@ARTICLE{Dong_Dhawa_Sang_Resource_2019,
author={Dong, Dhawa Sang and Pokhrel, Yagnya Murti and Gachhadar, Anand and Maharjan, Ram Krishna and Qamar, Faizan and Amiri, Iraj Sadegh},
volume={vol. 65},
number={No 3},
pages={463-469},
howpublished={online},
year={2019},
publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
abstract={Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.},
type={ArtykuĹ‚y / Articles},
title={Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks},
URL={http://www.czasopisma.pan.pl/Content/113304/PDF/62.pdf},
doi={10.24425/ijet.2019.129800},
keywords={Coding coefficients, computational complexity, lower triangular matrix, random network coding, sparse coding coefficients},
}