0000000001197146
AUTHOR
Pyry Kotilainen
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
Cyber security of vehicle CAN bus
There are currently many research projects underway concerning the intelligent transport system (ITS), with the intent to develop a variety of communication solutions between vehicles, roadside stations and services. In the near future, the roll-out of 5G networks will improve short-range vehicle-to-vehicle traffic and vehicle-to-infrastructure communications. More extensive services can be introduced due to almost non-delayed response time. Cyber security is central for the usability of the services and, most importantly, for car safety. The Controller Area Network (CAN) is an automation bus that was originally designed for real-time data transfer of distributed control systems to cars. La…