Reinforcement Learning for Efficient Access Control with M2M/H2H traffic in LTE-A networks

Speaker : Diego Pacheco Paramo
Universidad Sergio Arboleda, Colombie
Date: 03/01/2018
Time: 2:00 pm - 4:00 pm
Location: LINCS Seminars room


Although many advantages are expected from the provision of services for M2M communications in cellular networks such as extended coverage, security, robust management and lower deployment costs, coexistence with a large number of M2M devices is still an important challenge, in part due to the difficulty in allowing simultaneous access. Although the random access procedure in LTE-A is adequate for H2H communications, it is necessary to optimize this mechanism when M2M communications are considered. One of the proposed methods in 3GPP is Access Class Barring (ACB), which is able to reduce the number of simultaneous users contending for access. However, it is still not clear how to adapt its parameters in dynamic or bursty scenarios, such as those that appear when M2M communications are introduced.We propose a dynamic mechanism for ACB based on reinforcement learning, which aims to reduce the impact that M2M communications have over H2H communications, while at the same time ensuring that the KPIs for all users are in acceptable levels.