基于深度学习的高维信号信道估计算法

针对大部分基于深度学习（Deep Learning,DL）的信道估计算法估计高维信号时出现的训练开销过大、泛化能力差等问题,提出了一种不需要训练的基于深度学习的高维信号信道估计算法,即UTCENet（Untrained Channel Estimation Network）。在UTCENet中,信道信息上的复杂分布转换为模型参数上的简单分布,即通过神经网络参数化来获得隐式先验知识并将其应用于信道估计。虽然该算法不需要任何训练,但保证了估计的性能,其原因在于专门设计的网络模型可以有效利用时频网格中元素的相关性。仿真结果表明,与传统方法以及现有的深度学习方法相比,所提出的算法在归一化均方误差和误码率方面性能提升明显。     

基于复数生成对抗网络的5G OFDM信道估计方法

准确的信道估计能够显著地降低误码率（bit error rate,BER）,提高无线通信效率和质量,是5G OFDM通信系统接收机设计的关键环节之一。基于最小二乘（least square,LS）法和基于最小均方差（minimum mean square error,MMSE）的信道估计方法利用系统稀疏性计算信道响应矩阵,但LS算法计算精度较低,而MMSE算法计算量过大。为提升估计精度,业界设计了基于深度学习的信道估计方法。然而,现有的深度学习方法将复数矩阵拆分成实部和虚部,没有充分提取信道中的复数特征,造成估计的信道响应矩阵出现失真。为此,设计了一种基于复数的生成对抗网络模型,充分提取信号的复数特征,从而更准确地估计5G新空口（new radio,NR）标准的物理下行链路共享信道（physical downlink shared channel,PDSCH）的信道响应矩阵。为了验证所提方法的有效性,将所提方法分别与LS算法、实际信道估计、超分辨率神经网络、残差神经网络信道估计算法进行了对比分析。结果表明,当估计的信道响应矩阵与真实矩阵之间的均方差达到0.01时,采用所提方法实现的无线...     

人工智能在移动通信网络中的应用:基于机器学习理论的信道估计与信号检测算法

机器学习是人工智能的重要方向之一,文章介绍两种机器学习理论应用于移动通信网络的案例。首先介绍一种基于期望最大化算法的信道估计器,不需要导频也可实现对信道的盲估计,提高了系统的吞吐率。随后介绍了一种基于深度神经网络的信道估计和信号恢复算法,该算法能够隐性地分析信道的特性,直接将信号恢复出来,当导频数据减少时其性能优于传统算法。神经网络估计器虽然训练模型复杂,但后续可直接将信号恢复,降低了信号处理的复杂度。     

基于深度学习的DRFM信号识别

针对数字射频存储器(DRFM)产生信号与源信号之间无法有效区分的问题,运用基于小波变换的同步压缩变换将时域的雷达信号转换为时频图,运用深度学习强大的图像识别能力,实现了基于深度学习的源信号与DRFM信号识别,从而解决了在雷达信号处理中无法有效区分回波信号和DRFM欺骗信号以及在雷达干扰识别中基于DRFM的欺骗干扰难以识别的问题。为了验证深度学习过程的可靠性,通过神经网络可解释性算法对训练结果进行了验证和分析。实验结果表明,相比于识别原始信号,识别DRFM信号神经网络需要用到更多的特征,神经网络判断准确率达到了96.33%,识别精度良好。     

基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测

近年来,深度学习成为无线通信领域的关键技术之一。在基于深度学习的一系列MIMO信号检测算法中,大多未充分考虑相邻天线之间的干扰消除问题,无法彻底消除多用户干扰对误码率性能的影响。为此,该文提出一种将深度学习与串行干扰消除(SIC)算法进行结合的方法用于大规模MIMO系统上行链路信号检测。首先,通过优化传统的检测网络(DetNet)及改进ScNet检测算法,该文提出一种基于深度神经网络(DNN)的检测算法,称为ImpScNet。在此基础上,进一步将SIC思想应用到深度学习框架结构设计中,提出一种基于深度学习的大规模MIMO多用户SIC检测算法,称为ImpScNet-SIC。此算法在每个检测层上分为两级,其中,第1级由该文提出的ImpScNet算法提供初始解,再将初始解解调至相应的星座点上作为SIC的输入,由此构成该算法的第2级。此外,在SIC中也使用了ImpScNet算法估计传输符号,以便获得最优性能。仿真结果表明,与已有的各种典型代表算法相比,该文所提ImpScNet-SIC检测算法特别适合大规模MIMO信号检测,具有收敛速度快、收敛稳定及复杂度相对较低的优势,并且在10–3误码率上有至少0.5 dB以上的增益。     

基于CNN的毫米波无蜂窝大规模MIMO信道估计

针对小区间干扰导致蜂窝边缘无法满足不断增长的数据速率需求问题,毫米波无蜂窝大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)系统被认为是一种很有前途的解决方案。然而,毫米波的高频率、大带宽以及接入点配置的大量天线给信道估计带来了较大挑战。将毫米波大规模MIMO信道矩阵视为二维图像,结合图像去噪方法提出一种基于改进去噪卷积神经网络(Improved-Denoising Convolutional Neural Network, I-DnCNN)的信道估计算法。通过具有注意力机制的压缩与激励(Squeeze-and-Excitation, SE)模块,自适应调整提取的全局特征以增强对信道噪声特征的学习,根据接收信号估计出噪声等级图且增添为输入,提升对噪声的鲁棒性。最后,采用残差学习的方式获得估计信道矩阵。利用理论信道模型和基于波束追踪的信道数据集进行的仿真实验结果表明,与去噪卷积神经网络(Denoising Convolutional Neural Network, DnCNN)算法相比,所提算法在两个数据集下的信道估计精度可分别平均提升2.27...     

一种TIADC时间失配误差自适应校准算法

设计了一种TIADC时间失配误差自适应校准算法。基于相邻通道信号互相关原理,将相邻通道的输出信号作相关运算,利用简单的乘法器、加法器和取绝对值即可实现时间误差的估计;利用基于泰勒级数展开的1阶级联误差补偿方法进行误差校正。误差估计模块和校准模块构成反馈环路,实现误差的实时跟踪与校正。MATLAB仿真结果表明,当输入信号归一化频率为fin/fis=0.477 1时,系统校准后的SNR提高了45 dB以上,校准效果明显。相比于传统的基于泰勒级数展开的高阶校准,本文校准算法的结构更简单,校准精度更高,整个奈奎斯特频率范围内均有较好的校准效果,非常适用于工程应用。     

基于数据增强和特征嵌入的自动调制识别

基于端到端的深度学习模型已经被广泛应用于自动调制识别。现有的深度学习方案大多数依赖于丰富的样本分布,而大批量的标记训练集通常很难获得。提出了一种基于数据驱动和选择性核卷积神经网络(Convolutional Neural Network,CNN)的自动调制识别框架。首先开发深度密集生成式对抗网络增强5种调制信号的原始数据集;其次选择平滑伪Wigner-Ville分布作为信号的时频表示,并将注意力模块用于聚焦时频图像分类中的差异区域;最后将真实信号输入轻量级卷积神经网络进行时间相关性提取,并融合信号的时频特征完成分类。实验结果表明,所提算法提高了在低信噪比情况下的识别精度,表现出较强的鲁棒性。     

基于深度学习的故障诊断方法综述

海量高维度的过程测量信息给传统的故障诊断算法带来极大的计算复杂度和建模复杂度，且传统诊断算法存在难以利用高阶量进行在线估计的不足。鉴于深度学习技术强大的数据表示学习和分析能力，基于深度学习的故障诊断引起了工业界和学术界的广泛关注，并促使智能过程控制更加自动化和有效。该文从方法上将基于深度学习的故障诊断技术分为:基于栈式自编码的故障诊断方法、基于深度置信网络的故障诊断方法、基于卷积神经网络的故障诊断方法及基于循环神经网络的故障诊断方法4类，分别进行了回顾和总结，最后从数据预处理、深度网络设计和决策3个层面对这一领域进行展望，提出了“集成创新”、“数据+知识”和“多技术融合”等故障诊断思想，阐明基于深度学习技术进行复杂系统的故障诊断仍具有巨大潜力。     

基于领域自适应的动态噪声辐射源个体识别

现有基于深度神经网络的辐射源识别算法受训练场景限制,当待测信号与训练数据集的信道环境噪声不一致时,网络的识别性能严重退化。为了克服该问题,本文提出一种基于迁移学习的辐射源个体识别算法。该算法结合领域自适应的思想,建立优化模型将不同信噪比下信号的特征对齐,使在特定信噪比下训练的神经网络学习到与信道噪声无关的射频指纹特征,实现对其他信噪比下信号的高准确率识别。仿真实验结果表明,提出的算法显著提升了基于深度神经网络的辐射源个体识别算法在动态噪声条件下的准确率,在待识别信号信噪比下降4 dB的情况下,准确率提升了45.18%。      

Cost-effective test solutions for smart card and other integrated flash applications

From its emergence in the late 1980s as a lower cost alternative to early EEPROM technologies, flash memory has evolved to higher densities and speedsand rapidly growing acceptance in mobile applications.In the process, flash memory devices have placed increased test requirements on manufacturers. Today, as flash device test grows in importance in China, manufacturers face growing pressure for reduced cost-oftest, increased throughput and greater return on investment for test equipment. At the same time, the move to integrated flash packages for contactless smart card applications adds a significant further challenge to manufacturers seeking rapid, low-cost test.     

An improved parallel thinning algorithm with two subiterations

The parallel thinning algorithm with two subiterations is improved in this paper. By analyzing the notions of connected components and passes, a conclusion is drawn that the number of passes and the number of eight-connected components are equal. Then the expression of the number of eight-connected components is obtained which replaces the old one in the algorithm. And a reserving condition is proposed by experiments, which alleviates the excess deletion where a diagonal line and a beeline intersect. The experimental results demonstrate that the thinned curve is almost located in the middle of the original curve connectivelv with single pixel width and the processing speed is high.     

Theoretical analysis of the relationship between the Brillouin gain coefficient and the strain in the optical-fiber sensors

The relation between the power of the Brillouin signal and the strain is one of the bases of the distributed fiber sensors of temperature and strain. The coefficient of the Bfillouin gain can be changed by the temperature and the strain that will affect the power of the Brillouin scattering. The relation between the change of the Brillouin gain coefficient and the strain is thought to be linear by many researchers. However, it is not always linear based on the theoretical analysis and numerical simulation. Therefore, errors will be caused if the relation between the change of the Brillouin gain coefficient and the strain is regarded as to be linear approximately for measuring the temperature and the strain. For this reason, the influence of the parameters on the Brillouin gain coefficient is proposed through theoretical analysis and numerical simulation.     

Novel fiber-optical interferometer with miniaturized probe for in-hole measurements

Today, micro-system technology and the development of new MEMS （Micro-Electro-Mechanical Systems） are emerging rapidly. In order for this development to become a success in the long run, measurement systems have to ensure product quality. Most often, MEMS have to be tested by means of functionality or destructive tests. One reason for this is that there are no suitable systems or sensing probes available which can be used for the measurement of quasi inaccessible features like small holes or cavities. We present a measurement system that could be used for these kinds of measurements. The system combines a fiber optical, miniaturized sensing probe with low-coherence interferometry, so that absolute distance measurements with nanometer accuracy are possible.     

Teleportation of an arbitrary unknown N-qubit entangled state under the controlling of M controllers

A new quantum protocol to teleport an arbitrary unknown N-qubit entangled state from a sender to a fixed receiver under M controllers(M < N) is proposed. The quantum resources required are M non-maximally entangled Greenberger-Home-Zeilinger (GHZ) state and N-M non-maximally entangled Einstein-Podolsky-Rosen (EPR) pairs. The sender performs N generalized Bell-state measurements on the 2N particles. Controllers take M single-particle measurement along x-axis, and the receiver needs to introduce one auxiliary two-level particle to extract quantum information probabilistically with the fidelity unit if controllers cooperate with it.     

Diode-end-pumped Nd:YVO4/LBO lasers with 4.2W continuous-wave output at 457 nm

A continuous-wave (CW) 457 nm blue laser operating at the power of 4.2 W is demonstrated by using a fiber coupled laser diode module pumped Nd: YVO4 and using LBO as the intra-cavity SHG crystal With the optimization of laser cavity and crystal parameters, the laser operates at a very high efficiency. When the pumping power is about 31 W, the output at 457nm reaches 4.2 W, and the optical to optical conversion efficiency is about 13.5% accordingly. The stability of the out putpower is better than 1.2% for 8 h continuously working.     

Call for Papers

正Wireless Body-area Networks The last decade has witnessed the convergence of three giant worlds:electronics,computer science and telecommunications.The next decade should follow this convergence in most of our activities with the generalization of sensor networks.In particular with the progress in medicine,people live longer and the aging of population will push the development of wireless personal networks     

Call for Papers—Feature Topic,Vol.12,No.7,2015

正Information Centric Networking Information-Centric Networking(ICN) is an emerging direction in Future Internet architecture research,gaining significant tractions among academia and industry.Aiming to replace the conventional host-to-host communication model by a data-centric model,ICN treats data content as the first     

JOURNAL OF ELECTRONICS(CHINA)

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NEXT GENERATION WIRELESS NETWORKS–ESPECIALLY THE USE OF MILLI-MICROWAVE BANDS

The global bandwidth shortage of wireless communications has motivated the exploration of the naillimeter wave （ram-wave） frequency spectrum for the next generation wireless communications. Recent advances in RF CMOS technology and high speed baseband signal processing technologies have enabled tile extensive research and development of turn-wave wireless communications. The multi gigabit per second data rate of ram-wave system will lead to applications in many important scenarios, such as WPAN, WLAN,back-haul for cellular system. And the frequency bands include 28 GHz, 38 GHz, 45GHz, 60GHz, E-BAND and even beyond 100 GHz. The propagation and the imitation of the RF circuits design in these frequency bands make the directional antennas be inevitable for mm-wave communications.