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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 | 可重构智能表面中低复杂度毫米波信道估计算法显示文摘针对可重构智能表面(RIS)辅助的大规模多输入多输出(MIMO)毫米波系统中信道估计复杂度高的问题,该文提出一种低复杂度的信道估计算法。在该方案中,将RIS部分元素连接射频(RF)链,分离估计基站/用户和RIS之间的信道,分开获取信道有助于提升用户移动性场景下信道估计的灵活性。在所考虑系统中,首次使用低复杂度的2维快速傅里叶变换(2D-FFT)算法对角度进行估计,并考虑信号补零以获得更加精准的角度估计值,最后利用信号2维空间谱的谱峰和其对应的辐角得到路径增益估计。仿真结果表明,该算法达到了优良的信道估计性能,且在确保信道估计性能的系统参数设置下,该算法具有压倒性的复杂度优势。 | 蒲旭敏 孙致南 李静洁 黄琼 陈前斌 | 2022 | 电子与信息学报2022，44，7： | 2 |
| 3 | Delay Minimization for Intelligent Reflecting Surface Assisted Federated Learning显示文摘Federated learning(FL), which allows multiple mobile devices to cooperatively train a machine learning model without sharing their data with the central server, has received widespread attention.However, the process of FL involves frequent communications between the server and mobile devices,which incurs a long latency. Intelligent reflecting surface(IRS) provides a promising technology to address this issue, thanks to its capacity to reconfigure the wireless propagation environment. In this paper, we exploit the advantage of IRS to reduce the latency of FL. Specifically, we formulate a latency minimization problem for the IRS assisted FL system, by optimizing the communication resource allocations including the devices’ transmit-powers, the uploading time, the downloading time, the multi-user decomposition matrix and the phase shift matrix of IRS. To solve this non-convex problem, we propose an efficient algorithm which is based on the Block Coordinate Descent(BCD) and the penalty difference of convex(DC) algorithm to compute the solution. Numerical results are provided to validate the efficiency of our proposed algorithm and demonstrate the benefit of deploying IRS for reducing the latency of FL. In particular, the results show that our algorithm can outperform the baseline of Majorization-Minimization(MM) algorithm with the fixed transmit-power by up to 30%. | Ning Huang Tianshun Wang Yuan Wu Suzhi Bi Liping Qian Bin Lin | 2022 | China Communications2022，19，4： | 1 |
| 4 | 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 |
| 5 | Performance Analysis for Large Intelligent Surface Assisted Vehicular Networks显示文摘Large intelligent surface(LIS)is considered as a new solution to enhance the performance of wireless networks[1].LIS comprises low-cost passive elements which can be well controlled.In this paper,a LIS is invoked in the vehicular networks.We analyze the system performance under Weibull fading.We derive a novel exact analytical expression for outage probability in closed form.Based on the analytical result,we discuss three special scenarios including high SNR case,low SNR case,as well as weak interference case.The corresponding approximations for three cases are provided,respectively.In order to gain more insights,we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference,threshold and fading parameters which leads to 0 diversity order.Furthermore,we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds.Similar to the outage performance,three special cases are studied and the asymptotic expressions are provided in simple forms.A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope.Finally,we give the energy efficiency of LIS-assisted vehicular network.Numerical results are presented to verify the accuracy of our analysis.It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase Received:Aug.6,2020 Revised:Nov.17,2020 Editor:Caijun Zhong shift scheme significantly. | Yiyang Ni Yaxuan Liu Jin Zhou Qin Wang Haitao Zhao Hongbo Zhu | 2021 | China Communications2021，18，3： | 1 |
| 6 | FLIGHT:Federated Learning with IRS for Grouped Heterogeneous Training显示文摘In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in FL.In addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication quality.In this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the clients.Specifically,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL model.The simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous clients.Besides the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models. | Tong Yin Lixin Li Donghui Ma Wensheng Lin Junli Liang Zhu Han | 2022 | Journal of Communications and Information Networks2022，7，2： | 0 |
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