A Q-Learning strategy for LTE mobility Load Balancing

Cellular radio networks are seldom uniformly loaded. This motivates the need for Load Balancing (LB), as has been defined in the LTE Self-Organization standard. It is expected that on overload, a serving cell (S-cell) initiates LB 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 that adjusts the CIOs between the S-cell and all its neighbors by a fixed step φ. Our results show that the best φ depends 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 φ values to apply for different load conditions and demonstrate that the QL based algorithm performs better than the best fixed φ algorithm in virtually all scenarios.