Optimal downlink power allocation schemes for OFDM-NOMA-based Internet of things

Author:

Gao Ya12ORCID,Yu Fei13,Zhang Haoran13,Shi Yongpeng12ORCID,Xia Yujie12

Affiliation:

1. School of Physics and Electronic Information, Luoyang Normal University, Luoyang, China

2. Henan Key Laboratory for Big Data Processing and Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, China

3. School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, China

Abstract

With the continuous development of fifth-generation technology, the number of mobile terminal Internet of Things devices has increased exponentially. How to effectively improve the throughput of fifth-generation systems has become a challenge. In the Internet of Things networks, ultra-dense networks and non-orthogonal multiple access technology have drawn extensive attention in recent years, because they can achieve multiplexing from the space domain and power domain. To improve the throughput of the system, this article combines non-orthogonal multiple access with ultra-dense networks technology and considers the orthogonal frequency division multiplexing non-orthogonal multiple access–based ultra-dense networks with multiple base stations and multiple Internet of Things devices. In particular, first, we build the network model and channel model. Second, we construct the downlink transmission rate maximizing problem subject to the total power. Then, to solve this problem, we divide it into three sub-problems: device grouping, inter-sub-band power allocation, and intra-sub-band power allocation problems. Solving these sub-problems, we obtain the optimal power allocation schemes by jointly employing channel-state sorting–pairing algorithm, water-filling algorithm, and convex optimization theory. Finally, numerical simulations are conducted to validate the performance of our proposed optimal downlink power allocation scheme. Experimental results show that the total throughput of the system has been significantly improved.

Funder

natural science foundation of henan province

the Key Scientific Projects of Henan Higher Education Institutions

the Key Scientific and Technological Projects

the Foundation for Young Backbone Teachers in Higher Education Institutions

the Henan key Laboratory for Big Data Processing and Analytics of Electronic Commerce

Publisher

SAGE Publications

Subject

Computer Networks and Communications,General Engineering

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