Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

We propose a method to improve the radio link performance in a wireless network using a deep Q-Learning based algorithm. In this paper, we use this reinforcement learning model to allow the wireless network cluster to self-heal by performing certain fault management actions which improves the radio link performance of this wireless network. The main contributions of this paper are: 1) introduce a radio performance tuning algorithm that self-organizing networks can implement in a polynomial runtime, 2) employ deep reinforcement learning to perform fault management, and 3) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that an optimal action sequence to clear alarms is feasible even against the randomness of the network faults and user movements.