Abstract
With the huge increases in traffic volumes and subscribers, diverse devices, and rich media applications, manual management of mobile network becomes highly challenging in terms of optimization and management. Self-organizing networks (SON) has been introduced to optimize the network in an automatic manner. In this paper, we address the coverage and capacity joint optimization (CCO) by adaptively and simultaneously adjusting both antenna tilt and power. To this end, we propose: · a multi-player multi-armed bandit (MAB) framework (decentralized restless upper confidence bound (RUCB) algorithm) with a change point detection test based on Page-Hinkley (PH) statistics used to decide whether some change has occurred in the environment. Then, the strategy is designed to deal with such a change. · a central unit to deal with simultaneous conflicting actions when many cells decide to start the optimization process at the same time. · a Pareto search framework to deal with multi-objective optimization (CCO). To evaluate our work, we compared our proposal with the fixed antenna parameter scheme and with the linear scalarization function that transforms the multi-objective optimization problem into a scalar function. Simulation results show that the proposed method could improve user experience in terms of cell-center capacity and cell-edge coverage compared to different conventional methods and under different number of users.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-5 |
| Number of pages | 5 |
| Volume | 2017-September |
| ISBN (Electronic) | 9781509059355 |
| DOIs | |
| Publication status | Published - 2018 Feb 8 |
| Event | 86th IEEE Vehicular Technology Conference, VTC Fall 2017 - Toronto, Canada Duration: 2017 Sep 24 → 2017 Sep 27 |
Other
| Other | 86th IEEE Vehicular Technology Conference, VTC Fall 2017 |
|---|---|
| Country | Canada |
| City | Toronto |
| Period | 17/9/24 → 17/9/27 |
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ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics
Cite this
Antenna parameters optimization in self-organizing networks : Multi-armed bandits with Pareto search. / Dhahri, Chaima; Ohtsuki, Tomoaki.
2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings. Vol. 2017-September Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-5.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Antenna parameters optimization in self-organizing networks
T2 - Multi-armed bandits with Pareto search
AU - Dhahri, Chaima
AU - Ohtsuki, Tomoaki
PY - 2018/2/8
Y1 - 2018/2/8
N2 - With the huge increases in traffic volumes and subscribers, diverse devices, and rich media applications, manual management of mobile network becomes highly challenging in terms of optimization and management. Self-organizing networks (SON) has been introduced to optimize the network in an automatic manner. In this paper, we address the coverage and capacity joint optimization (CCO) by adaptively and simultaneously adjusting both antenna tilt and power. To this end, we propose: · a multi-player multi-armed bandit (MAB) framework (decentralized restless upper confidence bound (RUCB) algorithm) with a change point detection test based on Page-Hinkley (PH) statistics used to decide whether some change has occurred in the environment. Then, the strategy is designed to deal with such a change. · a central unit to deal with simultaneous conflicting actions when many cells decide to start the optimization process at the same time. · a Pareto search framework to deal with multi-objective optimization (CCO). To evaluate our work, we compared our proposal with the fixed antenna parameter scheme and with the linear scalarization function that transforms the multi-objective optimization problem into a scalar function. Simulation results show that the proposed method could improve user experience in terms of cell-center capacity and cell-edge coverage compared to different conventional methods and under different number of users.
AB - With the huge increases in traffic volumes and subscribers, diverse devices, and rich media applications, manual management of mobile network becomes highly challenging in terms of optimization and management. Self-organizing networks (SON) has been introduced to optimize the network in an automatic manner. In this paper, we address the coverage and capacity joint optimization (CCO) by adaptively and simultaneously adjusting both antenna tilt and power. To this end, we propose: · a multi-player multi-armed bandit (MAB) framework (decentralized restless upper confidence bound (RUCB) algorithm) with a change point detection test based on Page-Hinkley (PH) statistics used to decide whether some change has occurred in the environment. Then, the strategy is designed to deal with such a change. · a central unit to deal with simultaneous conflicting actions when many cells decide to start the optimization process at the same time. · a Pareto search framework to deal with multi-objective optimization (CCO). To evaluate our work, we compared our proposal with the fixed antenna parameter scheme and with the linear scalarization function that transforms the multi-objective optimization problem into a scalar function. Simulation results show that the proposed method could improve user experience in terms of cell-center capacity and cell-edge coverage compared to different conventional methods and under different number of users.
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U2 - 10.1109/VTCFall.2017.8288237
DO - 10.1109/VTCFall.2017.8288237
M3 - Conference contribution
VL - 2017-September
SP - 1
EP - 5
BT - 2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -