IEICE TRANSACTIONS on Communications
Neural Network Based Transmit Power Control and Interference Cancellation for MIMO Small Cell Networks
Summary :
The random deployment of small cell base stations (BSs) causes the coverage areas of neighboring cells to overlap, which increases intercell interference and degrades the system capacity. This paper proposes a new intercell interference management (IIM) scheme to improve the system capacity in multiple-input multiple-output (MIMO) small cell networks. The proposed IIM scheme consists of both an interference cancellation (IC) technique on the receiver side, and a neural network (NN) based power control algorithm for intercell interference coordination (ICIC) on the transmitter side. In order to improve the system capacity, the NN power control optimizes downlink transmit power while IC eliminates interfering signals from received signals. Computer simulations compare the system capacity of the MIMO network with several ICIC algorithms: the NN, the greedy search, the belief propagation (BP), the distributed pricing (DP), and the maximum power, all of which can be combined with IC reception. Furthermore, this paper investigates the application of a multi-layered NN structure called deep learning and its pre-training scheme, into the mobile communication field. It is shown that the performance of NN is better than that of BP and very close to that of greedy search. The low complexity of the NN algorithm makes it suitable for IIM. It is also demonstrated that combining IC and sectorization of BSs acquires high capacity gain owing to reduced interference.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E99-B No.5 pp.1157-1169
- Publication Date
- 2016/05/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2015EBP3358
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Michael Andri WIJAYA
Tokyo Institute of Technology
Kazuhiko FUKAWA
Tokyo Institute of Technology
Hiroshi SUZUKI
Tokyo Institute of Technology
Keyword
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Michael Andri WIJAYA, Kazuhiko FUKAWA, Hiroshi SUZUKI, "Neural Network Based Transmit Power Control and Interference Cancellation for MIMO Small Cell Networks" in IEICE TRANSACTIONS on Communications,
vol. E99-B, no. 5, pp. 1157-1169, May 2016, doi: 10.1587/transcom.2015EBP3358.
Abstract: The random deployment of small cell base stations (BSs) causes the coverage areas of neighboring cells to overlap, which increases intercell interference and degrades the system capacity. This paper proposes a new intercell interference management (IIM) scheme to improve the system capacity in multiple-input multiple-output (MIMO) small cell networks. The proposed IIM scheme consists of both an interference cancellation (IC) technique on the receiver side, and a neural network (NN) based power control algorithm for intercell interference coordination (ICIC) on the transmitter side. In order to improve the system capacity, the NN power control optimizes downlink transmit power while IC eliminates interfering signals from received signals. Computer simulations compare the system capacity of the MIMO network with several ICIC algorithms: the NN, the greedy search, the belief propagation (BP), the distributed pricing (DP), and the maximum power, all of which can be combined with IC reception. Furthermore, this paper investigates the application of a multi-layered NN structure called deep learning and its pre-training scheme, into the mobile communication field. It is shown that the performance of NN is better than that of BP and very close to that of greedy search. The low complexity of the NN algorithm makes it suitable for IIM. It is also demonstrated that combining IC and sectorization of BSs acquires high capacity gain owing to reduced interference.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2015EBP3358/_p
Copy
@ARTICLE{e99-b_5_1157,
author={Michael Andri WIJAYA, Kazuhiko FUKAWA, Hiroshi SUZUKI, },
journal={IEICE TRANSACTIONS on Communications},
title={Neural Network Based Transmit Power Control and Interference Cancellation for MIMO Small Cell Networks},
year={2016},
volume={E99-B},
number={5},
pages={1157-1169},
abstract={The random deployment of small cell base stations (BSs) causes the coverage areas of neighboring cells to overlap, which increases intercell interference and degrades the system capacity. This paper proposes a new intercell interference management (IIM) scheme to improve the system capacity in multiple-input multiple-output (MIMO) small cell networks. The proposed IIM scheme consists of both an interference cancellation (IC) technique on the receiver side, and a neural network (NN) based power control algorithm for intercell interference coordination (ICIC) on the transmitter side. In order to improve the system capacity, the NN power control optimizes downlink transmit power while IC eliminates interfering signals from received signals. Computer simulations compare the system capacity of the MIMO network with several ICIC algorithms: the NN, the greedy search, the belief propagation (BP), the distributed pricing (DP), and the maximum power, all of which can be combined with IC reception. Furthermore, this paper investigates the application of a multi-layered NN structure called deep learning and its pre-training scheme, into the mobile communication field. It is shown that the performance of NN is better than that of BP and very close to that of greedy search. The low complexity of the NN algorithm makes it suitable for IIM. It is also demonstrated that combining IC and sectorization of BSs acquires high capacity gain owing to reduced interference.},
keywords={},
doi={10.1587/transcom.2015EBP3358},
ISSN={1745-1345},
month={May},}
Copy
TY - JOUR
TI - Neural Network Based Transmit Power Control and Interference Cancellation for MIMO Small Cell Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1157
EP - 1169
AU - Michael Andri WIJAYA
AU - Kazuhiko FUKAWA
AU - Hiroshi SUZUKI
PY - 2016
DO - 10.1587/transcom.2015EBP3358
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2016
AB - The random deployment of small cell base stations (BSs) causes the coverage areas of neighboring cells to overlap, which increases intercell interference and degrades the system capacity. This paper proposes a new intercell interference management (IIM) scheme to improve the system capacity in multiple-input multiple-output (MIMO) small cell networks. The proposed IIM scheme consists of both an interference cancellation (IC) technique on the receiver side, and a neural network (NN) based power control algorithm for intercell interference coordination (ICIC) on the transmitter side. In order to improve the system capacity, the NN power control optimizes downlink transmit power while IC eliminates interfering signals from received signals. Computer simulations compare the system capacity of the MIMO network with several ICIC algorithms: the NN, the greedy search, the belief propagation (BP), the distributed pricing (DP), and the maximum power, all of which can be combined with IC reception. Furthermore, this paper investigates the application of a multi-layered NN structure called deep learning and its pre-training scheme, into the mobile communication field. It is shown that the performance of NN is better than that of BP and very close to that of greedy search. The low complexity of the NN algorithm makes it suitable for IIM. It is also demonstrated that combining IC and sectorization of BSs acquires high capacity gain owing to reduced interference.
ER -
English
