PAM-4

Wireless data traffic is expected to exponentially increase in the future, and meeting this demand will require high data rate photonic-wireless links operating in the W-band (75–110 GHz). For this purpose, pulse-amplitude-modulation with four levels (PAM-4)-based intensity modulation and direct detection (IM-DD) photonic-wireless systems are preferred due to their simplified configuration. In this Letter, we present an experimental demonstration of an IM-DD PAM-4 photonic-wireless link in the W-band, leveraging a monolithic dual-laser photonic chip to enhance integration. Through injection-locking by an optical comb, the chip generates a W-band wireless signal via photo-mixing with a photodiode. This comb injection approach facilitates the phase correlation of the chip’s two modes, resulting in a stabilized beat note. Additionally, the on-chip integration of the dual lasers enables the modulation of the two modes with a single modulator, improving the signal-to-noise ratio (SNR) while eliminating the need for extra splitters or combiners. Meanwhile, the envelope detector (ED) plays a crucial role in the simplified configuration, contributing to the overall decrease in size, weight, power, and complexity of the system. The integration of the chip-based phase-locked light source and the utilization of the ED thus signify noteworthy features of our experimental setup, which functions without the necessity of both optical and electrical local oscillators. PAM-4 signal modulation is simultaneously applied to the two coherent optical carriers. Our experiments have effectively transmitted 5 and 10 Gbaud PAM-4 W-band wireless signals in a cost-effective, lightweight, and straightforward configuration, achieving a line data rate of up to 20 Gbit/s economically. These experimental results demonstrate the practical potential of implementing fully integrated photonic-wireless transmitters.

Modulation format recognition (MFR) is a key technology for adaptive optical systems, but it faces significant challenges in underwater visible light communication (UVLC) due to the complex channel environment. Recent advances in deep learning have enabled remarkable achievements in image recognition, owing to the powerful feature extraction of neural networks (NN). However, the high computational complexity of NN limits their practicality in UVLC systems. This paper proposes a communication-informed knowledge distillation (CIKD) method that achieves high-precision and low-latency MFR with an ultra-lightweight student model. The student model consists of only one linear dense layer under a communication-informed auxiliary system and is trained under the guidance of a high-complexity and high-precision teacher model. The MFR task involves eight modulation formats: PAM4, QPSK, 8QAM-CIR, 8QAM-DIA, 16QAM, 16APSK, 32QAM, and 32APSK. Experimental results show that the student model based on CIKD can achieve comparable accuracy to the teacher model. After knowledge transfer, the prediction accuracy of the student model can be increased by up to 87%. Besides, it is worth noting that CIKD’s inference accuracy can reach up to 100%. Moreover, the parameters constituting the student model in CIKD correspond to merely 18% of the parameters found in the teacher model, which facilitates the hardware deployment and online data processing of MFR algorithms in UVLC systems.