Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks

Jingjing Cui; Mohammad Bariq Khan; Yansha Deng; Zhiguo Ding; Arumugam Nallanathan

doi:10.1109/SPAWC.2019.8815399

Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks

Jingjing Cui, Mohammad Bariq Khan, Yansha Deng, Zhiguo Ding, Arumugam Nallanathan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

This paper studies the application of simultaneous wireless information and power transfer (SWIPT) to millimeterwave non-orthogonal multiple access (mmWave-NOMA) enabled aerial networks, where an aerial base station (ABS) sends wireless information and energy simultaneously via NOMA schemes to multiple single-antenna information decoding (ID) devices and energy harvesting (EH) devices. This paper aims to maximize the harvested sum-power of all EH devices subject to given minimum rate constraints at different ID devices. Furthermore, we develop two machine learning based clustering algorithms, namely, K-means and K-medoids, where devices' locations are extracted to model the features for clustering. Our simulation results demonstrate: 1) the impact of different clustering approaches on the sum EH power under different spatial distributions of devices; 2) the proposed machine learning based clustering framework for mmWave-NOMA enabled aerial SWIPT networks is capable of achieving considerate improvements in terms of the harvested energy compared to conventional aerial SWIPT networks.

Original language	English
Title of host publication	2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2019-July
ISBN (Electronic)	9781538665282
DOIs	https://doi.org/10.1109/SPAWC.2019.8815399
Publication status	Published - 1 Jul 2019
Event	20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 - Cannes, France Duration: 2 Jul 2019 → 5 Jul 2019

Publication series

Name	2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019)
ISSN (Print)	2325-3789

Conference

Conference	20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Country/Territory	France
City	Cannes
Period	2/07/2019 → 5/07/2019

Access to Document

10.1109/SPAWC.2019.8815399

Cite this

Cui, J., Khan, M. B., Deng, Y., Ding, Z., & Nallanathan, A. (2019). Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks. In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 (Vol. 2019-July). Article 8815399 (2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2019.8815399

Cui, Jingjing ; Khan, Mohammad Bariq ; Deng, Yansha et al. / Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks. 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Vol. 2019-July Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019)).

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title = "Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks",

abstract = "This paper studies the application of simultaneous wireless information and power transfer (SWIPT) to millimeterwave non-orthogonal multiple access (mmWave-NOMA) enabled aerial networks, where an aerial base station (ABS) sends wireless information and energy simultaneously via NOMA schemes to multiple single-antenna information decoding (ID) devices and energy harvesting (EH) devices. This paper aims to maximize the harvested sum-power of all EH devices subject to given minimum rate constraints at different ID devices. Furthermore, we develop two machine learning based clustering algorithms, namely, K-means and K-medoids, where devices' locations are extracted to model the features for clustering. Our simulation results demonstrate: 1) the impact of different clustering approaches on the sum EH power under different spatial distributions of devices; 2) the proposed machine learning based clustering framework for mmWave-NOMA enabled aerial SWIPT networks is capable of achieving considerate improvements in terms of the harvested energy compared to conventional aerial SWIPT networks.",

author = "Jingjing Cui and Khan, {Mohammad Bariq} and Yansha Deng and Zhiguo Ding and Arumugam Nallanathan",

year = "2019",

month = jul,

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doi = "10.1109/SPAWC.2019.8815399",

language = "English",

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Cui, J, Khan, MB, Deng, Y, Ding, Z & Nallanathan, A 2019, Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks. in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. vol. 2019-July, 8815399, 2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019), Institute of Electrical and Electronics Engineers Inc., 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019, Cannes, France, 2/07/2019. https://doi.org/10.1109/SPAWC.2019.8815399

Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks. / Cui, Jingjing; Khan, Mohammad Bariq; Deng, Yansha et al.
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Vol. 2019-July Institute of Electrical and Electronics Engineers Inc., 2019. 8815399 (2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks

AU - Cui, Jingjing

AU - Khan, Mohammad Bariq

AU - Deng, Yansha

AU - Ding, Zhiguo

AU - Nallanathan, Arumugam

PY - 2019/7/1

Y1 - 2019/7/1

N2 - This paper studies the application of simultaneous wireless information and power transfer (SWIPT) to millimeterwave non-orthogonal multiple access (mmWave-NOMA) enabled aerial networks, where an aerial base station (ABS) sends wireless information and energy simultaneously via NOMA schemes to multiple single-antenna information decoding (ID) devices and energy harvesting (EH) devices. This paper aims to maximize the harvested sum-power of all EH devices subject to given minimum rate constraints at different ID devices. Furthermore, we develop two machine learning based clustering algorithms, namely, K-means and K-medoids, where devices' locations are extracted to model the features for clustering. Our simulation results demonstrate: 1) the impact of different clustering approaches on the sum EH power under different spatial distributions of devices; 2) the proposed machine learning based clustering framework for mmWave-NOMA enabled aerial SWIPT networks is capable of achieving considerate improvements in terms of the harvested energy compared to conventional aerial SWIPT networks.

AB - This paper studies the application of simultaneous wireless information and power transfer (SWIPT) to millimeterwave non-orthogonal multiple access (mmWave-NOMA) enabled aerial networks, where an aerial base station (ABS) sends wireless information and energy simultaneously via NOMA schemes to multiple single-antenna information decoding (ID) devices and energy harvesting (EH) devices. This paper aims to maximize the harvested sum-power of all EH devices subject to given minimum rate constraints at different ID devices. Furthermore, we develop two machine learning based clustering algorithms, namely, K-means and K-medoids, where devices' locations are extracted to model the features for clustering. Our simulation results demonstrate: 1) the impact of different clustering approaches on the sum EH power under different spatial distributions of devices; 2) the proposed machine learning based clustering framework for mmWave-NOMA enabled aerial SWIPT networks is capable of achieving considerate improvements in terms of the harvested energy compared to conventional aerial SWIPT networks.

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M3 - Conference contribution

AN - SCOPUS:85072339803

VL - 2019-July

T3 - 2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019)

BT - 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019

Y2 - 2 July 2019 through 5 July 2019

ER -

Cui J, Khan MB, Deng Y, Ding Z, Nallanathan A. Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks. In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Vol. 2019-July. Institute of Electrical and Electronics Engineers Inc. 2019. 8815399. (2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019)). doi: 10.1109/SPAWC.2019.8815399

Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks

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