Energy-Efficient Optimization for Device-to-Device Communication Underlaying Cellular Networks

Haibo DAI; Chunguo LI; Luxi YANG

doi:10.1587/transfun.E100.A.1079

Energy-Efficient Optimization for Device-to-Device Communication Underlaying Cellular Networks

Haibo DAI, Chunguo LI, Luxi YANG

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In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.4 pp.1079-1083

Publication Date: 2017/04/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.1079

Type of Manuscript: LETTER

Category: Numerical Analysis and Optimization

Authors

Haibo DAI
  Southeast University
Chunguo LI
  Southeast University
Luxi YANG
  Southeast University

Keyword

energy efficiency, device-to-device communication, resource allocation, potential game, Max-logit learning

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Haibo DAI, Chunguo LI, Luxi YANG, "Energy-Efficient Optimization for Device-to-Device Communication Underlaying Cellular Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 4, pp. 1079-1083, April 2017, doi: 10.1587/transfun.E100.A.1079.
Abstract: In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1079/_p

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@ARTICLE{e100-a_4_1079,
author={Haibo DAI, Chunguo LI, Luxi YANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Energy-Efficient Optimization for Device-to-Device Communication Underlaying Cellular Networks},
year={2017},
volume={E100-A},
number={4},
pages={1079-1083},
abstract={In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.},
keywords={},
doi={10.1587/transfun.E100.A.1079},
ISSN={1745-1337},
month={April},}

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TY - JOUR
TI - Energy-Efficient Optimization for Device-to-Device Communication Underlaying Cellular Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1079
EP - 1083
AU - Haibo DAI
AU - Chunguo LI
AU - Luxi YANG
PY - 2017
DO - 10.1587/transfun.E100.A.1079
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2017
AB - In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.
ER -