研究目的
To improve the performance of underwater and free space VLC systems by proposing a novel Square Geometrical Shaping (SGS) odd order QAM constellation that mitigates nonlinearity and enhances system robustness in low SNR and high nonlinearity conditions.
研究成果
The SGS QAM constellation effectively enhances VLC system performance in nonlinear and low SNR conditions by providing thorough Gray coding and acceptable Euclidean distance. Experimental results show improvements in Q factor and data rates, with a maximum of 2.534 Gb/s achieved in underwater transmission. This approach is beneficial for high-speed VLC applications in challenging environments.
研究不足
The study is limited to specific modulation orders (32QAM and 128QAM) and may not generalize to other orders. The experiments are conducted in controlled environments (underwater tank and free space), and real-world conditions might introduce additional challenges. The nonlinearity mitigation relies on constellation design, which may not address all sources of distortion.
1:Experimental Design and Method Selection:
The study employs numerical analysis and experimental demonstrations to compare SGS QAM with traditional QAM constellations. It uses DMT and CAP modulation formats to handle high data rates and nonlinear effects in VLC systems.
2:Sample Selection and Data Sources:
Binary data is mapped into QAM formats (SGS and normal) for transmission. The experiments are conducted in underwater and free space environments using a blue LED.
3:List of Experimental Equipment and Materials:
Includes an arbitrary waveform generator (Tektronix AWG710), power amplifier (Mini-Circuit ZHL-6A-S+), bias-Tee (Mini-Circuit ZFBT-4R2GW-FT+), blue LED, PIN photodiode (Hamamatsu S10784), transimpedance amplifier, oscilloscope (HP85545A), and a water tank for underwater tests.
4:Experimental Procedures and Operational Workflow:
Data is generated, modulated using DMT or CAP, transmitted through the LED, received via a PIN photodiode, amplified, and demodulated offline. Parameters like bias current, driving voltage (Vpp), and data rate are varied to assess performance.
5:Data Analysis Methods:
BER and Q factor are calculated using offline processing, including channel estimation, equalization (e.g., ISFA for DMT, LMS for CAP), and comparison with original data.
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