Although the 5G wireless network has made significant advances, it is not enough to accommodate the rapidly rising requirement for broader bandwidth in post-5G and 6G eras. As a result, emerging technologies in higher frequencies including visible light communication (VLC), are becoming a hot topic. In particular, LED-based VLC is foreseen as a key enabler for achieving data rates at the Tb/s level in indoor scenarios using multi-color LED arrays with wavelength division multiplexing (WDM) technology. This paper proposes an optimized multi-color LED array chip for high-speed VLC systems. Its long-wavelength GaN-based LED units are remarkably enhanced by V-pit structure in their efficiency, especially in the “yellow gap” region, and it achieves significant improvement in data rate compared with earlier research. This work investigates the V-pit structure and tries to provide insight by introducing a new equivalent circuit model, which provides an explanation of the simulation and experiment results. In the final test using a laboratory communication system, the data rates of eight channels from short to long wavelength are 3.91 Gb/s, 3.77 Gb/s, 3.67 Gb/s, 4.40 Gb/s, 3.78 Gb/s, 3.18 Gb/s, 4.31 Gb/s, and 4.35 Gb/s (31.38 Gb/s in total), with advanced digital signal processing (DSP) techniques including digital equalization technique and bit-power loading discrete multitone (DMT) modulation format.

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. Remarkably, our system achieves a high data rate of 11.2 Gbps, all while maintaining a satisfying bit error rate (BER) below the 7% hard decision forward error correction (HD-FEC) threshold of 3.8E-3.

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. A three-subnetwork architecture was developed to estimate the linear distortion from inter-symbol interference (ISI), quadratic distortion from signal-to-signal beat interference (SSBI), and higher-order distortion from the optoelectronic device. The superiority of the Ensemble estimator is demonstrated from both the time and frequency domains. In terms of mean square error performance, the Ensemble estimator outperforms the LMS estimator by 6.8 dB and the single network estimators by 15.4 dB. In terms of spectrum mismatch, the Ensemble estimator has the lowest average channel response error, which is 0.32 dB, compared to 0.81 dB for LMS estimator, 0.97 dB for the Linear estimator, and 0.76 dB for the ReLU estimator. Additionally, the Ensemble estimator was able to learn the V-shaped Vpp-BER curves of the channel, a task not achievable by single network estimators. Therefore, the proposed Ensemble estimator is a valuable tool for underwater visible light channel estimation, with potential applications in post-equalization, pre-equalization, and end-to-end 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. In a 40-m free-space link experiment, the AE-DSM exhibits an 8.4-dB lower bit error rate (BER) compared to 64QAM-DSM, enabling a transmission rate of 1.31 Gbps. Furthermore, the 1-bit AE-DSM significantly reduces power consumption in the receiving analog-to-digital converter (ADC), facilitates transmission at low SNRs, and effectively mitigates nonlinear effects. Consequently, the DSM-based AE holds immense potential for future mobile fronthaul links.

The next generation of mobile communication is committed to establishing an integrated three-dimensional network that encompasses air, land, and sea. The visible light spectrum is situated within the transmission window for underwater communication, making visible light laser communication a focus of intense research. In this paper, we design and integrate a compact 5-λ transmission module based on five laser diodes with different wavelengths, utilizing a self-developed narrow-ridge GaN blue laser. With this transmitter, we have developed a polarization division multiplexing (PDM) 5-λ underwater visible light laser communication (UVLLC) system based on this transmission module. To enhance the transmission quality of the system, we designed a dual-branch ResDualNet network as a reciprocal differential receiver that incorporates common-mode noise cancellation and equalization functions for post-processing the received signals. With the combined contribution of the devices and algorithms, we achieved a total transmission rate of 170.1 Gbps, which represents a 16.1 Gbps increase compared to systems that do not utilize ResDualNet. To the best of our knowledge, this is the highest communication rate currently achievable in a UVLLC system using a single laser transmission module.