SON Coordination in Heterogeneous Networks: A Reinforcement Learning Framework

An important problem of today's mobile network operators is to bring down the capital expenditures and operational expenditures. One strategy is to automate the parameter tuning on the small cells through the so-called self-organizing network (SON) functionalities, such as cell range expansion,...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2016-09, Vol.15 (9), p.5835-5847
Main Authors: Iacoboaiea, Ovidiu-Constantin, Sayrac, Berna, Ben Jemaa, Sana, Bianchi, Pascal
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Capital expenditures
CRE
eICIC
function approximation
Heterogeneous networks
Learning (artificial intelligence)
LTE
Mobile communication
Mobile computing
MRO
Optimization
reinforcement learning
SON coordination
SON instances
state aggregation
Telecommunications industry
Wireless communication
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Summary:An important problem of today's mobile network operators is to bring down the capital expenditures and operational expenditures. One strategy is to automate the parameter tuning on the small cells through the so-called self-organizing network (SON) functionalities, such as cell range expansion, mobility robustness optimization, or enhanced Inter-Cell Interference Coordination. Having several of these functionalities in the network will surely create conflicts, as, for example, they may try to change the same parameter in the opposite directions. This raises that the need for an SON COordinator (SONCO) meant to arbitrate the parameter change requests of the SON functions, ensuring some degree of fairness. It is difficult to anticipate the impact of accepting several simultaneous requests. In this paper, we provide a SONCO design based on reinforcement learning (RL) as it allows us to learn from previous experiences and improve our future decisions. Typically, RL algorithms are complex. To reduce this complexity, we employ two flavors of function approximation and provide a study-case. Results show that the proposed SONCO design is capable of biasing this fairness among the SON functions by means of weights attributed to the SON functions. Also, we evaluate the tracking capability of the algorithms.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2016.2571695