Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California USA, Oct. 28-31, 2018

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

Faris B. Mismar and Brian L. Evans

Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA -

Final Paper (Archive) - Final Paper (Local) - Poster (PowerPoint) - Poster (PDF) - Software Release


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 DL SINR and spectral efficiency 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 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.

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Last Updated 11/07/18.