基于点判决域的多模盲均衡算法及其FPGA实现

恒模算法无法克服信号的相位失真问题,且稳态误差大;修正恒模算法可以恢复信号相位,但均衡器收敛后,稳态误差依然很大。针对这一问题,提出一种基于点域判决的多模盲均衡算法,算法利用信号的判决值,在不增加设备复杂性的前提下,可以降低均衡器的稳态误差。给出了多模算法的FPGA设计方案,基于Xilinx公司的ISE设计平台,在Modelsim中进行了时序仿真分析,并和MATLAB仿真进行了比较。实验结果表明盲均衡器可以克服信号的幅度和相位失真,并且具有稳态误差低的优势。     

基于拟牛顿法的常模盲多用户检测算法

分析了传统常模算法及其改进算法的缺点,将线性约束和拟牛顿法应用于常模算法,提出了线性约束拟牛顿常模盲多用户检测算法,称之为LC-QNCMA。在DS-CDMA系统中,对本文算法与传统的线性约束常模算法(LC-CMA)及最小二乘常模算法(LSCMA)进行了仿真比较。结果表明,与LC-CMA相比,LC-QNCMA的信干比性能和误码性能方面有较大改善;与LSCMA算法相比,LC-QNC-MA算法复杂度有明显降低。本文算法在两种算法的优缺点中取得了较好的折中。     

面向物联网环境的高效通信接收机设计

物联网（IoT）通信系统具有活跃用户数低、数据帧短等特性,而信道估计和多用户识别所需的导频和用户识别码会显著降低IoT系统的通信效率和响应速度。针对上述问题,提出了一种基于非正交多址接入（NOMA）的盲信道估计和多用户检测算法。首先,利用码分多址（CDMA）系统中的扩频矩阵对每个用户所占用载波进行分配,并采用差分编码解决盲估计所引发的星座点旋转问题;其次,根据用户所分配载波具有的稀疏性,引入伯努利-高斯（B-G）分布作为先验分布,利用变量间的隐马尔可夫特性进行因式分解和建模,由用户数据的稀疏特征进行多用户识别;最后,应用消息传递算法对上述模型进行推导,解决NOMA引起的多用户干扰,得到面向IoT环境的联合信道估计和检测接收算法。仿真结果表明,相对于块稀疏单测量向量（BS-SMV）算法和块稀疏自适应子空间求解（BSASP）算法,所提算法能够在不提高复杂度的条件下获得约1 dB的性能增益。     

基于模因算法的多模盲均衡算法

由于常模盲均衡算法(Constant modulus blind equalization,CMA)收敛速度和均方误差都不甚理想,且对多模信号均衡时会发生相位旋转,本文提出了基于模因算法的多模盲均衡算法(Multi-modulus blind equalization algorithm based on memetic algorithm,MA-MMA)。该算法将多模盲均衡算法(Multi-modulus blind equalization algorithm,MMA)代价函数的倒数作为模因算法(Memetic algorithm,MA)的适应度函数,利用MA全局优化机制和局部深度搜索能力,在每次全局搜索后对全部新产生的个体进行局部深度搜索,将全局和局部搜索得到的最优个体解向量作为MMA的初始最优权向量。仿真结果表明,与传统的CMA,MMA以及基于遗传算法的多模盲均衡算法相比,MA-MMA 的收敛速度最快,稳态误差最小,输出信号星座图最清晰。     

低复杂度的分布式粒子滤波盲均衡算法

针对传统单接收节点盲均衡受信道影响大、误码率（BER）高的问题,提出了一种低复杂度的分布式粒子滤波盲均衡算法。该算法采用多个接收机组成无融合中心的分布式网络,使用分布式粒子滤波(DPF)合作估计共同的发送符号序列。为降低粒子采样复杂度,选用先验概率作为重要性函数;然后采用最小一致算法近似联合似然函数,从而使得每个接收节点的粒子权重达到一致。理论分析与仿真结果表明,该算法无需数据集中,具有更低的节点计算复杂度。全分布式的多接收机合作获得了空间分集增益,提高了系统误码性能。     

基于仲裁的SM9标识加密算法

SM9-IBE是我国于2016年发布的标识加密算法行业标准.标识加密算法以用户的标识(如邮件地址、身份证号等)作为公钥,从而降低系统管理用户密钥的复杂性.然而,标识加密算法的密钥撤销和更新问题却变得更加困难.此外,SM9算法的结构特殊使得已有技术无法完全适用于该算法.为此,本文提出一种基于仲裁的SM9标识加密算法,可快...     

基于非线性Volterra信道的复数神经多项式盲均衡算法

针对传统常模算法收敛速度慢、均方误差大以及传统神经网络参数多、复杂度高的问题,提出了基于非线性Volterra信道的复数神经多项式盲均衡算法(Fuzzy neural network-complex valued neural polynomial-constant modulus algorithm,FNN -CNP-CMA）。该算法包含单层神经网络和非线性处理器的复数神经多项式,模块结构简单、复杂度低。由模糊神经网络(Fuzzy neural network, FNN）设计的模糊规则控制器能有效提高步长的控制精度。仿真实验结果表明,该算法系统结构简单、复杂度低、收敛速度快且稳态误差小,较好地解决了收敛速度与均方误差之间存在的矛盾。     

适用于高阶QAM系统的双模式盲均衡算法

设计了一种适用于高阶QAM系统的双模式盲均衡算法,以具有冷启动能力的改进恒模算法(MCMA)作为基础模式,以稳态误差小的多模辅助算法(MAMA)作为辅助模式.MCMA始终对接收信号进行均衡,当正确判决概率较大时,MCMA再联合MAMA同时对抽头系数进行更新.以64QAM信号为研究对象进行信道均衡仿真.结果表明,该算法能...     

A two-stage Independent Component Analysis-based method for blind detection in CDMA systems

We propose an ICA-based method for blind detection of users in asynchronous DS-CDMA communications systems with multipaths channels with the only knowledge of the desired user?s code. The method can handle both the uplink and the downlink situations, since it does not require the synchronism between users. We convert the received cyclostationary signal into an observations vector that follows the ICA model with instantaneous mixture. The selection of the estimated source is carried out by means of the desired user?s code. Unlike previous works, we avoid to project the results after each iteration. Instead, we introduce a preprocessing based on a linear transformation of the data that enforces the extraction vector to lie in the desired user?s subspace. The detection is done in two stages. The second stage is a fine tuning in which the constraint is removed from the data in order to obtain more accurate results. Computer simulations show that the proposed method compares favorably with other well-known methods, in terms of mean-square error (MSE) of the output, symbol error rate and robustness against the near-far problem.     

Two versions of architectures for dynamic implied addressing mode

The complexity of today’s embedded applications increases with various requirements such as execution time, code size or power consumption. To satisfy these requirements for performance, efficient instruction set design is one of the important issues because an instruction customized for specific applications can make better performance than multiple instructions in aspect of fast execution time, decrease of code size, and low power consumption. Limited encoding space, however, does not allow adding application specific and complex instructions freely to the instruction set architecture. To resolve this problem, conventional architectures increases free space for encoding by trimming excessive bits required beyond the fixed word length. This approach however shows severe weakness in terms of the complexity of compiler, code size and execution time. In this paper, we propose a new instruction encoding scheme based on the dynamic implied addressing mode (DIAM) to resolve limited encoding space and side-effect by trimming. We report our two versions of architectures to support our DIAM-based approach. In the first version, we use a special on-chip memory to store extra encoding information. In the second version, we replace the memory by a small on-chip buffer along with a special instruction. We also suggest a code generation algorithm to fully utilize DIAM. In our experiment, the architecture augmented with DIAM shows about 8% code size reduction and 18% speed up on average, as compared to the basic architecture without DIAM.     

新的基于仿射投影算法的盲多用户检测器

多径衰落信道下的盲自适应多用户检测算法的运算复杂度通常都比较大,将基于仿射投影算法的盲多用户检测器与RAKE分集技术相结合,提出了一种盲自适应接收算法。该接收算法平衡了收敛速度和计算复杂度之间的矛盾,具有相对较好的收敛性能及较小的运算复杂度。通过模拟实验比较了几种算法的收敛、跟踪及误码性能,结果表明该方法具有明显的整体优势。     

Techniques for Edge Stratification of Complex Graph Drawings

We propose an approach that allows a user (e.g., an analyst) to explore a layout produced by any graph drawing algorithm, in order to reduce the visual complexity and clarify its presentation. Our approach is based on stratifying the drawing into layers with desired properties; to this aim, heuristics are presented. The produced layers can be explored and combined by the user to gradually acquire details. We present a user study to test the effectiveness of our approach. Furthermore, we performed an experimental analysis on popular force-directed graph drawing algorithms, in order to evaluate what is the algorithm that produces the smallest number of layers and if there is any correlation between the number of crossings and the number of layers of a graph layout. The proposed approach is useful to explore graph layouts, as confirmed by the presented user study. Furthermore, interesting considerations arise from the experimental evaluation, in particular, our results suggest that the number of layers of a graph layout may represent a reliable measure of its visual complexity. The algorithms presented in this paper can be effectively applied to graph layouts with a few hundreds of edges and vertices. For larger drawings that contain lots of crossings, the time complexity of our algorithms grows quadratically in the number of edges and more efficient techniques need to be devised. The proposed approach takes as input a layout produced by any graph drawing algorithm, therefore it can be applied in a variety of application domains. Several research directions can be explored to extend our framework and to devise new visualization paradigms to effectively present stratified drawings.     

均方误差控制步长恒模医学CT图像盲均衡算法

提出一种基于均方误差控制的自适应变步长恒模医学CT图像盲均衡算法,利用线性变换将图像的恢复过程等效为一维盲均衡运算,建立了降维处理的医学CT图像盲均衡恒模代价函数,采用信号的均方误差作为步长控制因子,加快算法收敛,改善恒模医学CT图像盲均衡算法性能。仿真结果验证了算法的有效性,新算法改善了峰值信噪比和恢复效果,提高了算法收敛速度。     

STBC-OFDM系统中有效降低计算量的盲接收机

为了降低空时分组码-正交频分复用（STBC-OFDM）系统中盲多用户接收机的计算复杂度,将基于子空间的接收机应用到STBC-OFDM系统中,证明其可以有效地降低计算复杂度并加快收敛速度。在此基础上,利用STBC码的正交特性推导出了两个权值之间的关系式,使得计算复杂度又降低了一半。仿真结果表明所提出的盲多用户接收机能剔除常规接收机中的冗余度,在误码率不变的前提下有效地解决了常规接收机中计算量大的问题。     

支持向量机初始化的常数模盲均衡算法仿真

为了提高常数模盲均衡算法的收敛速度并避免算法收敛至局部极小,提出了一种支持向量机初始化的常数模盲均衡算法.新算法采用一小段初始数据,利用支持向量机,将盲均衡问题转化为全局最优的支持向量回归问题,对盲均衡器的初始权向量进行设定,而后切换至计算量较小的常数模算法.采用浅海水声信道对新算法进行了计算机仿真,结果表明:支持向量机初始化阶段收敛速度快;切换至常数模算法后性能稳定.该算法适合应用于快衰落水声信道中通信数据的实时恢复.     

基于正交小波变换的分数间隔盲均衡算法

针对恒模算法(CMA)收敛速度慢,稳态误差大的缺点,在分析正交小波变换理论和分数间隔恒模算法(FSE-CMA)的基础上,提出了一种基于正交小波变换的分数间隔恒模盲均衡算法(WT-FSE-CMA)。该算法将正交小波变换理论引入到T/2分数间隔恒模盲均衡算法中,充分利用了小波变换对信号的去相关性,以及分数间隔均衡器对信号的过采样性质。与波特间隔、T/2分数间隔恒模盲均衡算法相比,该算法收敛速度快、稳态误差小,均衡效果较好。水声信道的仿真结果验证了该算法的性能。     

下含D2D蜂窝网基于有向加权二部图的资源分配

针对蜂窝下含D2D系统最多允许一条蜂窝链路和一条D2D对链路同时共占信道的场景,旨在设计一种低复杂度的资源分配算法。首先将以最大化系统吞吐量为目标的资源分配问题归结为整数规划问题。考虑到干扰是决定两条链路能否共占信道的关键因素,将最优化问题转化为以最小化干扰链路信道增益为目标的问题;该问题可看作一对一双偏好最优匹配问题,为此,首次提出有向加权二部图的相关概念,并用它对最优化问题建模。为了降低寻找最优匹配的难度,提出一种贪婪算法,该算法复杂度仅为O(n)。仿真表明,与加权二部图算法相比,所提算法不仅在复杂度方面下降两个数量级,而且在一定范围内得到的系统吞吐量与容量等性能比加权二部图算法略优。     

A low-cost decision-aided channel estimation method for Alamouti OSTBC

In wireless communication systems, channel state information (CSI) acquisition is typically performed at the receiver side every time a new frame is received, without taking into account whether it is really necessary or not. Considering the special case of the 2 × 1 Alamouti orthogonal space-time block code, this work proposes to reduce computational complexity associated with the CSI acquisition by including a decision rule to automatically determine the time instants when CSI must be again updated. Otherwise, a previous channel estimate is reused. The decision criterion has a very low computational complexity since it consists in computing the cross-correlation between preambles sent by the two transmit antennas. This allows us to obtain a considerable reduction on the complexity demanded by both supervised and unsupervised (blind) channel estimation algorithms. Such preambles do not penalize the spectral efficiency in the sense they are mandatory for frame detection as well as for time and frequency synchronization in current wireless communication systems.     

新的OFDM系统载波频偏盲估计算法

针对现有频偏估计算法复杂度高的问题,提出一种新的基于恒模信号的盲估计算法。假定信道变化缓慢,紧邻OFDM符号将产生相同信道响应,通过衡量它们之间的幅度差异,推导出一种目标函数,采用曲线拟合法对目标函数进行拟合并求得最小值时,即得到最接近真实值的CFO估计值。仿真结果表明,所提算法与现有PDE-F[1]算法和PDE-T[2]算法相比,提高了估计精度,降低了复杂度,尤其在窄带噪声和接收信号增益不断变化的环境下,该算法具有更好的适用性和稳定性。     

时变步长恒模医学CT图像盲均衡算法

提出一种基于误差信号峰度的自适应时变步长恒模医学CT图像盲均衡算法,通过降维处理将图像的恢复过程等效为一维盲均衡运算,构建行列等效变换的降维医学CT图像恒模盲均衡代价函数,利用误差信号峰度控制步长因子,加快算法的收敛速度。仿真结果表明,该算法能改善恢复效果,减小稳态剩余误差。