Q-Learning for LTE Self-Organized Mobility Load balancing
Dr.-Ing. Stephen Mwanje
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
- Date of publication
- Cellular radio networks are seldom uniformly loaded.
This motivates the need for Load Balancing (LB), ashas been
defined in the LTE Self-Organization standard. It is expected
that on overload, a serving cell (S-cell) initiatesLB to transfer
some of its edge users to its neighbor cells so called target cells, by
adjusting the Cell Individual Offset (CIO) parameter. In this
work, we have proposed a reactive LB algorithm thatadjusts the
CIOs between the S-cell and all its neighbors by a fixed step φ.
Our results show that the best φdepends on the load conditions
in both the S-cell and its neighbors as well as on the S-cell's user
distribution. We then propose a Q-Learning (QL) algorithm that
learns the best φvalues to apply for different load conditions and
demonstrate that the QL based algorithm performs better than
the best fixed φalgorithm in virtually all scenarios.