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. 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.

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. 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.

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. 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.