Optimal Error Rates for Interactive Coding II: Efficiency and List Decoding

We study coding schemes for error correction in interactive communications. Such interactive coding schemes simulate any n-round interactive protocol using N rounds over an adversarial channel that corrupts up to ρN transmissions. Important performance measures for a coding scheme are its maximum tolerable error rate ρ, communication complexity N, and computational complexity. We give the first coding scheme for the standard setting which performs optimally in all three measures: Our randomized non-adaptive coding scheme has a near-linear computational complexity and tolerates any error rate δ <; 1/4 with a linear N = Θ(n) communication complexity. This improves over prior results [1]-[4] which each performed well in two of these measures. We also give results for other settings of interest, namely, the first computationally and communication efficient schemes that tolerate ρ <; 2/7 adaptively, ρ <; 1/3 if only one party is required to decode, and ρ <; 1/2 if list decoding is allowed. These are the optimal tolerable error rates for the respective settings. These coding schemes also have near linear computational and communication complexity. These results are obtained via two techniques: We give a general black-box reduction which reduces unique decoding, in various settings, to list decoding. We also show how to boost the computational and communication efficiency of any list decoder to become near linear1.

[1]  Madhu Sudan,et al.  Optimal error rates for interactive coding I: adaptivity and other settings , 2013, STOC.

[2]  Oded Goldreich,et al.  A Sample of Samplers - A Computational Perspective on Sampling (survey) , 1997, Electron. Colloquium Comput. Complex..

[3]  Oded Goldreich,et al.  Foundations of Cryptography: Volume 1, Basic Tools , 2001 .

[4]  Mark Braverman,et al.  List and Unique Coding for Interactive Communication in the Presence of Adversarial Noise , 2014, FOCS.

[5]  Venkatesan Guruswami,et al.  List decoding of error correcting codes , 2001 .

[6]  Leonard J. Schulman Coding for interactive communication , 1996, IEEE Trans. Inf. Theory.

[7]  Yael Tauman Kalai,et al.  Efficient Interactive Coding against Adversarial Noise , 2012, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.

[8]  Moni Naor,et al.  Fast Algorithms for Interactive Coding , 2013, SODA.

[9]  Rafail Ostrovsky,et al.  Optimal Coding for Streaming Authentication and Interactive Communication , 2015, IEEE Transactions on Information Theory.

[10]  Ran Raz,et al.  Interactive channel capacity , 2013, STOC '13.

[11]  Amit Sahai,et al.  Adaptive protocols for interactive communication , 2013, 2016 IEEE International Symposium on Information Theory (ISIT).

[12]  Bernhard Haeupler,et al.  Interactive Channel Capacity Revisited , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.

[13]  Peter Elias,et al.  List decoding for noisy channels , 1957 .

[14]  Daniel A. Spielman,et al.  Linear-time encodable and decodable error-correcting codes , 1995, STOC '95.

[15]  Amit Sahai,et al.  Efficient and Explicit Coding for Interactive Communication , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.

[16]  Daniel A. Spielman Linear-time encodable and decodable error-correcting codes , 1996, IEEE Trans. Inf. Theory.

[17]  Mark Braverman,et al.  Towards deterministic tree code constructions , 2012, ITCS '12.

[18]  Madhu Sudan,et al.  Decoding of Reed Solomon Codes beyond the Error-Correction Bound , 1997, J. Complex..

[19]  Mark Braverman,et al.  Toward Coding for Maximum Errors in Interactive Communication , 2011, IEEE Transactions on Information Theory.