Interleaved Block-Sparse Transform
Abstract
Low-complexity Bayes-optimal memory approximate message passing (MAMP) is an efficient signal estimation algorithm in compressed sensing and multicarrier modulation. However, achieving replica Bayes optimality with MAMP necessitates a large-scale right-unitarily invariant transformation, which is prohibitive in practical systems due to its high computational complexity and hardware costs. To solve this difficulty, this letter proposes a low-complexity interleaved block-sparse (IBS) transform, which consists of interleaved multiple low-dimensional transform matrices, aimed at reducing the hardware implementation scale while mitigating performance loss. Furthermore, an IBS cross-domain memory approximate message passing (IBS-CD-MAMP) estimator is developed, comprising a memory linear estimator in the IBS transform domain and a non-linear estimator in the source domain. Numerical results show that the IBS-CD-MAMP offers a reduced implementation scale and lower complexity with excellent performance in IBS-based compressed sensing and interleave frequency division multiplexing systems.
Cite
@article{arxiv.2407.13255,
title = {Interleaved Block-Sparse Transform},
author = {Lei Liu and Ming Wang and Shufeng Li and Yuhao Chi and Ning Wei and ZhaoYang Zhang},
journal= {arXiv preprint arXiv:2407.13255},
year = {2024}
}
Comments
Submitted to the IEEE Journal