Spark Optimization of Linear Codes for Reliable Data Delivery by Relay Drones

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) i...

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Bibliographic Details
Published in:MILCOM IEEE Military Communications Conference pp. 1 - 6
Main Author: Chatzigeorgiou, Ioannis
Format: Conference Proceeding
Language:English
Published: IEEE 29.11.2021
Subjects:
compressed sensing
Decoding
Encoding
erasure channel
Linear codes
partial packet recovery
Random linear coding
Reliability
spark
Sparks
Sparse matrices
syndrome decoding
Transmitters
ISSN:2155-7586
Online Access:Get full text
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Summary: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.
ISSN:2155-7586
DOI:10.1109/MILCOM52596.2021.9653053