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Spark optimization of linear codes for reliable data delivery by relay drones

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Spark optimization of linear codes for reliable data delivery by relay drones. / Chatzigeorgiou, Ioannis.
MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM). IEEE, 2021.

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

Harvard

Chatzigeorgiou, I 2021, Spark optimization of linear codes for reliable data delivery by relay drones. in MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM). IEEE, IEEE Military Communications Conference, San Diego, California, United States, 29/11/21. https://doi.org/10.1109/MILCOM52596.2021.9653053

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Chatzigeorgiou I. Spark optimization of linear codes for reliable data delivery by relay drones. In MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM). IEEE. 2021 Epub 2021 Nov 29. doi: 10.1109/MILCOM52596.2021.9653053

Author

Chatzigeorgiou, Ioannis. / Spark optimization of linear codes for reliable data delivery by relay drones. MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM). IEEE, 2021.

Bibtex

@inproceedings{2bddc698a0a64db786b8bbd2e3fb2db2,
title = "Spark optimization of linear codes for reliable data delivery by relay drones",
abstract = "Data gathering operations in remote locations often rely on relay drones, which collect, store and deliver transmitted information to a ground control station. The probability of the ground control station successfully reconstructing the gathered data can be increased if random linear coding (RLC) is used, especially when feedback channels between the drones and the transmitter are not available. RLC decoding can be complemented by partial packet recovery (PPR), which utilizes sparse recovery principles to repair erroneously received data before RLC decoding takes place. We explain that the spark of the transpose of the parity-check matrix of the linear code, that is, the smallest number of linearly-dependent columns of the matrix, influences the effectiveness of PPR. We formulate a spark optimization problem and obtain code designs that achieve a gain over PPR-assisted RLC, in terms of the probability that the ground control station will decode the delivered data.",
author = "Ioannis Chatzigeorgiou",
year = "2021",
month = dec,
day = "30",
doi = "10.1109/MILCOM52596.2021.9653053",
language = "English",
isbn = "9781665439725",
booktitle = "MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)",
publisher = "IEEE",
note = "IEEE Military Communications Conference, MILCOM ; Conference date: 29-11-2021 Through 02-12-2021",
url = "https://milcom2021.milcom.org/",

}

RIS

TY - GEN

T1 - Spark optimization of linear codes for reliable data delivery by relay drones

AU - Chatzigeorgiou, Ioannis

PY - 2021/12/30

Y1 - 2021/12/30

N2 - Data gathering operations in remote locations often rely on relay drones, which collect, store and deliver transmitted information to a ground control station. The probability of the ground control station successfully reconstructing the gathered data can be increased if random linear coding (RLC) is used, especially when feedback channels between the drones and the transmitter are not available. RLC decoding can be complemented by partial packet recovery (PPR), which utilizes sparse recovery principles to repair erroneously received data before RLC decoding takes place. We explain that the spark of the transpose of the parity-check matrix of the linear code, that is, the smallest number of linearly-dependent columns of the matrix, influences the effectiveness of PPR. We formulate a spark optimization problem and obtain code designs that achieve a gain over PPR-assisted RLC, in terms of the probability that the ground control station will decode the delivered data.

AB - Data gathering operations in remote locations often rely on relay drones, which collect, store and deliver transmitted information to a ground control station. The probability of the ground control station successfully reconstructing the gathered data can be increased if random linear coding (RLC) is used, especially when feedback channels between the drones and the transmitter are not available. RLC decoding can be complemented by partial packet recovery (PPR), which utilizes sparse recovery principles to repair erroneously received data before RLC decoding takes place. We explain that the spark of the transpose of the parity-check matrix of the linear code, that is, the smallest number of linearly-dependent columns of the matrix, influences the effectiveness of PPR. We formulate a spark optimization problem and obtain code designs that achieve a gain over PPR-assisted RLC, in terms of the probability that the ground control station will decode the delivered data.

U2 - 10.1109/MILCOM52596.2021.9653053

DO - 10.1109/MILCOM52596.2021.9653053

M3 - Conference contribution/Paper

SN - 9781665439725

BT - MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)

PB - IEEE

T2 - IEEE Military Communications Conference

Y2 - 29 November 2021 through 2 December 2021

ER -