Fast Approximate Construction of Best Complex Antipodal Spherical Codes

TL;DR

This paper introduces a faster algorithm for constructing Best Complex Antipodal Spherical Codes, which are optimal for compressive sensing measurement matrices, improving efficiency and performance in large-scale signal recovery tasks.

Contribution

It proposes a linear-complexity modification to an existing algorithm for generating BCASCs, enabling their use in large-scale compressive sensing applications.

Findings

The new algorithm maintains coherence quality of BCASCs.

BCASCs outperform random and Fourier matrices in sparse recovery.

Performance improves for large n and low m/n ratios.

Abstract

Compressive Sensing (CS) theory states that real-world signals can often be recovered from much fewer measurements than those suggested by the Shannon sampling theorem. Nevertheless, recoverability does not only depend on the signal, but also on the measurement scheme. The measurement matrix should behave as close as possible to an isometry for the signals of interest. Therefore the search for optimal CS measurement matrices of size $m\times n$ translates into the search for a set of $n$ $m$-dimensional vectors with minimal coherence. Best Complex Antipodal Spherical Codes (BCASCs) are known to be optimal in terms of coherence. An iterative algorithm for BCASC generation has been recently proposed that tightly approaches the theoretical lower bound on coherence. Unfortunately, the complexity of each iteration depends quadratically on $m$ and $n$. In this work we propose a modification of the algorithm that allows reducing the quadratic complexity to linear on both $m$ and $n$. Numerical evaluation showed that the proposed approach does not worsen the coherence of the resulting BCASCs. On the contrary, an improvement was observed for large $n$. The reduction of the computational complexity paves the way for using the BCASCs as CS measurement matrices in problems with large $n$. We evaluate the CS performance of the BCASCs for recovering sparse signals. The BCASCs are shown to outperform both complex random matrices and Fourier ensembles as CS measurement matrices, both in terms of coherence and sparse recovery performance, especially for low $m/n$, which is the case of interest in CS.