Channel Estimation for Underwater Visible Light Communication: A Sparse
Learning Perspective
- URL: http://arxiv.org/abs/2303.07248v1
- Date: Mon, 13 Mar 2023 16:22:16 GMT
- Title: Channel Estimation for Underwater Visible Light Communication: A Sparse
Learning Perspective
- Authors: Younan Mou, Sicong Liu
- Abstract summary: This paper proposes a sparse learning based underwater visible light channel estimation (SL-UVCE) scheme.
Specifically, a deep-unfolding neural network mimicking the classical iterative sparse recovery algorithm of approximate message passing (AMP) is employed.
Compared with the existing non-CS-based and CS-based schemes, the proposed scheme shows better performance of accuracy in channel estimation.
- Score: 1.7830921962643287
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The underwater propagation environment for visible light signals is affected
by complex factors such as absorption, shadowing, and reflection, making it
very challengeable to achieve effective underwater visible light communication
(UVLC) channel estimation. It is difficult for the UVLC channel to be sparse
represented in the time and frequency domains, which limits the chance of using
sparse signal processing techniques to achieve better performance of channel
estimation. To this end, a compressed sensing (CS) based framework is
established in this paper by fully exploiting the sparsity of the underwater
visible light channel in the distance domain of the propagation links. In order
to solve the sparse recovery problem and achieve more accurate UVLC channel
estimation, a sparse learning based underwater visible light channel estimation
(SL-UVCE) scheme is proposed. Specifically, a deep-unfolding neural network
mimicking the classical iterative sparse recovery algorithm of approximate
message passing (AMP) is employed, which decomposes the iterations of AMP into
a series of layers with different learnable parameters. Compared with the
existing non-CS-based and CS-based schemes, the proposed scheme shows better
performance of accuracy in channel estimation, especially in severe conditions
such as insufficient measurement pilots and large number of multipath
components.
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