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| 1 | AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications显示文摘Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions. | Shun Zhang Muye Li Mengnan Jian Yajun Zhao Feifei Gao | 2021 | China Communications2021，18，7： | 4 |
| 2 | RIS-Aided Constant-Envelope Beamforming for Multiuser Wireless Power Transfer:A Max-Min Approach显示文摘As a revolutionary hardware technology that can reconfigure the propagation environment,reconfigurable intelligent surfaces(RISs)have been regarded as a promising solution to enhance wireless networks.In this paper,we consider a multiuser multiple-input single-output(MISO)wireless power transfer(WPT)system,which is assisted by several RISs.In order to improve energy efficiency and reduce hardware cost,we consider that the energy transmitter(ET)in the WPT system is equipped with a constant-envelope analog beamformer,instead of a digital beamformer.Focusing on user fairness,we study a minimum received power maximization problem by jointly optimizing the ET beamforming and the RIS phase shifts,subject to the constant-envelope constraints.We iteratively solve this non-convex maxmin problem by leveraging both the successive convex approximation(SCA)method and the alternating direction method of multipliers(ADMM)algorithm.Numerical results demonstrate the effectiveness of the proposed algorithm and show attractive performance gain brought by RISs. | Huiyuan Yang Chang Cai Xiaojun Yuan Yingchang Liang | 2021 | China Communications2021，18，3： | 3 |
| 3 | Multi-Person Device-Free Gesture Recognition Using mmWave Signals显示文摘Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented sensing ability.Researchers have made great achievements for singleperson device-free gesture recognition.However,when multiple persons conduct gestures simultaneously,the received signals will be mixed together,and thus traditional methods would not work well anymore.Moreover,the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch,and thus the recognition accuracy would degrade remarkably.To address these problems,we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy.Meanwhile,we also present a deep-learning based robust device free gesture recognition framework,which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment.Furthermore,we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively.Experimental results reveal that the proposed system can achieve average accuracies of 93%and 84%when 10 gestures are conducted in Received:Jun.18,2020 Revised:Aug.06,2020 Editor:Ning Ge different environments by two and four persons simultaneously,respectively. | Jie Wang Zhouhua Ran Qinghua Gao Xiaorui Ma Miao Pan Kaiping Xue | 2021 | China Communications2021，18，2： | 1 |
| 4 | B5G/6G通信的IRS技术综述显示文摘智能反射面(IRS)技术可以显著增强信号的覆盖范围,是B5G/6G的关键技术之一。针对IRS的当前理论研究中的关键技术,重点介绍信道估计、波束管理以及IRS辅助的NOMA、物理层安全增强方面的研究进展,同时给出了未来的研究方向。 | 李刚 王瑜新 章秀银 陈真 陈艺戬 窦建武 杨军 陆海涛 | 2021 | 电子技术应用2021，47，6： | 0 |
| 5 | 面向RIS-mmWave系统的机器学习辅助高效波束训练及信道估计方法显示文摘面向B5G及6G无线通信系统的高速无线信息传输,本文研究了智能超表面辅助毫米波(RIS-mmWave)系统的高效能波束训练及信道估计方法。特别地,基于RIS-mmWave波束管理及有效信道获取的内生关联性,本文创新性地提出一种机器学习辅助的RIS-mmWave系统高效波束训练及信道估计方法。具体而言,在第一阶段,设计了一种新颖的半监督学习模型,实现位置信息辅助的在线快速波束训练,并且免估计地直接获取粗略角度域信息以驱动精细化信道估计;在第二阶段中,提出半监督学习辅助的压缩感知级联信道估计算法,利用半监督学习模型直接输出的粗略角度域信息驱动块正交匹配追踪算法进行信道估计。仿真结果表明,所提波束训练及信道估计方法在系统开销和信道估计误差等方面的性能均优于代表性参考方案。 | 鲜啸啸 陈笛 高晖 曹若菡 别志松 | 2022 | 信号处理2022，38，8： | 0 |
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