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Sparse Degrees Analysis for LT Codes Optimization

Sparse Degrees Analysis for LT Codes Optimization. Pei- Chuan Tsai Chih -Ming Chen Ying-ping Chen WCCI 2012 IEEE World Congress on Computational Intelligence. Outline . Introduction Probability reallocation Selection function for sparse tags Experiment result. Introduction.

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Sparse Degrees Analysis for LT Codes Optimization

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  1. Sparse Degrees Analysis for LT Codes Optimization Pei-Chuan Tsai Chih-Ming Chen Ying-ping Chen WCCI 2012 IEEE World Congress on Computational Intelligence

  2. Outline • Introduction • Probability reallocation • Selection function for sparse tags • Experiment result

  3. Introduction • Digit fountain codes : • The performance is independent of channel parameters, such as the erasure rate. • Delivering data through an unclear channel • Broadcasting information to many users whose connection qualities are unequal. • LT codes: • The performance depends on the size of bit nodes, k, and a given degree distribution.

  4. [4] E. A. Bodine and M. K. Cheng, “Characterization of LubyTransform codes with small message size for low-latency decoding,” in Proceedings of the IEEE International Conference on Communications, 2008, pp.1195–1199. [5] E. Hyytia, T. Tirronen, and J. Virtamo, “Optimal degree distribution for LT codes with small message length,” in Proceedings of the 26th IEEE International Conference on Computer Communications (INFOCOM 2007), 2007, pp. 2576–2580. Introduction • The LT codes problem : • Can be modeled as an optimization problem • Decision variable : the probability on each degree • The objective : searching for better degree distributions • [4],[5] are focus on short data length • The optimization scale is limited to k ≤ 30 • For large k size : • [6] first applying heuristic search algorithms • [7] , [8] introducing evolutionary algorithms • Several distributions better than solitondistributions were obtained [6] “Optimizing the degree distribution of LT codes with an importance sampling approach,” in Proceedings of the 6th InternationalWorkshop on Rare Event Simulation (RESIM 2006), 2006, pp. 64–73. [7] C.-M. Chen, Y.-p. Chen, T.-C. Shen, and J. K. Zao, “On the optimization of degree distributions in LT code with covariance matrix adaptation evolution strategy,” in Proceedings of the IEEE Congress on Evolutionary Computation, 2010, pp. 3531–3538. [8] “Optimizing degree distributions in LT codes by using the multiobjective evolutionary algorithm based on decomposition,” in Proceedings of the IEEE Congress on Evolutionary Computation, 2010, pp. 3635–3642.

  5. Introduction • Evolutionary algorithms is feasible to optimize degree distributions • The challenge : huge search space • The problem dimensionality is equal to the source data length k • A sparse degree distribution is an alternative solution • A set of tags : predefined partial degrees • Previously, the tags were usually decided according to experimental experience. • In this paper • Employing evolutionary algorithms as a research tool to choose tags • Observing the influence of different tags on decoding error probability of LT codes.

  6. Probability reallocation • We conduct experiments to observe the effects of the probability reallocation on the degree distribution. • Using CMA-ES to obtain optimal degree distributions which guarantee the minimal error probability • The results illustrate two factors of error probability variances : • The distance between the reallocated and the removed degrees • The complementary property of the reallocated degrees • i=4 , (2,5) is better than (1,7) • (1,6) is better than (1,2) (3,5) is best reallocate the probability of degree i to degrees a and b

  7. Selection function for sparse tags • The main considerations of our degree selection strategy for sparse tags : • The number and value of probabilities around each degree. • The density parameter d acts as the bound • We only group the degrees with probabilities below 1/d and concentrate those probabilities to a nearby degree . • The distance between the probability reallocated degrees and the removed one. • We accumulate the probabilities multiplied the distance factors and take the degree while the sum exceeds the bound 1/d. • We reserve the degrees 1 and k to ensure there always exists degrees

  8. Selection function for sparse tags

  9. Experiment result • For each (k, d) pair : • Using the TSF function to determine the corresponding tags • Applying the CMA-ES algorithm to search for the optimal sparse degree distribution with these tags • Comparing the minimal error probabilities of the sparse degree distribution and the full degree distribution

  10. Experiment result

  11. Experiment result

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