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Smallcells have recently emerged as a potential approach for local area deployments that can satisfy high data rate requirements, reduce energy consumption and enhance network coverage. In this paper, we work on maximizing the weighted sum energy efficiency (WS-EE) for densely deployed smallcell networks. Due to the combinatorial and the general fractional program nature of the resource allocation problem, WS-EE maximization is non-convex and the optimal joint resource blocks (RBs) and power allocation is NP-hard. To solve this complex problem, we propose to decompose the primal problem into two subproblems (referred as RBs allocation and power control) and solve the subproblems sequentially. For the RBs allocation subproblem given any feasible network power profile, the optimal solution can be solved by maximizing throughput locally. For the power control subproblem, we propose to solve it locally based on a new defined pricing factor. Then, a distributed power control algorithm with guaranteed convergence is designed to achieve a Karush-Kuhn-Tucker (KKT) point of the primal problem. Simulation results verify the performance improvement of our proposed resource allocation scheme in terms of WS-EE. Besides, the performance evaluation shows the tradeoff between the WS-EE and the sum rate of the smallcell networks.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.11 pp.2362-2370

- Publication Date
- 2018/11/01

- Publicized
- 2018/04/27

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.2017EBP3425

- Type of Manuscript
- PAPER

- Category
- Terrestrial Wireless Communication/Broadcasting Technologies

Guodong ZHANG

Nantong University

Shibing ZHANG

Nantong University

Zhihua BAO

Nantong University

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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Guodong ZHANG, Shibing ZHANG, Zhihua BAO, "Distributed Energy Efficient Resource Allocation for OFDMA Smallcell Networks" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 11, pp. 2362-2370, November 2018, doi: 10.1587/transcom.2017EBP3425.

Abstract: Smallcells have recently emerged as a potential approach for local area deployments that can satisfy high data rate requirements, reduce energy consumption and enhance network coverage. In this paper, we work on maximizing the weighted sum energy efficiency (WS-EE) for densely deployed smallcell networks. Due to the combinatorial and the general fractional program nature of the resource allocation problem, WS-EE maximization is non-convex and the optimal joint resource blocks (RBs) and power allocation is NP-hard. To solve this complex problem, we propose to decompose the primal problem into two subproblems (referred as RBs allocation and power control) and solve the subproblems sequentially. For the RBs allocation subproblem given any feasible network power profile, the optimal solution can be solved by maximizing throughput locally. For the power control subproblem, we propose to solve it locally based on a new defined pricing factor. Then, a distributed power control algorithm with guaranteed convergence is designed to achieve a Karush-Kuhn-Tucker (KKT) point of the primal problem. Simulation results verify the performance improvement of our proposed resource allocation scheme in terms of WS-EE. Besides, the performance evaluation shows the tradeoff between the WS-EE and the sum rate of the smallcell networks.

URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3425/_p

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@ARTICLE{e101-b_11_2362,

author={Guodong ZHANG, Shibing ZHANG, Zhihua BAO, },

journal={IEICE TRANSACTIONS on Communications},

title={Distributed Energy Efficient Resource Allocation for OFDMA Smallcell Networks},

year={2018},

volume={E101-B},

number={11},

pages={2362-2370},

abstract={Smallcells have recently emerged as a potential approach for local area deployments that can satisfy high data rate requirements, reduce energy consumption and enhance network coverage. In this paper, we work on maximizing the weighted sum energy efficiency (WS-EE) for densely deployed smallcell networks. Due to the combinatorial and the general fractional program nature of the resource allocation problem, WS-EE maximization is non-convex and the optimal joint resource blocks (RBs) and power allocation is NP-hard. To solve this complex problem, we propose to decompose the primal problem into two subproblems (referred as RBs allocation and power control) and solve the subproblems sequentially. For the RBs allocation subproblem given any feasible network power profile, the optimal solution can be solved by maximizing throughput locally. For the power control subproblem, we propose to solve it locally based on a new defined pricing factor. Then, a distributed power control algorithm with guaranteed convergence is designed to achieve a Karush-Kuhn-Tucker (KKT) point of the primal problem. Simulation results verify the performance improvement of our proposed resource allocation scheme in terms of WS-EE. Besides, the performance evaluation shows the tradeoff between the WS-EE and the sum rate of the smallcell networks.},

keywords={},

doi={10.1587/transcom.2017EBP3425},

ISSN={1745-1345},

month={November},}

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TY - JOUR

TI - Distributed Energy Efficient Resource Allocation for OFDMA Smallcell Networks

T2 - IEICE TRANSACTIONS on Communications

SP - 2362

EP - 2370

AU - Guodong ZHANG

AU - Shibing ZHANG

AU - Zhihua BAO

PY - 2018

DO - 10.1587/transcom.2017EBP3425

JO - IEICE TRANSACTIONS on Communications

SN - 1745-1345

VL - E101-B

IS - 11

JA - IEICE TRANSACTIONS on Communications

Y1 - November 2018

AB - Smallcells have recently emerged as a potential approach for local area deployments that can satisfy high data rate requirements, reduce energy consumption and enhance network coverage. In this paper, we work on maximizing the weighted sum energy efficiency (WS-EE) for densely deployed smallcell networks. Due to the combinatorial and the general fractional program nature of the resource allocation problem, WS-EE maximization is non-convex and the optimal joint resource blocks (RBs) and power allocation is NP-hard. To solve this complex problem, we propose to decompose the primal problem into two subproblems (referred as RBs allocation and power control) and solve the subproblems sequentially. For the RBs allocation subproblem given any feasible network power profile, the optimal solution can be solved by maximizing throughput locally. For the power control subproblem, we propose to solve it locally based on a new defined pricing factor. Then, a distributed power control algorithm with guaranteed convergence is designed to achieve a Karush-Kuhn-Tucker (KKT) point of the primal problem. Simulation results verify the performance improvement of our proposed resource allocation scheme in terms of WS-EE. Besides, the performance evaluation shows the tradeoff between the WS-EE and the sum rate of the smallcell networks.

ER -