journal article·2021Open access

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

Lukas Danys, Radek Martinek, Rene Jaros, Jan Baros, Petr Simonik, Vaclav Snasel

Computers, Materials & Continua· Volume 68· Issue 3

Abstract

This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication (SDR-V2X-VLC). The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization (FFSDE) methods of the least mean squares (LMS), normalized LMS (NLMS) and QR decomposition-based recursive least squares (QR-RLS) algorithms. Individual parameters of adaptive equalizations are adjusted in real-time to reach the best possible results. Experiments were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals (USRP) from National Instruments. The transmitting/receiving elements used multistate quadrature amplitude modulation (M-QAM) implemented in LabVIEW programming environment. Experimental results were verified based on bit error ratio (BER), error vector magnitude (EVM) and modulation error ratio (MER). Experimental results of the pilot study unambiguously confirmed the effectiveness of the proposed solution (longer effective communication range, higher immunity to interference, deployment of higher state QAM modulation formats, higher transmission speeds etc.), as the adaptive equalization significantly improved BER, MER and EVM parameters. The best results were achieved using the QR-RLS algorithm. The results measured on deployed QR-RLS algorithm had significantly better Eb/N0 (improved by approx. 20 dB) and BER values (difference by up to two orders of magnitude).
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Optical Wireless Communication Technologies · PAPR reduction in OFDM · Power Line Communications and Noise

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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