Reed–Muller Codes for Random Erasures and Errors

This paper studies the parameters for which binary Reed-Muller (RM) codes can be decoded successfully on the binary erasure channel and binary symmetry channel, and, in particular, when can they achieve capacity for these two classical channels. Necessarily, this paper also studies the properties of evaluations of multivariate GF(2) polynomials on the random sets of inputs. For erasures, we prove that RM codes achieve capacity both for very high rate and very low rate regimes. For errors, we prove that RM codes achieve capacity for very low rate regimes, and for very high rates, we show that they can uniquely decode at about the square root of the number of errors at capacity. The proofs of these four results are based on different techniques, which we find interesting in their own right. In particular, we study the following questions about E(m, r), the matrix whose rows are the truth tables of all the monomials of degree ≤ r in m variables. What is the most (resp. least) number of random columns in E(m, r) that define a submatrix having full column rank (resp. full row rank) with high probability? We obtain tight bounds for very small (resp. very large) degrees r, which we use to show that RM codes achieve capacity for erasures in these regimes. Our decoding from random errors follows from the following novel reduction. For every linear code C of sufficiently high rate, we construct a new code C' obtained by tensorizing C, such that for every subset S of coordinates, if C can recover from erasures in S, then C' can recover from errors in S. Specializing this to the RM codes and using our results for erasures imply our result on the unique decoding of the RM codes at high rate. Finally, two of our capacity achieving results require tight bounds on the weight distribution of RM codes. We obtain such bounds extending the recent bounds from constant degree to linear degree polynomials.

[1]  E. Arkan,et al.  A performance comparison of polar codes and Reed-Muller codes , 2008, IEEE Communications Letters.

[2]  Emmanuel Abbe,et al.  Randomness and Dependencies Extraction via Polarization, With Applications to Slepian–Wolf Coding and Secrecy , 2011, IEEE Transactions on Information Theory.

[3]  Madhu Sudan,et al.  Optimal Testing of Reed-Muller Codes , 2009, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

[4]  Neil J. A. Sloane,et al.  The theory of error-correcting codes (north-holland , 1977 .

[5]  Gregory Poltyrev,et al.  Bounds on the decoding error probability of binary linear codes via their spectra , 1994, IEEE Trans. Inf. Theory.

[6]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[7]  Avi Wigderson,et al.  Reed-Muller Codes for Random Erasures and Errors , 2015, IEEE Trans. Inf. Theory.

[8]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[9]  Emanuele Viola,et al.  Pseudorandom Bits for Polynomials , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[10]  M. Kerimov The theory of error-correcting codes☆ , 1980 .

[11]  Rüdiger L. Urbanke,et al.  From polar to Reed-Muller codes: A technique to improve the finite-length performance , 2014, 2014 IEEE International Symposium on Information Theory.

[12]  Leonid A. Levin,et al.  Pseudo-random generation from one-way functions , 1989, STOC '89.

[13]  Shachar Lovett,et al.  Weight Distribution and List-Decoding Size of Reed–Muller Codes , 2012, IEEE Transactions on Information Theory.

[14]  Tadao Kasami,et al.  On the Weight Enumeration of Weights Less than 2.5d of Reed-Muller Codes , 1976, Inf. Control..

[15]  Simon Litsyn,et al.  Simple MAP decoding of first-order Reed-Muller and Hamming codes , 2004, IEEE Transactions on Information Theory.

[16]  Ilya Dumer,et al.  Soft-decision decoding of Reed-Muller codes: a simplified algorithm , 2006, IEEE Transactions on Information Theory.

[17]  O. Antoine,et al.  Theory of Error-correcting Codes , 2022 .

[18]  W. T. Gowers,et al.  Linear Forms and Higher-Degree Uniformity for Functions On $${\mathbb{F}^{n}_{p}}$$ , 2010, 1002.2208.

[19]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[20]  Daniel J. Costello,et al.  Channel coding: The road to channel capacity , 2006, Proceedings of the IEEE.

[21]  N. J. A. Sloane,et al.  Weight enumerator for second-order Reed-Muller codes , 1970, IEEE Trans. Inf. Theory.

[22]  Michael O. Rabin,et al.  Efficient dispersal of information for security, load balancing, and fault tolerance , 1989, JACM.

[23]  Emmanuel Abbe,et al.  Randomness and dependencies extraction via polarization , 2011, 2011 Information Theory and Applications Workshop.

[24]  Tadao Kasami,et al.  On the weight structure of Reed-Muller codes , 1970, IEEE Trans. Inf. Theory.

[25]  Aditya Bhaskara,et al.  Smoothed analysis of tensor decompositions , 2013, STOC.

[26]  Shachar Lovett,et al.  List decoding Reed-Muller codes over small fields , 2014, Electron. Colloquium Comput. Complex..

[27]  I. G. Núñez,et al.  Generalized Hamming Weights for Linear Codes , 2001 .

[28]  G. David Forney,et al.  Concatenated codes , 2009, Scholarpedia.

[29]  A. Razborov Lower bounds on the size of bounded depth circuits over a complete basis with logical addition , 1987 .

[30]  Lance Fortnow,et al.  Proceedings of the 55th Annual ACM Symposium on Theory of Computing , 2011, STOC.

[31]  Carsten Lund,et al.  Proof verification and hardness of approximation problems , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.

[32]  Prasad Raghavendra,et al.  Making the Long Code Shorter , 2012, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.

[33]  Joan Feigenbaum,et al.  Hiding Instances in Multioracle Queries , 1990, STACS.

[34]  Rocco A. Servedio,et al.  Explorer Efficient Density Estimation via Piecewise Polynomial Approximation , 2013 .

[35]  Ilya Dumer,et al.  Recursive error correction for general Reed-Muller codes , 2006, Discret. Appl. Math..

[36]  Leonid A. Levin,et al.  Pseudo-random Generation from one-way functions (Extended Abstracts) , 1989, STOC 1989.

[37]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[38]  Rüdiger L. Urbanke,et al.  Threshold Saturation via Spatial Coupling: Why Convolutional LDPC Ensembles Perform So Well over the BEC , 2010, IEEE Transactions on Information Theory.

[39]  William Gasarch A Survey on Private Information Retrieval , 2004 .

[40]  Erdal Arikan,et al.  Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels , 2008, IEEE Transactions on Information Theory.

[41]  R. Lathe Phd by thesis , 1988, Nature.

[42]  Irving S. Reed,et al.  A class of multiple-error-correcting codes and the decoding scheme , 1954, Trans. IRE Prof. Group Inf. Theory.

[43]  Daniel A. Spielman,et al.  Practical loss-resilient codes , 1997, STOC '97.

[44]  Noga Alon,et al.  The Probabilistic Method , 2015, Fundamentals of Ramsey Theory.

[45]  S. Litsyn,et al.  Simple MAP decoding of first order Reed-Muller and Hamming codes , 2002, The 22nd Convention on Electrical and Electronics Engineers in Israel, 2002..

[46]  Carsten Lund,et al.  Nondeterministic exponential time has two-prover interactive protocols , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[47]  Ilya Dumer,et al.  Recursive decoding and its performance for low-rate Reed-Muller codes , 2004, IEEE Transactions on Information Theory.

[48]  Seyed Hamed Hassani Polarization and Spatial Coupling - Two Techniques to Boost Performance , 2013 .

[49]  Adi Shamir,et al.  IP = PSPACE , 1992, JACM.

[50]  David E. Muller,et al.  Application of Boolean algebra to switching circuit design and to error detection , 1954, Trans. I R E Prof. Group Electron. Comput..

[51]  Tor Helleseth,et al.  Error-correction capability of binary linear codes , 2003, IEEE Transactions on Information Theory.

[52]  Robert M. Gray,et al.  Coding for noisy channels , 2011 .