LTE-Advanced中协作多点传输技术研究

为了提高小区边缘用户的性能,满足小区边缘频谱效率的要求,LTE-Advanced系统中引入了协作多点传输技术CoMP（Coordinated Multi-Point）。CoMP技术是一种干扰消除技术,通过小区间的联合调度和协作传输,使小区边缘的CoMP用户的干扰信号变为有用信号,或降低来自邻小区的干扰水平,从而提高小区边缘吞吐量,并且增强系统性能。文章主要介绍了协作多点传输技术及其原理,对CoMP技术中的协作簇选择、CoMP用户划分、用户传输方案及预编码做出了介绍,通过仿真对比得到采用CoMP技术的系统性能增益,最后对协作多点传输技术作出了展望。     

基于部分频率复用的多小区协作方案及容量分析

针对无线蜂窝网中的小区间干扰问题,给出一种基于部分频率复用的多小区协作传输方案.该方案将小区用户划分为中心用户和边缘用户,中心用户采用频率复用因子为1的本地基站通信,边缘用户采用频率复用因子为3的多基站协作通信,并通过对多个小区的基站进行功率控制,达到抑制小区间干扰、提高系统容量的目的.数值分析表明,相对于传统多小区蜂窝系统(Traditional Multi-Cell System,TMCS)和广义分布式天线系统(Generalized Distributed Antenna System,GDAS),所提多小区协作蜂窝系统(Multi-Cell Cooperative System,MCCS)可以有效地克服小区间干扰,提高系统容量.     

上行中心式协作多点传输基站处理算法研究

在蜂窝通信系统中,小区间干扰是影响系统性能的关键因素.在上行传输系统中,通过基站协作接收可以把协作簇内的小区间干扰转化为有用的信号.基站协作需要通过骨干网交换大量的数据,当骨干网容量有限时,必须考虑对各基站接收信号先进行本地处理.本文用统一的框架研究四种本地处理方案,包括对各天线采样信号的直接量化、基于信源编码的单源压缩算法、考虑基站间接收信号相关性的分布式多源压缩算法、以及对部分基站实行本地解调等.我们将研究当采用这四种方案时骨干网容量约束与系统可达和数据率之间的关系,分析用户位置、收发端天线配置等参数对这些方案性能的影响,讨论他们的实现复杂度和适用场景.     

时分长期演进上行协作多点技术原理与实践

介绍了上行协作多点（CoMP）传输的技术原理以及如何实现协作小区多天线的宏分集接收,提高终端上行吞吐率.通过在TD-LTE（时分长期演进）大规模实验网中多场景下的实践,验证了上行CoMP技术能有效提升小区边缘终端的上行吞吐率.上行CoMP技术能有效抵抗网络干扰,扩大小区覆盖范围,提升用户的网络感知.     

LTE-A关键技术及标准化进展分析

LTE-A是LTE技术的平滑演进,引入了多载波聚合、中继、协作多点传输等关键技术。其中,多载波聚合是在频域上进行扩展,以满足LTE-A对高带宽的需求;中继技术能带来更广的覆盖范围和更高的系统容量,同时也面临干扰复杂化问题;协作多点传输技术能提高小区边缘的吞吐量;多天线技术通过增加上下行天线端口的数量,来提高峰值速率和频谱效率。     

一种适用于超密集网络下行链路的干扰抑制方案

在超密集网络（UDN）下行链路场景中,为了有效克服因节点间距减小、邻近节点传输损耗相差不大而产生多个强度相近的干扰源对用户性能的影响,提出了一种基于优先级以用户为中心的小区间干扰抑制方案。为了便于量化基站协作取得的带宽性能增益,采用了一种计算带宽盈亏率的定量分析指标。通过该指标,易于确定在特定UDN场景下干扰协调方案是否处于盈利状态,进而通过系统能够接受的最大损耗比优化参数配置。仿真结果表明,该干扰抑制方案能显著提升用户的信号干扰噪声比（SINR）性能,且能更好地保障用户公平性,有效地克服了用户SINR性能在基站数较多时受天线数限制的缺陷,为提升UDN网络用户性能和系统性能提供了一种解决方案。     

基于下行LTE-Advanced系统的协作多点传输技术研究

基于正交频分复用的LTE-Advanced系统具有对小区内干扰进行有效消除的作用,但是对于小区间干扰的消除能力较弱。协作多点传输技术(CoMP)可于上、下行对小区平均、边缘用户吞吐量进行分别提升,虽然该技术的应用会导致系统复杂度增加的问题,但其所具备的提高小区覆盖范围与容量的特点,依然值得我们给予它更多的关注。本次研究针对基于BBU+RRU模式的下行多用户MIMO(多输入输出)协作多点传输机制,给出了协作传输系统的模型并对接收信号检测进行了分析,系统级的仿真结果表明,新机制具有提高小区平均、边缘用户吞吐量的积极作用。     

MIMO时分蜂窝系统混合协作上行性能研究

下一代无线通信系统中,基站联合解码和移动终端协作传输是两种提高系统性能的重要技术。该文在线性时分蜂窝系统中,通过研究MIMO信道条件下混合协作可获得的单个小区上行可达传输速率,得出两种协作方式的相互作用和系统传输最小Eb/N0,并给出了小区联合解码与终端DF(解码前向)协作混合模式下的可达速率计算方法和最小Eb/N0的闭式解。最后,文中给出了MIMO衰减信道条件下的仿真结果,得出了多小区联合解码(宏分集)与终端协作性能的线性叠加关系,强临近小区干扰将会严重削弱用户与小区联合协作时终端协作作用的结论,验证了混合协作模式会极大提高系统传输性能。     

大尺度信干比共享下的基站协作干扰协调

在基于正交频分多址接入(OFDMA)技术的蜂窝移动小区中,小区间的干扰是影响系统性能的主要因素。多点协调(CoMP)技术被视为能够协调小区间干扰的主要手段。在下行多点协作传输系统中,小区基站采用三向天线来对小区划分扇区,从而消除了相邻小区边缘处的干扰。各扇区分别计算扇区内用户的大尺度信干比(SIR),小区之间通过共享大尺度信干比信息,对各自服务的用户按照一定的规则进行匹配,对小区中心用户的SIR和边缘用户的SIR进行了折中,从而有效解决小区边缘用户由于小区间干扰带来的低信干噪比(SINR)问题。仿真结果表明,本文提出的用户匹配算法以较小的反馈开销,较大的提高了小区边缘用户的信干噪比和系统吞吐量。      

异构网中基于BD的协作波束赋形算法研究

波束赋形是下一代无线蜂窝网中提高频谱效率的一项重要技术,文中在多用户波束赋形技术的研究基础上,研究了CoMP中的CB技术。针对LTE系统中的小区间干扰严重,且已有的干扰协调技术无法有效解决边缘用户频谱效率低的问题,LTE-A系统引入了多点协作传输/接收技术。该技术通过相邻小区间的联合处理或协作波束赋形,可抑制小区间干扰,提高用户的接收信号质量,使系统容量和小区边缘用户的频谱效率得到有效提高。     

Supervised learning‐based DDoS attacks detection: Tuning hyperparameters

Two supervised learning algorithms, a basic neural network and a long short‐term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.     

MEMS惯性传感器可靠性试验方法研究

MEMS惯性传感器在军事与商业应用中的一个主要问题便是可靠性试验方法尚未标准化,因而目前绝大多数MEMS惯性传感器器件的可靠性试验依据的是傲电子的试验标准。但是,这些标准对于这类器件的适用性却受到许多机构的质疑,国外关于该问题的研究也已起步。汇总了MEMS惯性传感器器件的结构和工作原理等信息,重点总结了该类器件的典型环境失效机理,并将典型的环境载荷情况与失效机理进行了对比分析：从现有的微电子可靠性试验标准中选取了针对不同环境失效机理的试验方法。并对其适用性问题进行了讨论。     

Subjective validation methods for analog integrated circuits’ metamodels using graphical displays of data

Metamodels are approximations to simulation models. They are built and validated using simulation results for samples of data points in the design variables space. Metamodels are more efficient to run compared to the simulation models they represent. They are validated to substantiate their accuracy using objective and/or subjective techniques. Objective validation methods based on various statistics such as root mean square RMS errors are often used. These methods require that certain statistical assumptions be satisfied by the data used in validation. Subjective validation methods are used, in particular, when some of these statistical assumptions are violated; for example, if the number of data points used is not sufficient. This paper presents and compares six different plot types that can be used to display data for subjective validation of metamodels, and demonstrates their usefulness as validation tools for analog circuits’ metamodels. These plots are easy to generate, using metamodel and simulation data. Furthermore, these methods are general and require no statistical assumptions for the data that can be displayed.     

Throughput and packet error probability of cellularfrequency-hopped spread-spectrum radio networks

The authors characterize multiple-access interference for cellular mobile networks, in which users are assumed to be Poisson-distributed in the plane and use frequency-hopped spread-spectrum signaling with a transmitter-oriented assignment of frequency-hopping patterns. Exact expressions for the bit error probabilities are derived for binary coherently demodulated systems without coding. Approximations for the packet-error probability are derived for coherent and noncoherent systems and these approximations are applied when forward-error-control coding is used. In all cases, the effects of varying interference power are accurately taken into account according to some propagation law. Numerical results are given in terms of bit-error probability for the exact case and throughput for the approximate analyses. Comparisons are made with previously derived bounds, and it is shown that these tend to be very pessimistic     

Robust estimators of the 2-parameter gamma distribution

The available estimators for parameters of the gamma distribution are moment estimators, maximum-likelihood estimators, and approximations to the maximum-likelihood estimators. These estimators are not suitable for small samples; however, they are still being used at the present time. The proposed robust estimators for scale and shape parameters are more suitable for small samples. They have RMS (root-mean-square) errors that are considerably smaller than those of the other estimators. In addition, they are easier to calculate, and are therefore appropriate in many applications     

MOS technology for VLSI

The scaling laws for MOS transistors are reviewed and the optimum performance predicted for both n-channel and p-channel devices are discussed. The physical and technological limitations for MOS VLSI are then described and some important technological challenges such as the implementation of new isolation techniques are pointed out. The mobility degragation effect due to velocity saturation is explained and illustrated by experimental data. The various limitations to the maximum operating voltage of scaleg devices are discussed. Finally, some considerations about speed and power consumption of scaled technologies are made.     

Automatic segmentation of left and right ventricles in cardiac MRI using 3D-ASM and deep learning

Segmentation of the left and right ventricles in cardiac MRI (Magnetic Resonance Imaging) is a prerequisite step for evaluating global and regional cardiac function. This work presents a novel and robust schema for MRI segmentation by combining the advantages of deep learning localization and 3D-ASM (3D Active Shape Model) restriction without any user interaction. Three fundamental techniques are exploited: (1) manual 2D contours are used to build distance maps to get 3D ground truth shape, (2) derived right ventricle points are employed to rotate the coarse initial shape for a refined bi-ventricle initial estimation, (3) segmentation results from deep learning are utilised to build distance maps for the 3D-ASM matching process to help image intensity modelling. The datasets used for experimenting the cine MRI data are 1000 cases from UK Biobank, 500 subjects are selected to train CNN (Convolution Neural Network) parameters, and the remaining 500 cases are adopted for validation. Specifically, cases are used to rebuild point distribution and image intensity models, and also utilized to train CNN. In addition, the left 500 cases are used to perform the validation experiments. For the segmentation of the RV (Right Ventricle) endocardial contour, LV (Left Ventricle) endo- and epicardial contours, overlap, Jaccard similarity index, Point-to-surface errors and cardiac functional parameters are calculated. Experimental results show that the proposed method has advantages over the previous approaches.     

Adaptive filters suitable for real-time spectral analysis

New cascade structures for adaptive filters are presented. They are especially suitable for real-time applications. Since the new structures are intended to be realized using single-chip DSP ICs or single-chip custom VLSI circuits, the requirements for memory and divisions are minimized. The new structures are based on state-variable biquads that in addition to having good SNRs and low sensitivities (for fixed-point implementations) can also have their resonant frequencies and Q-factors independently tuned. The special cases of using the adaptive filters for tracking sinusoids corrupted by noise and for formant based speech compression are described in detail.     

Compact-Modeling Solutions For Nanoscale Double-Gate and Gate-All-Around MOSFETs

Compact-modeling principles and solutions for nanoscale double-gate and gate-all-around MOSFETs are explained. The main challenges of compact modeling for these devices are addressed, and different approaches for describing the electrostatics, the transport mechanisms, and the high-frequency behavior are explained. Several approximations used to derive analytical solutions of Poisson's equation for doped and undoped devices are discussed, and the need for self-consistency with SchrÖdinger's equation and with the current continuity equation resulting from the transport models is addressed. Several techniques to extend the compact modeling to the high-frequency regime and to study the RF performance, including noise, are presented and discussed.