Concurrent cooperative games for coordinating SON functions in cognitive cellular networks

Multiple Self-Organizing Networks (SON) functions have been deveoped towards the SON promise of automating cellular network operations. Meanwhile, advancing SON towards Cognitive Cellular Networks requires the (SON) Functions (SFs) to autonomously learn the required optimal configurations. Since the SFs adjust the same or related network parameters, conflicts are bound to occur. Mechanisms that are better than current SON coordination approaches must thus be devised to manage the conflicts. In this paper we propose multi-agent Concurrent Cooperative Games (CCG) an approach where peer SFs communicate with one another so as to learn to minimize the conflicts. Using two Q-learning based SFs, we evaluate the benefits of CCG comparing against the independent functions and their uncoordinated operation. Our results show that CCG achieves good compromise especially where concurrent action among neighbor cells is avoided.