We reinvestigate the general distributed secure source coding based on the common key cryptosystem proposed by Oohama and Santoso (ITW 2021). They proposed a framework of distributed source encryption and derived the necessary and sufficient conditions to have reliable and secure transmission. However, the bounds of the rate region, which specifies both necessary and sufficient conditions to have reliable and secure transmission under the proposed cryptosystem, were derived based on a self-tailored non-standard security criterion. In this paper we adopt the standard security criterion, i.e., standard mutual information. We successfully establish the bounds of the rate region based on this security criterion. Information spectrum method and a variant of Birkhoff-von Neumann theorem play important roles in deriving our result.
Recently, Huang and Zhang [1], [2] introduced the derivative as a fundamental structure of cyclic codes, based on which the derivative decoding was further presented. It is well known that some cyclic codes constructed based on finite geometries form a special class of low-density parity-check (LDPC) codes, which perform well under the sum-product algorithm. In this paper, we study the derivative structure of Euclidean geometry (EG) codes, with a special interest in EG-LDPC codes. It is proved that for µ = m −2, the derivative ascendant of the extended (µ,s)th-order EG code of length 2ms is the extended (µ+1,s)th-order twofold EG code of length 2ms. As a subcode of EG-LDPC code, the derivative descendant of the extend (1,s)th-order twofold EG code is characterized in terms of the roots of the generator polynomial. The new code relationship suggests that the twofold EG code can leverage the sum-product decoding of the derivative descendant for its derivative decoding.
In this paper, a new soft-decision decoding for extended Bose-Chaudhuri-Hocquenghem (eBCH) codes, referred to as CPC-SCL, is proposed, and it can achieve error-correction performance close to that of polarization-adjusted convolutional (PAC) codes in [1] when the code length n and dimension k are 128 and 64, respectively. Specifically, this paper first decomposes the eBCH code into a concatenated structure comprising an outer code and a Reed-Muller inner code. The outer code has a parity-check matrix characterized by a special block structure, which reveals that the positions of all frozen bits (including frozen zero bits and dynamic frozen bits) in the eBCH code are closely related to the index weights of elements from the perspective of the polar code. Subsequently, the CPC-SCL decoding of the eBCH codes is proposed by utilizing the cyclic property of codewords and parity-check-aided successive cancellation list (PC-SCL) decoding. Simulations also demonstrate that, over an additive white Gaussian noise (AWGN) channel with binary phase-shift keying (BPSK) modulation, the proposed decoding can achieve near maximum-likelihood (ML) performance at n = 64, k = 24 or 45.
Levenshtein’s sequence reconstruction model plays an essential role in information retrieval in DNA-based storage systems. In this model, a word ${\text{x}} \in {\mathbb{Z}}_q^n$ is transmitted through N noisy channels, and the goal is to recover the original word exactly, or with a small uncertainty ${\mathcal{L}}$, using the outputs from these channels. Errors occurring in the channels usually involve substitutions, insertions or deletions. In this paper, we focus on insertion errors, which we represent using (so-called) insertion vectors. One of the main questions in this context is determining the minimum number of channels N required to recover the word either unambiguously or within a given precision ${\mathcal{L}}$. The original formulation of Levenshtein’s reconstruction problem requires that all the outputs from the channels are distinct. However, different channels may produce the same output word even when different errors occur. In this paper, we investigate two generalized reconstruction models where the channels are allowed to produce the same output word as long as, in each channel, different errors occur (that is, the errors correspond to different insertion vectors). Our objective is to determine the number of channels N required to uniquely recover the transmitted word x under these conditions. We present several results in this direction, some of which are optimal.
This paper shows that projection-aggregation (PA) decoding of Reed-Muller (RM) codes can decode up to half the minimum distance d efficiently. By generalizing the false vote matrix from 1-dimensional to higher-dimensional subspaces, we prove that projecting onto disjoint subspaces, regardless of their dimensions, provides at most d/2 − 1 false votes for each codeword bit. Thus, d disjoint subspaces guarantee the correction for errors of d/2 − 1 or less. Moreover, the flexibility in subspace dimensions allows projecting into repetition codes directly, resulting in decoding efficiency. Finally, we prove that PA decoding with d disjoint subspaces decodes up to half the minimum distance in $O(n\sqrt n )$ for RM codes of length n and half rate or less.
