Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44527
Title: A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA With SWIPT
Authors: Luo, J
Tang, J
So, DKC
Chen, G
Cumanan, K
Chambers, JA
First Published: 25-Jan-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Access, 2019, 7, pp. 17450-17460 (11)
Abstract: 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.
DOI Link: 10.1109/ACCESS.2019.2895201
ISSN: 2169-3536
Links: https://ieeexplore.ieee.org/document/8626195
http://hdl.handle.net/2381/44527
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2019, Institute of Electrical and Electronics Engineers (IEEE). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Appears in Collections:Published Articles, Dept. of Engineering

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