S
Shubhangi Bhadauria
Researcher at Fraunhofer Society
Publications - 6
Citations - 226
Shubhangi Bhadauria is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Vehicular ad hoc network & Quality of service. The author has an hindex of 2, co-authored 6 publications receiving 87 citations.
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6G White Paper on Machine Learning in Wireless Communication Networks
Sammad Ali,Walid Saad,Nandana Rajatheva,Kapseok Chang,Daniel Steinbach,Benjamin Sliwa,Christian Wietfeld,Kai Mei,Hamid Mohammad Shiri,Hans-Jurgen Zepernick,Thi My Chinh Chu,Ijaz Ahmad,Jykri Huusko,Jaakko Suutala,Shubhangi Bhadauria,Vimal Bhatia,Rangeet Mitra,SaiDhiraj Amuru,Robert Abbas,Baohua Shao,Michele Capobianco,Guanghui Yu,Maëlick Claes,Teemu Karvonen,Mingzhe Chen,Maksym A. Girnyk,Hassan Malik +26 more
TL;DR: An overview of the vision of how machine learning will impact the wireless communication systems and the ML methods that have the highest potential to be used in wireless networks are provided.
Journal ArticleDOI
V2X in 3GPP Standardization: NR Sidelink in Rel-16 and Beyond.
TL;DR: In this article, the authors summarized the most important aspects of NR-V2X, which is standardized by 3GPP, focusing on sidelink communication, and the main part of this work belongs to the 3-GPP Rel-16.
Journal ArticleDOI
V2X in 3GPP Standardization: NR Sidelink in Release-16 and Beyond
TL;DR: In this paper, the authors summarized the most important aspects of NR-V2X, which is standardized by 3GPP, focusing on sidelink communication, and the main part of this work belongs to 3-GPP Release 16.
Proceedings ArticleDOI
Deep Reinforcement Learning based Congestion Control for V2X Communication
TL;DR: In this article, a centralized congestion control scheme for C-V2X communication based on the Deep Reinforcement Learning (DRL) framework is proposed to achieve the packet reception ratio (PRR) as per the packet's associated QoS while maintaining the average measured Channel Busy Ratio (CBR) below 0.65.
Proceedings ArticleDOI
QoS based Deep Reinforcement Learning for V2X Resource Allocation
TL;DR: This paper presents a QoS aware decentralized resource allocation for V2X communication based on a deep reinforcement learning (DRL) framework that incorporates the independent QoS parameter that reflects the latency required in both user equipment (UE) and the base station.