11.2 Gbps 100-meter free-space visible light laser communication utilizing bidirectional reservoir computing equalizer.

In this paper, we introduce an innovative post-equalization technique leveraging bidirectional reservoir computing (BiRC), and apply it to waveform-to-symbol level equalization for visible light laser communication for the first time. This strategy is more resistant to nonlinearities compared to traditional equalizers like least mean square (LMS) equalizer, while requiring less training time and fewer parameters than neural network (NN) -based equalizers. Through this approach, we successfully conduct a 100-meter transmission of a 32-amplitude phase shift keying (32APSK) signal using a green laser operating at a wavelength of 520 nm.

One-bit quantization delta-sigma modulation-based autoencoder for power-efficient free-space communication.

This Letter proposes a novel, to the best of our knowledge, approach utilizing a delta-sigma modulation (DSM)-based 1-bit autoencoder (AE) for efficient encoding and decoding in various channel conditions. Simulation analysis demonstrates the AE's ability to mitigate noise by reducing a peak-to-average power ratio (PAPR) and enhancing an in-band power of the signals, particularly under low signal-to-noise ratios (SNRs). The AE-DSM achieves theoretical transmission performance even at SNRs below 6 dB.

Physical prior inspired ensemble learning enables effective channel estimation of underwater visible light communication.

Deep neural networks have been applied to estimate the optical channel in communication systems. However, the underwater visible light channel is highly complex, making it challenging for a single network to accurately capture all its features. This paper presents a novel approach to underwater visible light channel estimation using a physical prior inspired network based on ensemble learning.