TY - GEN
T1 - Q-Learning for Policy Based SON Management in wireless Access Networks
AU - Daher, Tony
AU - Jemaa, Sana Ben
AU - Decreusefond, Laurent
N1 - Publisher Copyright:
© 2017 IFIP.
PY - 2017/7/20
Y1 - 2017/7/20
N2 - Self organized networks has been one of the first concrete implementations of autonomic network management concept. Currently, several Self-Organizing-Network (SON) functions are developed by Radio Access Network (RAN) vendors and already deployed in many networks all around the world. These functions have been designed independently to replace different operational tasks. The concern of making these functions work together in a coherent manner has been studied later in particular in SEMAFOUR project where a Policy Based SON Management (PBSM) framework has been proposed to holistically manage a SON enabled network, namely a network with several individual SON functions. Enriching this PBSM framework with cognition capability is the next step towards the realization of the initial promise of SON concept: a unique self-managed network that responds autonomously and efficiently to the operator high level requirements and objectives. This paper proposes a cognitive PBSM system that enhances the SON management decisions by learning from past experience using Q-learning approach. Our approach is evaluated by simulation on a SON enabled Long-Term Evolution Advanced (LTE-A) network with several SON functions. The paper shows that the decisions are enhanced during the learning process and discusses the implementation options of this solution.
AB - Self organized networks has been one of the first concrete implementations of autonomic network management concept. Currently, several Self-Organizing-Network (SON) functions are developed by Radio Access Network (RAN) vendors and already deployed in many networks all around the world. These functions have been designed independently to replace different operational tasks. The concern of making these functions work together in a coherent manner has been studied later in particular in SEMAFOUR project where a Policy Based SON Management (PBSM) framework has been proposed to holistically manage a SON enabled network, namely a network with several individual SON functions. Enriching this PBSM framework with cognition capability is the next step towards the realization of the initial promise of SON concept: a unique self-managed network that responds autonomously and efficiently to the operator high level requirements and objectives. This paper proposes a cognitive PBSM system that enhances the SON management decisions by learning from past experience using Q-learning approach. Our approach is evaluated by simulation on a SON enabled Long-Term Evolution Advanced (LTE-A) network with several SON functions. The paper shows that the decisions are enhanced during the learning process and discusses the implementation options of this solution.
U2 - 10.23919/INM.2017.7987442
DO - 10.23919/INM.2017.7987442
M3 - Conference contribution
AN - SCOPUS:85029445340
T3 - Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management
SP - 1091
EP - 1096
BT - Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management
A2 - Chemouil, Prosper
A2 - Simoes, Paulo
A2 - Madeira, Edmundo
A2 - Secci, Stefano
A2 - Monteiro, Edmundo
A2 - Gaspary, Luciano Paschoal
A2 - dos Santos, Carlos Raniery P.
A2 - Charalambides, Marinos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017
Y2 - 8 May 2017 through 12 May 2017
ER -