Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Pliatsios, Dimitrios; Sarigiannidis, Panagiotis

doi:10.3390/info10110336

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Dimitrios Pliatsios

and

Panagiotis Sarigiannidis

Department of Electrical and Computer Engineering, University of Western Macedonia, Karamanli & Ligeris Str., 50131 Kozani, Greece

Author to whom correspondence should be addressed.

Information 2019, 10(11), 336; https://doi.org/10.3390/info10110336

Received: 1 October 2019 / Revised: 28 October 2019 / Accepted: 28 October 2019 / Published: 30 October 2019

(This article belongs to the Special Issue IoT Applications and Industry 4.0)

View Full-Text Download PDF

Browse Figures

Review Reports Citation Export

Abstract

The ever-increasing requirement of massive connectivity, due to the rapid deployment of internet of things (IoT) devices, in the emerging 5th generation (5G) mobile networks commands for even higher utilization of the available spectrum. Non-orthogonal multiple access (NOMA) is a promising solution that can effectively accommodate a higher number of users, resulting in increased spectrum utilization. In this work, we aim to maximize the total throughput of a NOMA system, while maintaining a good level of fairness among the users. We propose a three-step method where the first step matches the users to the channels using a heuristic matching algorithm, while the second step utilizes the particle swarm optimization algorithm to allocate the power to each channel. In the third step, the power allocated to each channel is further distributed to the multiplexed users based on their respective channel gains. Based on extensive performance simulations, the proposed method offers notable improvement, e.g., 15% in terms of system throughput and 55% in terms of user fairness. View Full-Text

Keywords: 5G; heuristic optimization; non-orthogonal multiple access; resource allocation

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

MDPI and ACS Style

Pliatsios, D.; Sarigiannidis, P. Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access. Information 2019, 10, 336. https://doi.org/10.3390/info10110336

AMA Style

Pliatsios D, Sarigiannidis P. Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access. Information. 2019; 10(11):336. https://doi.org/10.3390/info10110336

Chicago/Turabian Style

Pliatsios, Dimitrios, and Panagiotis Sarigiannidis. 2019. "Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access" Information 10, no. 11: 336. https://doi.org/10.3390/info10110336

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI