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Such algorithms operate by building a model from inputs in order to make predictions or decisions, rather than following strictly static program instructions. These set of algorithms have successfully been applied to various applications such as computer security, bioinformatics, computer vision, medical diagnosis, and search engines.
Common to all these fields is the need to automatically process large set of data in Thesis about machine learning to generate useful insights and take appropriate decisions. Mobile networks are complex by nature.
A 4G LTE network today is by far more complex compared to a 2G GSM network due to increasing number of base stations and users, but also due to advancements in radio and network technology.
In addition, it is expected that the next-generation, 5G, mobile communication systems will handle an even broader set of scenarios, not fully addressed by current cellular systems. This includes massive deployment of ultra-low power sensors, intelligent traffic systems, critical low-latency communications, enterprise networks, etc.
To handle this complexity there is a need to deploy intelligent methods for analysing data from 4G and 5G networks.
Supervised Machine Learning. It is a good topic for machine learning masters thesis. It is a type of machine learning algorithm in which makes predictions based on known data-sets. Input and output is provided to the system along with feedback. Supervised Learning is further classified into classification and regression problems. Contact Persons Mehdi Amirijoo +46 73 [email protected] Master Thesis – Machine Learning for 5G Networks. Background. Machine learning constitutes a set of algorithms that can learn from and make predictions on data. The Master thesis will focus on methods to scale up and make parallel machine learning algorithms in order to deal effectively with fast and high dimensional streams of data by focusing in particular on time series forecasting.
Such methods need to reduce efforts for network management essentially offloading human effort needed to operate the networksbe able to draw new insights, and predict future network and user behaviour in order to make smarter decisions. This could result in higher network performance, better reliability and more adaptive systems.
Thesis Description This thesis work will investigate a machine learning method called deep learning with the purpose of improving 5G network performance.
Such a network will, using deep learning techniques, extract information and create relevant features that automatically optimizes network performance. In particular for 5G systems, the usage of beam forming will be a central technology component to increase data rates and coverage, where each base station will communicate with users through a set of defined beams pointing in different directions.
With users moving around in the network, when to trigger a user beam switch to another target beam is problematic since it is hard to predict if the candidate target beam provides better performance. For example, the target beam might experience heavy interference resulting in a lower user bitrate.
We would like to investigate possibilities of addressing the user beam switching using deep learning techniques. The purpose of the thesis is to use deep learning for extracting the features that optimizes the 5G network performance. Feasibility of such approaches will be investigated using a 5G simulator.
Extent This position is for one student. Ericsson complies with applicable country, state and all local laws governing nondiscrimination in employment in every location across the world in which the company has facilities.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, training and development. Ericsson expressly prohibits any form of workplace harassment based on race, color, religion, sex, sexual orientation, marital status, pregnancy, parental status, national origin, ethnic background, age, disability, political opinion, social status, veteran status, union membership or genetic information.Contact Persons Mehdi Amirijoo +46 73 [email protected] Master Thesis – Machine Learning for 5G Networks.
Machine learning constitutes a set of algorithms that can learn from and make predictions on data. Semester Thesis: Machine Learning algorithms for multiphase flows – Evaluating the potential and the limits The aim of this thesis is to investigate the potential of machine learning tools to analyse and predict the dynamic behavior of cavitating flows and cavitation erosion.
In cavitating flows, vapor filled. Search Machine Learning jobs in Sweden with company ratings & salaries. open jobs for Machine Learning in Sweden. 25 Best Cities for Jobs NEW! Jobs; Company Reviews. Company Reviews Master Thesis - Machine Learning for Bug Categorization.
Electronic . This thesis has investigated the use of machine learning in three key sub-tasks of information extraction: part of speech tagging, named entity recognition, and relation extraction. I have developed skills and interest in Machine Learning and data analytics after I started my Master's in the University of Helsinki.
One reason behind that is, I love to play with data a lot. Currently, I am working with audio-based sensing as a Research Assistant at the University of Helsinki. Master's thesis worker (Data analysis) at GE. The Master thesis will focus on methods to scale up and make parallel machine learning algorithms in order to deal effectively with fast and high dimensional streams of data by focusing in particular on time series forecasting.