Performance and complexity of tunable sparse network coding with gradual growing tuning functions over wireless networks

TY - GEN

T1 - Performance and complexity of tunable sparse network coding with gradual growing tuning functions over wireless networks

AU - Garrido, Pablo

AU - W. Sørensen, Chres

AU - Lucani Rötter, Daniel Enrique

AU - Agüero, Ramon

N1 - Electronic ISSN: 2166-9589

PY - 2016/12/21

Y1 - 2016/12/21

N2 - Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages a trade-off between computational complexity and goodput. An optimal density tuning function has not been found yet, due to the lack of a closed-form expression that links density, performance and computational cost. In addition, it would be difficult to implement, due to the feedback delay. In this work we propose two novel tuning functions with a lower computational cost, which do not highly increase the overhead in terms of the transmission of linear dependent packets compared with RLNC and previous proposals. Furthermore, we also broaden previous studies of TSNC techniques, by means of an extensive simulation campaign carried out using the ns-3 simulator. This brings the possibility of assessing their performance over more realistic scenarios, e.g considering MAC effects and delays. We exploit this implementation to analyze the impact of the feedback sent by the decoder. The results, compared to RLNC, show a reduction of 3.5 times in the number of operations without jeopardizing the network performance, in terms of goodput, even when we consider the delay effect on the feedback sent by the decoder.

AB - Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages a trade-off between computational complexity and goodput. An optimal density tuning function has not been found yet, due to the lack of a closed-form expression that links density, performance and computational cost. In addition, it would be difficult to implement, due to the feedback delay. In this work we propose two novel tuning functions with a lower computational cost, which do not highly increase the overhead in terms of the transmission of linear dependent packets compared with RLNC and previous proposals. Furthermore, we also broaden previous studies of TSNC techniques, by means of an extensive simulation campaign carried out using the ns-3 simulator. This brings the possibility of assessing their performance over more realistic scenarios, e.g considering MAC effects and delays. We exploit this implementation to analyze the impact of the feedback sent by the decoder. The results, compared to RLNC, show a reduction of 3.5 times in the number of operations without jeopardizing the network performance, in terms of goodput, even when we consider the delay effect on the feedback sent by the decoder.

KW - Random Linear Coding

KW - Simulation

KW - Sparse Matrices

KW - TSNC

KW - Wireless Networks

UR - http://www.scopus.com/inward/record.url?scp=85010041752&partnerID=8YFLogxK

U2 - 10.1109/PIMRC.2016.7794915

DO - 10.1109/PIMRC.2016.7794915

M3 - Article in proceedings

SN - 978-1-5090-3254-9/16

BT - 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2016

T2 - IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

Y2 - 4 September 2016 through 8 September 2016

ER -