating efficiency in mission-critical, rich-information and complex scenarios. The multi-sensory XR devices introduce several new types of traffic with unique…