Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication

Abstract

This paper focuses on a channel feed-forward software defined equalization (FSDE) of visible light communication (VLC) multistate quadrature amplitude modulation (M-QAM) based system, implemented in the LabVIEW programming environment. A highly modular platform is introduced; the whole experiment is simulated in software and then thoroughly explored and analyzed during practical measurements in the laboratory, simulating real-world situations. The whole platform is based on modified National Instruments software defined radios (NI SDR) and a commercially available Philips light source, often used in Czech government institutions. Three FSDE algorithms were tested: least mean squares (LMS), normalized least mean squares (NLMS), and QR decomposition based RLS (QR-RLS). Based on measurements, QR-RLS provides the best results, improving measured values by up to 10%. The experiments also show that the simulated results are very similar to real measurements, thus proving the validity of the chosen approach. The whole platform manages to improve measured data simply by making changes to the software side of the testing prototype.

Performance Analysis and Software-Defined Implementation of Real-Time MIMO FSO With Adaptive Switching in GNU Radio Platform

“…Apart from highly reconfigurable software defined implementation in the RF domain, SDR has also attracted considerable interest in OWC systems for diverse applications. In [16], experimental evaluation and performance analysis of an indoor visible light communications (VLC) system with adaptive softwaredefined equalization using USRPs and LABVIEW was reported. The work reported validated the flexibility of LABVIEW software platform and the ability to improve the measured data simply by changing the software side of the testing prototype.…”

Enhancements of SDR-Based FPGA System for V2X-VLC Communications

“…The least mean squares algorithm (LMS) [22], normalized LMS algorithm (NLMS) [23], and QR decompositionbased recursive least squares (QR-RLS) [24] algorithm. All these methods provided good accuracy in different areas and have a potential to improve the system performance [21,25].…”

“…Some experiments proved, that results from simulated environments can't be reached in real-world scenarios. Therefore, any further research must be backed by relevant measured data and tested on hardware platform resembling the future deployment scenarios [19,21].…”

“…These methods are based on calculation of error signal e between real output of the system y and the desired output d according to Eq. (1) [21,22,25].…”

“…Speed of NLMS algorithm depends on values of input signal x. The higher the values of input signal x, the slower the convergence of NLMS will be [21,23,26].…”