As a with-carry analog (based on modular arithmetic) of the usual Walsh-Hadamard transform (WHT), arithmetic Walsh transform (AWT) has been used to obtain analogs of some properties of Boolean functions which are important in the design and analysis of cryptosystems. The existence of nonzero linear structure of Boolean functions is an important criterion to measure the weakness of these functions in their cryptographic applications. In this paper, we find more analogs of linear structures of Boolean functions from AWT. For some classes of n-variable Boolean functions f, we find necessary and sufficient conditions for the existence of an invariant linear structure and a complementary linear structure 1n of f. We abstract out a sectionally linear relationship between AWT and WHT of n-variable balanced Boolean functions f with linear structure 1n. This result show that AWT can characterize cryptographic properties of these functions as long as WHT can. In addition, for a diagonal Boolean function f, a recent result by Carlet and Klapper says that the AWT of f can be expressed in terms of the AWT of a diagonal Boolean function of algebraic degree at most 3 in a larger number of variables. We provide for the result a complete and more modular proof which works for both even and odd weights (of the parameter c in the Corollary 19 by Carlet and Klapper (DCC 73(2): 299-318, 2014).

Given specified parameters, the number of check nodes, the expected girth and the variable node degrees, the Progressive Weight-Growth (PWG) algorithm is proposed to generate high rate low-density parity-check (LDPC) codes. Based on the theoretic foundation that is to investigate the girth impact by adding/removing variable nodes and edges of the Tanner graph, the PWG progressively increases column weights of the parity check matrix without violating the constraints defined by the given parameters. The analysis of the computational complexity and the simulation of code performance show that the LDPC codes by the PWG provide better or comparable performance in comparison with LDPC codes by some well-known methods (e.g., Mackay's random constructions, the PEG algorithm, and the bit-filling algorithm).