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Research on metamaterials shows excellent potential in the field of solar energy harvesting. In recent years, the design of broadband solar metamaterial absorbers (SMAs) has attracted significant interest with the wide application of deep learning methods. This paper proposes a deep neural network (DNN) to realize forward prediction and inverse design of reconfigurable 3D SMAs. In the inverse design, a polarization-insensitive broadband SMA with an absorption bandwidth of 2.7 μm and an average absorption rate of 97.6% with an adjustable bandwidth range of 369 nm is successfully designed. The design of SMAs with different structures is also realized by a transfer learning method to improve the training speed and performance further. Using the transfer learning approach, the training speed of the neural network target model can be accelerated, and its training performance can be improved on small datasets by utilizing the trained neural network source model. Meanwhile, using the trained inverse design target model, a polarization-insensitive broadband SMA was designed with an absorption bandwidth of 2.7 μm, an average absorption of 97.9%, and an adjustable bandwidth range of 141 nm. Finally, we verified the solar energy harvesting capability of the designed broadband SMAs under real-world conditions using air mass (AM) 1.5, and they were calculated to be capable of harvesting the vast majority of the energy. The method is instructive in the design process of SMAs and can be effectively used to explore multifunctional complex nanophotonic devices. • Designing reconfigurable solar absorbers with material selectivity using deep learning methods. • Design of ultra-wideband solar metmaterial absorbers with different shapes through the transfer learning approach. • The designed solar metamaterial absorber has high solar energy harvesting performance. [ABSTRACT FROM AUTHOR]

To compensate for the deficiencies of low spectrum efficiency (SE) and scarce spectrum resources in Radio Frequency (RF), the FSO/THz-RF dual-hop downlink based on Non-Orthogonal Multiple Access (NOMA) is proposed, which utilizes the superior SE of NOMA and the spectrum resources of Free-Space Optical (FSO) and Terahertz (THz). Closed-form expressions of outage probability (OP) and ergodic capacity (EC) are derived for single-user and NOMA system, accounting for joint effects of channel fading, pointing error, and path loss. Additionally, corresponding asymptotic OP and diversity gains provide valuable physical insights. Results show that asymptotic OP and diversity gains are equal for both single-user and NOMA system, with diversity gains dependent on the performance parameters of the first link, independent of the total number and order of users. Moreover, better system performance is achieved with a smaller power allocation coefficient (PAC) mean deviation and NOMA system integer coefficient (NSIC). Finally, numerical results illustrate the superiority of the system. [Display omitted] • NOMA FSO/THz-RF links are modeled to improve SE and to economize spectrum resources. • The OP, AOP and EC are calculated and the coding gain and diversity gain are derived. • An algorithm to determine the optimal PAC and NSIC and two lower bounds are studied. [ABSTRACT FROM AUTHOR]

As one of the spatial dimensions, orbital angular momentum (OAM) expands the channel capacity of underwater wireless optical communication (UWOC) system. However, the performance of the OAM-UWOC system is degraded due to the distortion of the OAM by oceanic turbulence. In this paper, we present a 7-layer convolutional neural network (CNN) and validate its ability to identify OAM patterns carried by Bessel Gaussian (BG) beams in oceanic turbulence channels. The trained CNN performs the task of recognizing OAM patterns with an accuracy of over 99% under weak-to-moderate oceanic turbulence at 100 m. Furthermore, in order to demonstrate the performance and robustness of the designed CNN, the effects of training sample number, epoch, turbulence intensity, balance parameter, transmission distance, type of training dataset, and coding method on the recognition ability of CNN are fully investigated. We hope that the method devised in our work will contribute to UWOC and facilitate its further development. [ABSTRACT FROM AUTHOR]