SON Self-Coordination Framework using Restricted Boltzmann Machines based Recommender Systems

Typ:

Studienarbeit, Advanced Research Project (MSCSP)

Betreuer:

M.Sc. Tanmoy Bag

Status:

ausgeschrieben

Beschreibung:

Restricted Boltzmann Machine (RBM) based Recommender System (RecSys) is to be studied and implemented to learn its applicability in the domain of Self-Organizing Network functions (SFs). The objective is to model the dynamics between the concurrently executing SFs in order to recommend appropriate network configurations according to the changing state of the environment.

Tasks
- Literature study on the conflict between ICIC and CCO SFs.
- Literature study on Deep Learning based Restricted Boltzmann Machines and Recommender Systems.
- Implement RBM based RecSys with the available infrastructure and compare results with state-of-the-art approaches.

Requirements
- Programming experience in Python & C++
- Basic knowledge of Machine Learning algorithms and frameworks

References
1. T. Bag, S. Garg, D. F. Preciado Rojas, A. Mitschele-Thiel, “Machine Learning based Recommender Systems to achieve Self-Coordination between SON Functions”, in IEEE Transactions on Network and Service Management, Accepted Sept. 01, 2020.
2. H. Ben Yedder, U. Zakia, A. Ahmed and L. Trajković, "Modeling prediction in recommender systems using restricted boltzmann machine," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, 2017, pp. 2063-2068, doi: 10.1109/SMC.2017.8122923.