# Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications

arXiv: https://arxiv.org/abs/2304.11838 · HTML: https://mlsanity.com/paper/2304.11838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2023.

## Built on by (most cited first, 1 of 1)

- [In-Context Learning for Non-Stationary MIMO Equalization](https://mlsanity.com/paper/2510.08711/) (2025)

## Abstract (arXiv)

Sparse structures are widely recognized and utilized in channel estimation. Two typical mechanisms, namely proportionate updating (PU) and zero-attracting (ZA) techniques, achieve better performance, but their computational complexity are higher than non-sparse counterparts. In this paper, we propose a DCS technique based on the recursive least squares (RLS) algorithm which can simultaneously achieve improved performance and reduced computational complexity. Specifically, we develop the sparse adaptive subspace pursuit-improved RLS (SpAdSP-IRLS) algorithm by updating only the sparse structure in the IRLS to track significant coefficients. The complexity of the SpAdSP-IRLS algorithm is successfully reduced to O(L2+2L(s+1)+10s), compared with the order of O(3L2+4L) for the standard RLS. Here, L represents the length of the channel, and s represents the size of the support set. Our experiments on both synthetic and real data show the superiority of the proposed SpAdSP-IRLS, even though only s elements are updated in the channel estimation.