Design and fabrication of 3D-printed Y-branch power splitters based on the conjugated polymer PFeF8
Conjugated polymers are essential materials employed in organic optoelectronics. Their application in photonic circuits is a subject of increasing relevance and study. In this work, we present the design, numerical simulation, fabrication, and experimental characterization of a Y-branch optical power splitter based on the PFeF8 conjugated polymer, a basic building block used in such circuits. The blend with PMMA enables the control and reduction of the refractive index and offers a large transparency region above 550 nm, which is useful for the design of waveguide structures based on materials with a low extinction coefficient. Measurements in samples of the PFeF8/PMMA blend at concentrations of 5% and 10% revealed propagation losses of 0.022 dB/mm (R2 = 0.96) and 0.025 dB/mm (R2 = 0.90), respectively. The splitters fabricated with the DLP 3D printing technique show excess loss from 4.78 dB to 6.36 dB, most of it due to scattering on the guiding structure. An average 0.16 dB power imbalance between the two arms indicates the uniformity of the splitting ratio.
Asymmetric CNN-based equalization for clipping distortion compensation in LED-based VLC systems
Visible Light Communication (VLC) systems utilizing Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) are inherently limited by a high Peak-to-Average Power Ratio (PAPR), which leads to non-linear distortions due to the restricted dynamic range of LEDs. Conventional PAPR reduction techniques often impose significant computational overhead on the transmitter or severely degrade the Error Vector Magnitude (EVM). This paper proposes an asymmetric deep learning-based framework to mitigate clipping noise. We introduce a low-complexity clipping approach at the transmitter ((𝑁)) and shift the compensation burden to a Convolutional Neural Network (CNN) at the receiver. Two clipping ratios were evaluated: CR = 0.9 (moderate, 2.9 dB PAPR reduction) and CR = 0.7 (aggressive, 5.8 dB PAPR reduction). Experimental results from 20 spatial positions within a 2.0 × 1.0 m2 test area demonstrate that the proposed CNN effectively recovers the signal under both clipping conditions. For 64-QAM modulation with CR = 0.7,
the CNN achieves a mean EVM of 7.07%, below the 8% reference threshold defined by 3GPP standards, and a mean BER of 8.98 × 10−4, while the conventional MMSE equalizer reaches 15.05% EVM and 5.37 × 10−2 BER. The architecture shows consistent generalization and spatial robustness compared to standard Deep Neural Networks (DNNs), suggesting its potential for LED-based lighting applications.