We successfully demonstrated a real time 6.4Tbps self-homodyne coherent transmissions without polarization tracking through specially designed 10km multi-core fibers with 1 polarization maintaining core and 8 normal single-mode cores, using the wavelength unlocked DFB laser.

The inherent unknown deformations of inverse synthetic aperture radar (ISAR) images, such as translation, scaling, and rotation, pose great challenges to space target classification. To achieve high-precision classification for ISAR images, a deformation-robust ISAR image classification network using contrastive learning (CL), i.e., CLISAR-Net, is proposed for deformation ISAR image classification. Unlike traditional supervised learning methods, CLISAR-Net develops a new unsupervised pretraining phase, which means that the method uses a two-phase training strategy to achieve classification. In the unsupervised pretraining phase, combined with data augmentation, positive and negative sample pairs are constructed using unlabeled ISAR images, and then the encoder is trained to learn discriminative deep representations of deformation ISAR images by means of CL. In the fine-tuning phase, based on the deep representations obtained from pretraining, a classifier is fine-tuned using a small number of labeled ISAR images, and finally, the deformation ISAR image classification is realized. In the experimental analysis, CLISAR-Net achieves higher classification accuracy than supervised learning methods for unknown scaled, rotated, and combined deformations. It implies that CLISAR-Net learned more robust deep features of deformation ISAR images through CL, which ensures the performance of the subsequent classification.

Raman distributed optical fiber temperature sensing (RDTS) has been extensively studied for decades because it enables accurate temperature measurements over long distances. The signal-to-noise ratio (SNR) is the main factor limiting the sensing distance and temperature accuracy of RDTS. We manufacture a low water peak optical fiber (LWPF) with low transmission loss to improve the SNR for long-distance application. Additionally, an optimized denoising neural network algorithm is developed to reduce noise and improve temperature accuracy. Finally, a maximum temperature uncertainty of 1.77 °C is achieved over a 24 km LWPF with a 1 m spatial resolution and a 1 s averaging time.

For space division multiplexing self-homodyne coherent systems, we propose a novel digital in-service relative time delay (RTD) estimation method without any additional optoelectronic device. Taking advantage of the frequency-domain periodicity of the colored frequency modulation noise, we manage to find the peak with location reflecting the RTD in its autocorrelation function (ACF). The peak to average ratio is further enhanced by leveraging a low-pass differential finite impulse response filter for robust identification. By simulations, the method is validated to be feasible for various linewidths, formats (16QAM, 32QAM and 64QAM), and links up to 80 km. Particularly, it is proved to be inherently compatible with large-linewidth low-cost lasers for the 10-km link. Also, for a low-complexity implementation, we discuss the way to reduce the number of points used to calculate the ACF while maintaining the same dynamic range. Furthermore, we demonstrate a 50-GBaud 16-QAM experiment to investigate its performances. With received optical power varying from -11 dBm to -17 dBm, 216 points are sufficient to provide an estimation accuracy of standard deviation (STD) less than 0.089 ns for the RTD range of [2.6, 491.0 ns]. The STD can be lowered to 0.036 ns by adopting 218 points. Especially, at -11-dBm ROP, the highest performance has been achieved with an accuracy smaller than the symbol period (0.018-ns STD) and a RTD range of [1.5, 491.0 ns].