TY - JOUR
T1 - Deep Learning-Based Sum Data Rate and Energy Efficiency Optimization for MIMO-NOMA Systems
AU - Huang, Hongji
AU - Yang, Yuchun
AU - Ding, Zhiguo
AU - Wang, Hong
AU - Sari, Hikmet
AU - Adachi, Fumiyuki
N1 - Funding Information:
Manuscript received July 10, 2019; revised October 4, 2019, February 10, 2020, and May 1, 2020; accepted May 2, 2020. Date of publication May 14, 2020; date of current version August 12, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61801246, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20170910, in part by the China Postdoc Innovation Talent Supporting Program under Grant BX20180143, in part by the Open Research Foundation of National Mobile Communications Research Laboratory of Southeast University under Grant 2018D09, and in part by the China Postdoctoral Science Foundation under Grant 2019M660126. The associate editor coordinating the review of this article and approving it for publication was T. Q. Quek. (Corresponding author: Hong Wang.) Hongji Huang is with the School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (e-mail: hongji.huang@ieee.org).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - The increasing demands for massive connectivity, low latency, and high reliability of future communication networks require new techniques. Multiple-input-multiple-output non-orthogonal multiple access (MIMO-NOMA), which incorporates the NOMA concept into MIMO, is an appealing technology to enhance system throughput and energy efficiency. However, rapidly changing channel conditions and extremely complex spatial structure degrade the system performance and hinder its application. Thus, to tackle these limitations, in this paper, we propose a deep learning-based MIMO-NOMA framework for maximizing the sum data rate and energy efficiency. To be specific, we design an effective communication deep neural network (CDNN) in which several convolutional layers and multiple hidden layers are included. Thanks to the impressive representation ability of the deep learning technique, the CDNN framework addresses the power allocation problem for achieving higher data rate and energy efficiency of MIMO-NOMA with the aid of training algorithms. Additionally, simulation results corroborate that the proposed CDNN framework is a good candidate to enhance the performance of MIMO-NOMA in term of power allocation, and extensive simulations show that it realizes larger sum data rate and energy efficiency compared with conventional strategies.
AB - The increasing demands for massive connectivity, low latency, and high reliability of future communication networks require new techniques. Multiple-input-multiple-output non-orthogonal multiple access (MIMO-NOMA), which incorporates the NOMA concept into MIMO, is an appealing technology to enhance system throughput and energy efficiency. However, rapidly changing channel conditions and extremely complex spatial structure degrade the system performance and hinder its application. Thus, to tackle these limitations, in this paper, we propose a deep learning-based MIMO-NOMA framework for maximizing the sum data rate and energy efficiency. To be specific, we design an effective communication deep neural network (CDNN) in which several convolutional layers and multiple hidden layers are included. Thanks to the impressive representation ability of the deep learning technique, the CDNN framework addresses the power allocation problem for achieving higher data rate and energy efficiency of MIMO-NOMA with the aid of training algorithms. Additionally, simulation results corroborate that the proposed CDNN framework is a good candidate to enhance the performance of MIMO-NOMA in term of power allocation, and extensive simulations show that it realizes larger sum data rate and energy efficiency compared with conventional strategies.
KW - MIMO-NOMA
KW - deep learning
KW - energy efficiency
KW - power allocation
KW - sum data rate
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U2 - 10.1109/TWC.2020.2992786
DO - 10.1109/TWC.2020.2992786
M3 - Article
AN - SCOPUS:85089945142
SN - 1536-1276
VL - 19
SP - 5373
EP - 5388
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 8
M1 - 9093213
ER -
