TY - GEN
T1 - Energy Efficient resource optimization for a downlink noma heterogeneous small-cell network
AU - Fang, Fang
AU - Cheng, Julian
AU - Ding, Zhiguo
AU - Poor, H. Vincent
N1 - Funding Information:
Fang Fang and Zhiguo Ding are with School of Electrical and Electronic Engineering, The University of Manchester, M13 9PL, UK (e-mail: [email protected], [email protected]). Julian Cheng is with School of Engineering, The University of British Columbia, Kelowna, BC, V1V 1V7, Canada (e-mail: [email protected]). H. Vincent Poor is with the Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA (e-mail: [email protected]). This work was supported in part by the U.S. National Science Foundation under Grants CNS-1702808 and ECCS-1647198.
Publisher Copyright:
©2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - Non-orthogonal multiple access (NOMA) is considered to be a key technology for fifth-generation mobile communication networks due to its superior spectral efficiency. In this paper, the problem of energy efficient subchannel and power allocation is investigated for a downlink NOMA heterogeneous network. The energy efficient resource allocation problem is first formulated as a mixed integer nonconvex optimization problem. By exploiting convex relaxation and dual decomposition techniques, a closed-form expression is derived via a Lagrangian approach. Simulation results show that the proposed algorithm can converge within ten iterations and obtain high system energy efficiency.
AB - Non-orthogonal multiple access (NOMA) is considered to be a key technology for fifth-generation mobile communication networks due to its superior spectral efficiency. In this paper, the problem of energy efficient subchannel and power allocation is investigated for a downlink NOMA heterogeneous network. The energy efficient resource allocation problem is first formulated as a mixed integer nonconvex optimization problem. By exploiting convex relaxation and dual decomposition techniques, a closed-form expression is derived via a Lagrangian approach. Simulation results show that the proposed algorithm can converge within ten iterations and obtain high system energy efficiency.
UR - http://www.scopus.com/inward/record.url?scp=85053632312&partnerID=8YFLogxK
U2 - 10.1109/SAM.2018.8448840
DO - 10.1109/SAM.2018.8448840
M3 - Conference contribution
AN - SCOPUS:85053632312
SN - 9781538647523
T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
SP - 51
EP - 55
BT - 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PB - IEEE Computer Society
T2 - 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Y2 - 8 July 2018 through 11 July 2018
ER -
