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

TL;DR

This paper proposes optimized linear coding strategies for relay drones to enhance reliable data delivery in remote data gathering, leveraging spark properties and partial packet recovery to improve decoding success rates.

Contribution

It introduces a spark optimization framework for linear codes that improves decoding probability in drone relay systems using RLC and PPR techniques.

Findings

Optimized codes increase decoding success probability.

Spark properties influence partial packet recovery effectiveness.

Proposed design outperforms existing RLC methods.

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.