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 - Nailanathan, Arumugam
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
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.
UR - http://www.scopus.com/inward/record.url?scp=85072339803&partnerID=8YFLogxK
U2 - 10.1109/SPAWC.2019.8815399
DO - 10.1109/SPAWC.2019.8815399
M3 - Conference contribution
AN - SCOPUS:85072339803
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
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 -