Please use this identifier to cite or link to this item:
|Title:||Coordinated resource allocation-based integrated visible light communication and positioning systems for indoor IoT||Authors:||Yang, Helin
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2020||Source:||Yang, H., Zhong, W.-D., Chen, C., Alphones, A., Du, P., Zhang, S., & Xie, X. (2020). Coordinated resource allocation-based integrated visible light communication and positioning systems for indoor IoT. IEEE Transactions on Wireless Communications, 19(7), 4671-4684. doi:10.1109/TWC.2020.2986109||Journal:||IEEE Transactions on Wireless Communications||Abstract:||With the rapid development of Internet of Things (IoT) in the smart city, smart grid and smart industry, indoor communication and positioning are important fields of applications for indoor IoT. This paper presents an integrated visible light communication and positioning (VLCP) system for indoor IoT, in order to provide the high-speed data rate and high-accuracy positioning for IoT devices. where the filter bank multicarrier-based subcarrier multiplexing (FBMCSCM) technique is exploited to effectively reduce the out-ofband interference (OOBI) on both adjacent communication and positioning subcarriers. After that, we propose a coordinated resource allocation approach for the system with the purpose of maximizing the sum rate while guaranteeing the minimum data rates and positioning accuracy requirements of devices. To this end, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results verify the superiority in performance of the presented integrated VLCP system for indoor IoT, and the results also reveal that the proposed coordinated resource allocation approach can effectively improve the sum rate and the positioning accuracy compared with other resource allocation approaches.||URI:||https://hdl.handle.net/10356/143531||ISSN:||1536-1276||DOI:||10.1109/TWC.2020.2986109||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TWC.2020.2986109||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on Mar 7, 2021
Updated on May 15, 2021
Updated on May 15, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.