Game theoretic Conflict Resolution Mechanism for Cognitive Autonomous Networks

Cognitive Autonomous Networks (CAN) advance network automation by using Cognitive Functions (CFs) which learn optimal behavior through interaction with the network. However, as in self Organizing Networks (SON), CFs encounter conflicts due to overlap in parameters or objectives. Owing to the nondeterministic behavior of CFs, their conflicts cannot be resolved using SON-style rule-based approaches. This paper proposes the Cognitive Bargaining Mechanism (CBM) as the optimal generic way for resolving - any type of conflict among CFs, conflict among any number of CFs and any number of simultaneously existing conflicts among CFs. With the CAN modeled as a multi-agent system (MAS), CBM uses Nash's Social Welfare Function (NSWF) to compute a compromise among CFs that is fair and optimal for the collective interest of the system. To prove the feasibility of the approach, we model three different CAN scenarios in Python and show the resulting configurations when a CBM-enabled controller is used to resolve all the possible conflicts in the CAN.