Distributed cooperative Q-learning for mobility-sensitive handover optimization in LTE SON

Optimal settings for Handover parameters (Hysteresis and Time-to-Trigger) depend on user velocities in the network. The Self-Organization Networks (SON) standard defines the Mobility Robustness Optimization (MRO) use case for the autonomous methods of configuring the parameters in congruence to the mobility pattern. State of the art MRO solutions have relied on expert knowledge, rule based algorithms to search the parameter space; yet it is unwieldy to design rules for all possible mobility patterns in any network. In this work, we present a Q-learning MRO solution, QMRO, which learns the required parameter values appropriate for specific velocity conditions in the individual cells. We compare QMRO against the best static reference configuration (Ref) that is obtained by sweeping the parameter space. Our results show that QMRO is able to learn parameter settings that achieve similar performance to Ref in a realistic network environment where users have dynamically varying velocities.