|Autors: Semov, P. T., Koleva, P. H., Tonchev, K., Poulkov, V. K., Mihovska, A.|
Title: Autonomous learning model for achieving multi cell load balancing capabilities in HetNet
Keywords: autonomic load balancing; machine learning; Self-organisation
Abstract: Heterogeneous networks (HetNets) have been proposed as a capacity and coverage enabler in LTE-Advanced and beyond communication networks. Their optimal operation requires a significant degree of self-organization. Autonomic Load Balancing (ALB) has been proposed as an important self-organizing (SON) function in the LTE radio access network (RAN). In this work, distributed ALB is achieved by implementing a programmable autonomous learning model. The optimization problem (load balancing) is split into many small optimization problems and tasks, which are solved by using machine learning algorithms. The load conditions of the E-UTRAN NodeB (eNBs) and the measurement reports from the mobile terminals are used for creating a decision map for the load balancing. The simulation results show that by using ALB, the system capacity can be improved significantly.
1. Rajesh, L., Bhoopathy Bagan, K., Tamilarasan, K., Meena, M., "Load balancing in heterogeneous network using machine learning technique", International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 8, pp. 1564-1569, 2019. - 2019 - в издания, индексирани в Scopus или Web of Science
2. Sun, Q., Chih-Lin, I., Duan, R., Wu, J., Xie, Y., "Data driven smart load balancing in wireless networks", 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings, 2020, DOI: 10.1109/ICCWorkshops49005.2020.9145444. - 2020 - в издания, индексирани в Scopus или Web of Science
Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science