Skip to main content
Kent Academic Repository

Joint Constellation-Labeling Optimization for VLC-CSK Systems

Jia, Linqiong, Shu, Feng, Chen, Ming, Zhang, Weibin, Li, Jun, Wang, Jiangzhou (2019) Joint Constellation-Labeling Optimization for VLC-CSK Systems. IEEE Wireless Communications Letters, 8 (4). pp. 1280-1284. ISSN 2162-2337. (doi:10.1109/LWC.2019.2916336) (KAR id:77552)

Abstract

In this letter, we have optimized the joint constellation-labeling for a finite number of signals in visible light communication (VLC) systems with color-shift keying (CSK) modulation by maximizing the pragmatic mutual information (PMI). First, an equivalent parallel channel model of the VLC-CSK system is constructed. Then the PMI is derived to be the objective function of the newly formulated joint constellation-labeling optimization problem of the VLC-CSK system. Simulated annealing (SA) algorithm and modified interior point method (MIPM) are adopted to solve the joint constellation-labeling optimization problem. Simulation results show that the proposed methods improve the PMI performance compared to the existing constellation-labeling scheme of signals, especially in the low signal-to-noise ratio (SNR) region. Besides, it can been seen that the MIPM saves a large amount of computational time compared to the SA algorithm while achieving the same performance improvement.

Item Type: Article
DOI/Identification number: 10.1109/LWC.2019.2916336
Uncontrolled keywords: Visible light communication, color-shift keying, pragmatic mutual information, joint signal-labeling Optimization
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Jiangzhou Wang
Date Deposited: 17 Oct 2019 15:17 UTC
Last Modified: 09 Dec 2022 03:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77552 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.