The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les petites cellules sont récemment apparues comme une approche potentielle pour les déploiements locaux, capables de satisfaire aux exigences de débit de données élevé, de réduire la consommation d'énergie et d'améliorer la couverture du réseau. Dans cet article, nous travaillons sur la maximisation de l’efficacité énergétique pondérée (WS-EE) pour les réseaux à petites cellules densément déployés. En raison de la nature combinatoire et générale du programme fractionnaire du problème d'allocation de ressources, la maximisation WS-EE est non convexe et les blocs de ressources conjoints optimaux (RB) et l'allocation de puissance sont NP-difficiles. Pour résoudre ce problème complexe, nous proposons de décomposer le problème principal en deux sous-problèmes (appelés allocation de RB et contrôle de puissance) et de résoudre les sous-problèmes séquentiellement. Pour le sous-problème d’allocation des RB, quel que soit le profil de puissance du réseau réalisable, la solution optimale peut être résolue en maximisant le débit localement. Pour le sous-problème de contrôle de puissance, nous proposons de le résoudre localement sur la base d’un nouveau facteur de tarification défini. Ensuite, un algorithme de contrôle de puissance distribué avec convergence garantie est conçu pour atteindre un point Karush-Kuhn-Tucker (KKT) du problème primal. Les résultats de la simulation vérifient l’amélioration des performances de notre schéma d’allocation de ressources proposé en termes de WS-EE. En outre, l'évaluation des performances montre le compromis entre le WS-EE et le débit total des réseaux à petites cellules.
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.
Copier
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
Copier
@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},}
Copier
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 -