Sparse recovery guarantees for block orthogonal binary matrices constructed via Generalized Euler Squares

TL;DR

This paper introduces a new class of deterministic binary matrices constructed via Generalized Euler Squares, which have low block coherence and orthogonal blocks, enhancing sparse signal recovery capabilities.

Contribution

It generalizes Euler Squares to create binary matrices with better aspect ratios and block orthogonality, improving deterministic sparse recovery methods.

Findings

Matrices exhibit low block coherence

Matrices have orthogonal blocks

Supports recovery of block sparse signals

Abstract

In recent times, the construction of deterministic matrices has gained popularity as an alternative of random matrices as they provide guarantees for recovery of sparse signals. In particular, the construction of binary matrices has attained significance due to their potential for hardware-friendly implementation and appealing applications. Our present work aims at constructing incoherent binary matrices consisting of orthogonal blocks with small block coherence. We show that the binary matrices constructed from Euler squares exhibit block orthogonality and possess low block coherence. With a goal of obtaining better aspect ratios, the present work generalizes the notion of Euler Squares and obtains a new class of deterministic binary matrices of more general size. For realizing the stated objectives, to begin with, the paper revisits the connection of finite field theory to Euler Squares and their construction. Using the stated connection, the work proposes Generalized Euler Squares (GES) and then presents a construction procedure. Binary matrices with low coherence and general row-sizes are obtained, whose column size is in the maximum possible order. Finally, the paper shows that the special structure possessed by GES is helpful in resulting in block orthogonal structure with small block coherence, which supports the recovery of block sparse signals.