An optimized framework for degree distribution in LT codes based on power law

Asim Muhammad , Choi GoangSeog

Journal of Central South University ›› 2013, Vol. 20 ›› Issue (10) : 2693 -2699.

PDF
Journal of Central South University ›› 2013, Vol. 20 ›› Issue (10) : 2693 -2699. DOI: 10.1007/s11771-013-1785-3
Article

An optimized framework for degree distribution in LT codes based on power law

Author information +
History +
PDF

Abstract

LT codes are practical realization of digital fountain codes, which provides the concept of rateless coding. In this scheme, encoded symbols are generated infinitely from k information symbols. Decoder uses only (1+α)k number of encoded symbols to recover the original information. The degree distribution function in the LT codes helps to generate a random graph also referred as tanner graph. The artifact of tanner graph is responsible for computational complexity and overhead in the LT codes. Intuitively, a well designed degree distribution can be used for an efficient implementation of LT codes. The degree distribution function is studied as a function of power law, and LT codes are classified into two different categories: SFLT and RLT codes. Also, two different degree distributions are proposed and analyzed for SFLT codes which guarantee optimal performance in terms of computational complexity and overhead.

Keywords

fountain codes / degree distribution / overhead / computational complexity / power law

Cite this article

Download citation ▾
Asim Muhammad, Choi GoangSeog. An optimized framework for degree distribution in LT codes based on power law. Journal of Central South University, 2013, 20(10): 2693-2699 DOI:10.1007/s11771-013-1785-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

ByersJ, LubyM, MitzenmacherM, RegeA. A digital fountain approach to reliable distribution of bulk data [C]. Proceeding of ACM SIGCOMM, 199855-67

[2]

LubyM. LT Codes [C]. Proceedings of the 43rd Annual IEEE Symposium on the Foundations of Computer Science. Vancouver, Canada, 2002271-280

[3]

TarusH, BushJ, IrvineJ, DunlopJ. Exploiting redundancies to improve performance of LT decoding [C]. Proceeding of the 6th Annual Conference on Communication Networks and Services Research (CNSR 2008), 2008568-573

[4]

HyytiäE, TirronenT, VirtamoJ. Optimizing the degree distribution of LT codes with an importance sampling approach [C]. 6th International Workshop on Rare Event Simulation, 200664-73

[5]

ZhuH, ZhangG, LiG. A novel degree distribution algorithm of LT codes [C]. the 11th IEEE International Conference on Communication Technology, 2008

[6]

SørensenJ H, PopovskiP, ØstergaardJ. Design and analysis of LT codes with decreasing ripple size [J]. IEEE Transactions on Communication, 2012, 60(11): 3191-3197

[7]

ChenC M, ChenY P, ShenT C, ZaoJ K. Optimizing the degree distributions in LT codes by using the multi-objective evolutionary algorithm based on decomposition [C]. Proceeding of the IEEE Congress on Evolutionary Computation. Barcelona, 20103635-3642

[8]

ChenC M, ChenY P, ShenT C, ZaoJ KA practical optimization frame for the degree distribution in LT codes [R], 2011

[9]

BarabasiA L, DezsoZ, RavaszE, YookS H, OltvaiZ. Scale free and hierarchical structures in complex networks [C]. American Institute of Physics (AIP) Conference Proceedings. Granada, 20021-16

[10]

NewmanM E J. Power laws pareto distributions and Zipf’s law [J]. Contemporary Physics, 2005, 46(5): 323-351

[11]

LeiM, ZhaoO-g, HouZ-ting. Three vertex degree correlations of fixed act-size collaboration networks [J]. Journal of Central South University, 2011, 18(3): 830-833

[12]

StockhammerT, JenkacH, MayerT, XuW. Soft decoding of LT codes for wireless broadcast [C]. Proceeding of IST Mobile. Dresden, Germany, 2005262-264

[13]

UyedaF, XiaH, ChenA AEvaluation of a high performance erasure code implementation [R], 2004San DiegoUniversity of California

AI Summary AI Mindmap
PDF

110

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/