EurekaMag.com logo
+ Site Statistics
References:
54,215,046
Abstracts:
30,230,908
PMIDs:
28,215,208
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

Channel capacity and receiver deployment optimization for multi-input multi-output visible light communications



Channel capacity and receiver deployment optimization for multi-input multi-output visible light communications



Optics Express 24(12): 13060-13074



Multi-input multi-output (MIMO) technique is attractive for visible light communication (VLC), which exploits the high signal-to-noise ratio (SNR) of a single channel to overcome the capacity limitation due to the small modulation bandwidth of the light emitting diode. This paper establishes a MIMO VLC system under the non-negativity, peak power and dimmable average power constraints. Assume that perfect channel state information at the transmitter is known, the MIMO channel is changed to parallel, non-interfering sub-channels by using the singular value decomposition (SVD). Based on the SVD, the lower bound on the channel capacity for MIMO VLC is derived by employing entropy power inequality and variational method. Moreover, by maximizing the derived lower bound on the capacity under the given constraints, the receiver deployment optimization problem is formulated. The problem is solved by employing the principle of particle swarm optimization. Numerical results verify the derived capacity bound and the proposed deployment optimization scheme.

(PDF emailed within 0-6 h: $19.90)

Accession: 057382518

Download citation: RISBibTeXText

PMID: 27410325

DOI: 10.1364/OE.24.013060



Related references

Enhanced channel estimation and symbol detection for high speed multi-input multi-output underwater acoustic communications. Journal of the Acoustical Society of America 125(5): 3067-3078, 2009

RGB visible light communication using mobile-phone camera and multi-input multi-output. Optics Express 24(9): 9383-9388, 2016

Convex relaxation for illumination control of multi-color multiple-input-multiple-output visible light communications with linear minimum mean square error detection. Applied Optics 56(23): 6587-6595, 2017

Multi-input multi-output underwater communications over sparse and frequency modulated acoustic channels. Journal of the Acoustical Society of America 130(1): 249-262, 2011

Input Shaping enhanced Active Disturbance Rejection Control for a twin rotor multi-input multi-output system (TRMS). Isa Transactions 62: 287-298, 2016

Pole assignment for multi-input multi-output systems using output feedback. Automatica 27(6): 1061-1062, 1991

Multi-modal, multi-output, multiregional variable input-output model. Regional Science and Urban Economics 14(2): 265-281, 1984

Spectrally interleaved multi-carrier signals for radar network applications and multi-input multi-output radar. Iet Radar Sonar & Navigation 7(3): 261-269, 2013

Inter-cell interference mitigation in multi-cellular visible light communications. Optics Express 24(8): 8512-8526, 2016

Mitigation technique for receiver performance variation of multi-color channels in visible light communication. Sensors 11(6): 6131-6144, 2012

Experimental investigation of multi-band OCT precoding for OFDM-based visible light communications. Optics Express 25(11): 12908-12914, 2017

A policy oriented multi-input and multi-output measure of overall efficiency and its decomposition. Journal of Agricultural Economics 48(1): 52-64, 1997

Consistent parameter estimation in multi-input multi-output discrete systems. Automatica 13(3): 301-305, 1977

Estimation of a multi-input multi-output model of lot-fed beef cattle in Australia. Working Paper Series in Agricultural and Resource Economics University of New England (2004-13): 23 pp., 2004

The multi-output and multi-input symmetric positive equilibrium problem. Agricultural sector modelling and policy information systems Proceedings of the 65th European Seminar of the European Association of Agricultural Economists EAAE, Bonn, Germany, 29-31 March, 2000: 88-100, 2001