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Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network

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Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network. / Zheng, Guhan; Ni, Qiang; Navaie, Keivan et al.
2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022). Institute of Electrical and Electronics Engineers Inc., 2022. p. 307-311.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Zheng, G, Ni, Q, Navaie, K, Pervaiz, H & Zarakovitis, C 2022, Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network. in 2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022). Institute of Electrical and Electronics Engineers Inc., pp. 307-311. https://doi.org/10.1109/ICCWorkshops53468.2022.9814494

APA

Zheng, G., Ni, Q., Navaie, K., Pervaiz, H., & Zarakovitis, C. (2022). Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network. In 2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022) (pp. 307-311). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCWorkshops53468.2022.9814494

Vancouver

Zheng G, Ni Q, Navaie K, Pervaiz H, Zarakovitis C. Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network. In 2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022). Institute of Electrical and Electronics Engineers Inc. 2022. p. 307-311 doi: 10.1109/ICCWorkshops53468.2022.9814494

Author

Zheng, Guhan ; Ni, Qiang ; Navaie, Keivan et al. / Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network. 2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022). Institute of Electrical and Electronics Engineers Inc., 2022. pp. 307-311

Bibtex

@inproceedings{a14cadd308034bc3bd479fd3703887be,
title = "Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network",
abstract = "The recent advances in low earth orbit (LEO) satellite-borne edge cloud (SEC) enable resource-limited users to access edge servers via a terrestrial station terminal (TST) for rapid task processing capability. However the dynamic variation in the TST transmit power challenges the served users to develop optimal computing task processing decisions. In this paper we propose an efficient pruning-split long short-term memory (LSTM) learning algorithm to address this challenge. First we present an LSTM algorithm for TST transmit power prediction. The proposed algorithm is then pruned and split to decrease the computing workload and the communication resource consumption considering the limited computing resource of TSTs and served users' quality of service (QoS). Finally an algorithm split layer selection method is introduced based on the real-time situation of the TST. The simulation results are shown to verify the effectiveness of the proposed pruning-split LSTM algorithm.",
author = "Guhan Zheng and Qiang Ni and Keivan Navaie and Haris Pervaiz and Charilaos Zarakovitis",
year = "2022",
month = may,
day = "16",
doi = "10.1109/ICCWorkshops53468.2022.9814494",
language = "English",
isbn = "9781665426725",
pages = "307--311",
booktitle = "2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - GEN

T1 - Efficient Pruning-Split LSTM Machine Learning Algorithm for Terrestrial-Satellite Edge Network

AU - Zheng, Guhan

AU - Ni, Qiang

AU - Navaie, Keivan

AU - Pervaiz, Haris

AU - Zarakovitis, Charilaos

PY - 2022/5/16

Y1 - 2022/5/16

N2 - The recent advances in low earth orbit (LEO) satellite-borne edge cloud (SEC) enable resource-limited users to access edge servers via a terrestrial station terminal (TST) for rapid task processing capability. However the dynamic variation in the TST transmit power challenges the served users to develop optimal computing task processing decisions. In this paper we propose an efficient pruning-split long short-term memory (LSTM) learning algorithm to address this challenge. First we present an LSTM algorithm for TST transmit power prediction. The proposed algorithm is then pruned and split to decrease the computing workload and the communication resource consumption considering the limited computing resource of TSTs and served users' quality of service (QoS). Finally an algorithm split layer selection method is introduced based on the real-time situation of the TST. The simulation results are shown to verify the effectiveness of the proposed pruning-split LSTM algorithm.

AB - The recent advances in low earth orbit (LEO) satellite-borne edge cloud (SEC) enable resource-limited users to access edge servers via a terrestrial station terminal (TST) for rapid task processing capability. However the dynamic variation in the TST transmit power challenges the served users to develop optimal computing task processing decisions. In this paper we propose an efficient pruning-split long short-term memory (LSTM) learning algorithm to address this challenge. First we present an LSTM algorithm for TST transmit power prediction. The proposed algorithm is then pruned and split to decrease the computing workload and the communication resource consumption considering the limited computing resource of TSTs and served users' quality of service (QoS). Finally an algorithm split layer selection method is introduced based on the real-time situation of the TST. The simulation results are shown to verify the effectiveness of the proposed pruning-split LSTM algorithm.

U2 - 10.1109/ICCWorkshops53468.2022.9814494

DO - 10.1109/ICCWorkshops53468.2022.9814494

M3 - Conference contribution/Paper

SN - 9781665426725

SP - 307

EP - 311

BT - 2022 IEEE International Conference on Communications Workshops (ICC Workshops 2022)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -