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

TL;DR

This paper presents a deep reinforcement learning algorithm that enables cellular networks to autonomously manage faults and enhance radio performance, demonstrating superior results over existing methods in realistic simulations.

Contribution

Introduces a deep RL-based fault management algorithm for self-organizing networks that operates efficiently and improves radio link quality in practical scenarios.

Findings

Algorithm effectively clears alarms and improves performance.

Outperforms existing fault management algorithms.

Operates in polynomial runtime.

Abstract

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.