Jingxuan Cheng

Solar cells have been employed in underwater wireless optical communication (UWOC) for data reception, featuring cost-effectiveness and easy optical alignment. However, crystalline silicon (c-Si) solar cells are rigid and impractical for deployment on curved or irregular surfaces. This letter presents a novel UWOC system that utilizes a flexible copper indium gallium selenide (CIGS) solar cell as the receiver, demonstrating potential for conformal integration onto the surface of submerged devices. The key challenge for practical implementation is preventing CIGS breakdown under reverse bias (required for bandwidth enhancement). Experiments show that at a reverse bias voltage of 14.7 V and a received optical power (ROP) of 12.67 dBm after a 7-m underwater transmission, the CIGS solar cell achieved the maximum data rates of 32 and 25 Mbit/s for flat and bent PV surface, respectively. Moreover, the flexible CIGS solar cell can still work at an ROP of −4 dBm, achieving a 6-Mbit/s data rate with the bit error rate (BER) under the forward error correction (FEC) limit. To the best of our knowledge, this work represents the first application of a flexible CIGS solar cell in a UWOC system. Their remarkable flexibility and cut-to-size capability highlight their potential for diverse UWOC applications.

Obtaining the light source position from the image is an important solution for achieving link alignment in laser-based underwater wireless optical communication (UWOC) systems. However, in practical scenarios, the misalignment degree between the light source and camera is variable, and factors such as ambient light may introduce disturbances, leading to significant variations in the appearance of light spots in images. Existing research primarily relies on simple features like brightness, color, or shape, which makes it difficult to accurately obtain position information from these non-ideal images. In this paper, deep neural networks (DNNs) with strong feature extraction capabilities are introduced to automatically learn the patterns of the light source from diverse images. A detection architecture cascading an object detector and a keypoint detector is adopted, achieving better comprehensive performance in terms of accuracy and speed. To train and evaluate the deep learning model, we construct the UWOC Light Source Detection Benchmark (ULDB) dataset. This dataset comprises 2200 images captured in a standard swimming pool, covering a misalignment range far beyond existing studies. On the ULDB test set, the proposed detection method achieves an average precision (AP) of 99.1% and an average positioning error of 4.66 pixels, while the traditional method may frequently extract false light spots. To the best of our knowledge, the ULDB dataset is the first image dataset specifically designed for the task of link alignment between UWOC terminals. And the dataset is available at https://github.com/happycode123/ULDB-Dataset.

The link alignment in underwater wireless optical communication (UWOC) systems is a knotty problem. The diffractive deep neural network (D ² NN) has shown great potential in accomplishing tasks all optically these years. In this paper, a 6-layer D ² NN is proposed to alleviate the link alignment difficulties in UWOC systems. Simulation results demonstrate that the proposed method can focus incident light with tilt angles from 0° to 60° into a 6.25% area of the detection plane with an average focusing efficiency of 93.15%. Extra simulations further reveal that more layers lead to a sustained performance improvement before reaching a bottleneck, and the D ² NN can achieve large field angle focusing within a certain focusing area. The proposed receiver design, which can be highly integrated with detectors, holds promise to realize reliable link establishment in UWOC systems in the future.

Polarization measurement plays an important role in optical detection, communication, and imaging systems. Compact polarimetry with a wide angle of incident light will break the restrictions of the limited incident angle and improve its practicality. In this paper, a full Stokes vector polarimetry with a wide incident angle of ±20°, based on a two-dimensional metasurface, is proposed. According to the simulation results, the maximum measurement error of the Stokes vector at 20° oblique incidence is 0.09. The light transmittance of the proposed structure is higher than 83%. Moreover, the tilt angle of the incident light can be determined with a calculation error lower than 0.5°, according to the focusing position of the transmitted light on the focal plane. The operating wavelength of the proposed polarimetry is set to 530 nm of green light, which makes it a potential application in visible light communication and underwater optical systems.