posted on 2019-06-20, 09:18authored byJ Luo, J Tang, DKC So, G Chen, K Cumanan, JA Chambers
Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 61601186, in part by the Natural
Science Foundation of Guangdong Province under Grant 2017A030313383, in part by the Guangzhou Science Technology and Innovation
Commission under Grant 201707010159, and in part by the Open Research Fund of the National Mobile Communications Research
Laboratory, Southeast University, under Grant 2019D06.
History
Citation
IEEE Access, 2019, 7, pp. 17450-17460 (11)
Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering
Version
VoR (Version of Record)
Published in
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)