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pro vyhledávání: '"Aslam, Nauman"'
Nowadays, many machine learning (ML) solutions to improve the wireless standard IEEE802.11p for Vehicular Adhoc Network (VANET) are commonly evaluated in the simulated world. At the same time, this approach could be cost-effective compared to real-wo
Externí odkaz:
http://arxiv.org/abs/2409.16968
Optimizing QoS in HD Map Updates: Cross-Layer Multi-Agent with Hierarchical and Independent Learning
The data collected by autonomous vehicle (AV) sensors such as LiDAR and cameras is crucial for creating high-definition (HD) maps to provide higher accuracy and enable a higher level of automation. Nevertheless, offloading this large volume of raw da
Externí odkaz:
http://arxiv.org/abs/2408.11605
Reinforcement Learning (RL) algorithms have been used to address the challenging problems in the offloading process of vehicular ad hoc networks (VANET). More recently, they have been utilized to improve the dissemination of high-definition (HD) Maps
Externí odkaz:
http://arxiv.org/abs/2407.21460
Publikováno v:
2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM)
One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires con
Externí odkaz:
http://arxiv.org/abs/2408.03329
High-definition (HD) Map systems will play a pivotal role in advancing autonomous driving to a higher level, thanks to the significant improvement over traditional two-dimensional (2D) maps. Creating an HD Map requires a huge amount of on-road and of
Externí odkaz:
http://arxiv.org/abs/2402.14582
Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at
Externí odkaz:
http://arxiv.org/abs/2210.02840
Autor:
Zawish, Muhammad, Ashraf, Nouman, Ansari, Rafay Iqbal, Davy, Steven, Qureshi, Hassan Khaliq, Aslam, Nauman, Hassan, Syed Ali
6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial
Externí odkaz:
http://arxiv.org/abs/2203.06465
In this paper, to reduce the congestion rate at the city center and increase the quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAVP) is presented, where an Autonomous Vehicle (AV) is deployed in the c
Externí odkaz:
http://arxiv.org/abs/2109.11661
A myriad of recent literary works has leveraged generative adversarial networks (GANs) to generate unseen evasion samples. The purpose is to annex the generated data with the original train set for adversarial training to improve the detection perfor
Externí odkaz:
http://arxiv.org/abs/2109.08026
Recommendation systems rely heavily on users behavioural and preferential data (e.g. ratings, likes) to produce accurate recommendations. However, users experience privacy concerns due to unethical data aggregation and analytical practices carried ou
Externí odkaz:
http://arxiv.org/abs/2102.13453