Self-organizing networks in next generation radio access networks: Application to fractional power control
Self-organizing networks (SONs) are considered as a driving technology that aims at enhancing usage of radio resources, at simplifying network management, and at reducing cost of operation of next generation radio access networks. This paper describes a framework for designing SON mechanisms for dynamically optimizing Radio Resource Management (RRM) functions. The base station is modeled as an agent that learns from its own local information and that of its neighbors to dynamically optimize RRM parameters. An application of the design framework to SON enabled fractional power control (FPC) in a LTE network is presented. The FPC is particularly important in OFDMA technology as a means to mitigate interference originated by uplink transmission power between neighboring cells. The agent uses fuzzy-reinforcement learning to dynamically adjust the FPC parameter to reach optimal tradeoffs between cell-edge and neighboring cell performance. The learning process is adapted to operate in a sporadic context related to the rapid variations in power, in users position and in the number of interferers. Results show important gain brought about by the self-optimizing FPC to the network capacity and to the perceived quality for data applications.
self-organizing network fractional power control lte reinforcement learning fuzzy q-learning