热点论文带您探索智能化以及超表面在未来通信中的应用——图书馆前沿文献专题推荐服务(62)
2022-04-11
在上一期前沿文献中推荐中,介绍了未来通信的研究热点,包括:智简无线网络理论与技术,低轨卫星通信遥感融合:架构、技术与试验,用于5G通信的紧凑型二元MIMO天线,以及适用于72.5-81GHz频率范围片上应用的一种基于超材料的新型天线。
在本期的文献推荐中,关注点着眼于智能化以及超表面在未来通信中的应用,选取了:基于数字编码超表面阵列的可编程衍射深度神经网络、用于 5G 应用的基于VO2的超可重构智能反射面、面向未来超 5G、6G 和超级物联网应用的效应曲面法优化 RF-MEMS 可重构器件的开发、基于无线信号的人工智能四篇文献,供相关领域的科研人员参考。
领域一 基于数字编码超表面阵列的可编程衍射深度神经网络
A programmable diffractive deep neural network based on a digital-coding metasurface array
Che Liu, etc.
Nature Electronics, 2022, 5:113-122
The development of artificial intelligence is typically focused on computer algorithms and integrated circuits. Recently, all-optical diffractive deep neural networks have been created that are based on passive structures and can perform complicated functions designed by computer-based neural networks. However, once a passive diffractive deep neural network architecture is fabricated, its function is fixed. Here we report a programmable diffractive deep neural network that is based on a multi-layer digital-coding metasurface array. Each meta-atom on the metasurfaces is integrated with two amplifier chips and acts an active artificial neuron, providing a dynamic modulation range of 35 dB (from −22 dB to 13 dB). We show that the system, which we term a programmable artificial intelligence machine, can handle various deep learning tasks for wave sensing, including image classification, mobile communication coding–decoding and real-time multi-beam focusing. We also develop a reinforcement learning algorithm for on-site learning and a discrete optimization algorithm for digital coding.
https://www.nature.com/articles/s41928-022-00719-9
Fig. A reprogrammable D2NN platform
领域二 用于 5G 应用的基于VO2的超可重构智能反射面
VO2-based ultra-reconfigurable intelligent reflective surface for 5G applications
Randy Matos, etc.
Scientific Reports, 2022, 12
As demand for higher capacity wireless communications increases, new approaches are needed to improve capacity. The lack of configurable radio platforms and power consumed to create new signals are some of the limitations preventing further advancements. To address these limitations, we propose an Ultra-Reconfigurable Intelligent Surface (URIS) platform based on the metal-to-insulator transition property of VO2. A VO2 layer is placed on a high-density micro-heater matrix consisting of pixels that can be electronically switched on. With this manner of control, heat can be transferred to selected areas of the VO2 layer and convert it to highly conductive metallic phase. This technique allows dynamically changing the shape of the reflection surface with high speed. We numerically investigated the heat activated switching and RF reflection characteristics of a reflectarray designed for potential 5G applications operating at 32 GHz. It consists of heating pixels with the size of 40 × 40 μm which can generate metallic VO2 patches or arbitrary shapes with ~ 100 × 100 μm spatial resolution. Our analyses resulted in large phase range of ~ 300° and approximate losses of −2 dB. The proposed device can serve as a novel platform for ultra-reconfigurable reflectarrays, other IRSs, and various wide spectral range RF applications.
https://www.nature.com/articles/s41598-022-08458-9
Fig. (a) Diagram describing differential spatial phase delay of a reflectarray.
(b) Exploded device structure with materials.
领域三 面向未来超 5G、6G 和超级物联网应用的效应曲面法优化 RF-MEMS 可重构器件的开发
Exploitation of response surface method for the optimization of RF-MEMS reconfigurable devices in view of future beyond-5G, 6G and super-IoT applications
Jacopo Iannacci, etc.
Scientific Reports, 2022, 12
The emerging paradigms of the Beyond-5G, 6G and Super-IoT will demand for high-performance Radio Frequency (RF) passive components, and RF-MEMS technology, i.e. Microsystems-based RF passives, is a good candidate to meet such a challenge. As known, RF-MEMS have a complex behavior, that crosses different physical domains (mechanical; electrical; electromagnetic), making the whole design optimization and trimming phases particularly articulated and time consuming. In this work, we propose a novel design optimization approach based on the Response Surface Method (RSM) statistical methodology, focusing on a class of RF-MEMS-based programmable step power attenuators. The proposed method is validated both against physical simulations, performed with Finite Element Method (FEM) commercial software tools, as well as experimental measurements of physical devices. The case study here discussed features 3 DoFs (Degrees of Freedom), comprising both geometrical and material parameters, and aims to optimize the RF performances of the MEMS attenuator in terms of attenuation (S21 Scattering parameter) and reflection (VSWR—Voltage Standing Wave Ratio). When validated, the proposed RSM-based method allows avoiding physical FEM simulations, thus making the design optimization considerably faster and less complex, both in terms of time and computational load.
https://www.nature.com/articles/s41598-022-07643-0
Fig. Cross-section of the RF-MEMS technology employed in this study. Image created with Microsoft Office 365 PowerPoint
领域四 基于无线信号的人工智能
Artificial intelligence built on wireless signals
Xing Lin, etc.
Nature Electronics, 2022, 5
Multi-layer programmable metasurfaces can be used to construct diffractive neural networks in which radio waves are directly processed.
Wireless sensing and communication technology is a dominant feature of modern life — from mobile phones to broadcast television to wireless networks — and is based on the manipulation of radio waves1, electromagnetic waves with frequencies ranging from 30 Hz to 300 GHz. Processing the information carried by radio waves typically requires converting them into electronic signals and computing with electronic processors. However, the increasing demand for high-speed sensing and high-throughput transmission creates challenges for such an approach, and alternative techniques will be required to effectively process radio-wave signals in the next generation of wireless sensing and communication systems.
https://www.nature.com/articles/s41928-022-00724-y
Fig. The PAIM architecture
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