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  • Energy Efficient Resource Allocation for Uplink LoRa Networks

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Energy Efficient Resource Allocation for Uplink LoRa Networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Energy Efficient Resource Allocation for Uplink LoRa Networks. / Su, Binbin; Qin, Zhijin; Ni, Qiang.

2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 2018.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Su, B, Qin, Z & Ni, Q 2018, Energy Efficient Resource Allocation for Uplink LoRa Networks. in 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, IEEE Global Communications Conference 2018, Abu Dhabi, United Arab Emirates, 9/12/18. https://doi.org/10.1109/GLOCOM.2018.8647416

APA

Vancouver

Su B, Qin Z, Ni Q. Energy Efficient Resource Allocation for Uplink LoRa Networks. In 2018 IEEE Global Communications Conference (GLOBECOM). IEEE. 2018 doi: 10.1109/GLOCOM.2018.8647416

Author

Su, Binbin ; Qin, Zhijin ; Ni, Qiang. / Energy Efficient Resource Allocation for Uplink LoRa Networks. 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 2018.

Bibtex

@inproceedings{878bb276196a4adf9196a99f4e1e15e2,
title = "Energy Efficient Resource Allocation for Uplink LoRa Networks",
abstract = "In this paper, we investigate energy efficiency for uplink LoRa, which is one of the most promising and widely deployed low-power wide-area (LPWA) networks. In the considered networks, we explore user scheduling, spreading factor (SF) assignment, and power allocation jointly. A nonconvex optimization problem for maximizing the system energy efficiency is formulated, with targeted SNR requirement and power range as constraints for each LoRa user. To solve this problem, we first propose a low-complexity suboptimal algorithm, which includes energy-efficient user scheduling and heuristic SF assignment for scheduled users based on matching theory. Then a novel power allocation based on Charnes-Cooper transformation is proposed to transform the fractional objective into the convex form to maximize the system energy efficiency. Simulation results show that the proposed algorithms achieve near-optimal performance in terms of energy efficiency.",
author = "Binbin Su and Zhijin Qin and Qiang Ni",
note = "{\textcopyright}2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; IEEE Global Communications Conference 2018, IEEE GLOBECOM 2018 ; Conference date: 09-12-2018 Through 13-12-2018",
year = "2018",
month = dec,
day = "9",
doi = "10.1109/GLOCOM.2018.8647416",
language = "English",
booktitle = "2018 IEEE Global Communications Conference (GLOBECOM)",
publisher = "IEEE",
url = "http://globecom2018.ieee-globecom.org/",

}

RIS

TY - GEN

T1 - Energy Efficient Resource Allocation for Uplink LoRa Networks

AU - Su, Binbin

AU - Qin, Zhijin

AU - Ni, Qiang

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/12/9

Y1 - 2018/12/9

N2 - In this paper, we investigate energy efficiency for uplink LoRa, which is one of the most promising and widely deployed low-power wide-area (LPWA) networks. In the considered networks, we explore user scheduling, spreading factor (SF) assignment, and power allocation jointly. A nonconvex optimization problem for maximizing the system energy efficiency is formulated, with targeted SNR requirement and power range as constraints for each LoRa user. To solve this problem, we first propose a low-complexity suboptimal algorithm, which includes energy-efficient user scheduling and heuristic SF assignment for scheduled users based on matching theory. Then a novel power allocation based on Charnes-Cooper transformation is proposed to transform the fractional objective into the convex form to maximize the system energy efficiency. Simulation results show that the proposed algorithms achieve near-optimal performance in terms of energy efficiency.

AB - In this paper, we investigate energy efficiency for uplink LoRa, which is one of the most promising and widely deployed low-power wide-area (LPWA) networks. In the considered networks, we explore user scheduling, spreading factor (SF) assignment, and power allocation jointly. A nonconvex optimization problem for maximizing the system energy efficiency is formulated, with targeted SNR requirement and power range as constraints for each LoRa user. To solve this problem, we first propose a low-complexity suboptimal algorithm, which includes energy-efficient user scheduling and heuristic SF assignment for scheduled users based on matching theory. Then a novel power allocation based on Charnes-Cooper transformation is proposed to transform the fractional objective into the convex form to maximize the system energy efficiency. Simulation results show that the proposed algorithms achieve near-optimal performance in terms of energy efficiency.

U2 - 10.1109/GLOCOM.2018.8647416

DO - 10.1109/GLOCOM.2018.8647416

M3 - Conference contribution/Paper

BT - 2018 IEEE Global Communications Conference (GLOBECOM)

PB - IEEE

T2 - IEEE Global Communications Conference 2018

Y2 - 9 December 2018 through 13 December 2018

ER -