Linear and non‐linear space diversity combining algorithms over fading channels in TDMA systems

This paper investigates the performances of various adaptive algorithms for space diversity combining in time division multiple access (TDMA) digital cellular mobile radio systems. Two linear adaptive algorithms are investigated, the least mean square (LMS) and the square root Kalman (SRK) algorithm. These algorithms are based on the minimization of the mean‐square error. However, the optimal performance can only be obtained using algorithms satisfying the minimum bit error rate (BER) criterion. This criterion can be satisfied using non‐linear signal processing techniques such as artificial neural networks. An artificial neural network combiner model is developed, based on the recurrent neural network (RNN) structure, trained using the real‐time recurrent learning (RTRL) algorithm. It is shown that, for channels characterized by Rician fading, the artificial neural network combiners based on the RNN structure are able to provide significant improvements in the BER performance in comparison with the linear techniques. In particular, improvements are evident in time‐varying channels dominated by inter‐symbol interference. Copyright © 2001 John Wiley & Sons, Ltd.     

基于全注意力机制的多步网络流量预测

预测资源分配能有效利用无线网络的剩余资源服务非实时业务,其中的关键问题之一是剩余资源的预测,可转化为实时业务流量预测问题。本文把面向自然语言处理提出的全注意力机制引入到时间序列预测问题中,预测未来分钟级时间窗内秒级的流量,通过在每秒记录的实测流量数据集上进行训练和测试,与其他基于循环神经网络和线性、非线性预测模型的方法在复杂度（由训练和测试时间衡量）、预测精度（由平均相对百分比误差衡量）和预测误差统计特性（由预测误差的均值和标准差衡量）等方面进行比较。研究结果表明,与无注意力机制的循环神经网络相比,所设计的基于全注意力机制的方法计算复杂度低,由于多步预测的累积误差,在预测精度方面增益不明显。      

基于粒子群优化的数字波束形成算法

波束形成的加权系数求解是一个优化过程,现有算法大多经过多次迭代,计算量大,实现复杂。为降低波束形成算法复杂度,将粒子群优化原理应用于数字波束形成中,提出了基于粒子群优化的自适应数字波束形成算法。该算法将每一组权值作为一个粒子,将阵列加权和的输出信号与干扰噪声比(SINR)作为适应度函数,通过比较各个粒子的适应度值,进行迭代搜索得到最优解。该算法可使天线阵在主波束对准有用信号,同时能有效抑制两个以上的干扰,且对阵列通道误差有较好的稳健性。计算机仿真验证了算法的有效性。     

基于离网格结合粒子群算法的稀疏阵列综合

针对大型稀疏阵列未知期望方向图的综合问题, 提出了一种基于离网格结合粒子群算法的方法综合稀布阵列单元位置以使增益最大化.该方法在粒子群算法的基础上, 再次在网格单元中进行梯度寻优, 能快速找到增益最大时对应的阵元位置且无需已知期望方向图.该算法对稀疏阵列综合效果良好, 增益提升显著且扫描过程中栅瓣抑制良好.通过对综合结果进行HFSS全波仿真对比, 数值仿真结果与综合结果基本一致, 证明了该方法的正确性.     

A new macromodeling approach for nonlinear microwave circuits basedon recurrent neural networks

A new macromodeling approach is developed in which a recurrent neural network (RNN) is trained to learn the dynamic responses of nonlinear microwave circuits. Input and output waveforms of the original circuit are used as training data. A training algorithm based on backpropagation through time is developed. Once trained, the RNN macromodel provides fast prediction of the full analog behavior of the original circuit, which can be useful for high-level simulation and optimization. Three practical examples of macromodeling a power amplifier, mixer, and MOSFET are used to demonstrate the validity of the proposed macromodeling approach     

时变信道中非理想反馈条件下波束成形的误码率分析

在基于信道信息有限反馈的无线多入单出系统中,发射机可采用简单的波束成形技术实现发射分集增益和阵列增益。已有的相关研究大多包含块衰落信道、准确信道估计或无反馈延迟等理想假设。该文建立了更为实际的Jakes时变信道中存在信道估计误差和反馈延迟的系统模型,分析了方形和矩形正交幅度调制星座图的平均误码率。研究表明:误码率的理论分析和仿真结果完全相符;增加反馈比特数可提高阵列增益,但不能增加分集增益;在慢变信道中,波束成形要显著优于正交空时分组码;误码率受信道估计误差和反馈延迟影响,且对后者尤为敏感。     

A posteriori real-time recurrent learning schemes for a recurrentneural network based nonlinear predictor

Recurrent neural networks (RNNs) are well established for the nonlinear and nonstationary signal prediction paradigm. Appropriate learning algorithms, such as the real-time recurrent learning (RTRL) algorithm, have been developed for that purpose. However, little is known about the RNN time-management policy. Here, insight is provided into the time-management of the RNN, and an a posteriori approach to the RNN based nonlinear signal prediction paradigm is offered. Based upon the chosen time-management policy, algorithms are developed, from the a priori learning-a priori error strategy through to the a posteriori learning-a posteriori error strategy. Compared with the a priori algorithms, the a posteriori algorithms offered are shown to provide a better prediction performance with little further expense in terms of computational complexity. Simulations undertaken on speech using the newly introduced algorithms confirm the theoretical results     

结合波束形成与发射分集的闭环下行发射方案

多天线技术是提高下行发射性能的一种有效方法。波束形成(BF)和发射分集(TD)均通过使用多个天线来有效地改善系统性能并提高信道容量。该文提出了一种将BF和TD结合用于闭环通信系统的自适应下行发射方案, 它包括适合于慢变信道的分集阵列方法和适合于快变信道的波束形成阵列方法。基站可以根据实际的信道状况自适应地调整发射方法。文中详细地介绍了自适应方案的实现算法,并对算法的复杂度和适应环境进行了分析。仿真结果证明新方案可以同时获得BF增益和TD增益,在使系统的误比特率(BER)性能大幅提高的同时,增强了系统对信道变化的适应能力。     

New improved fractional order integrators using PSO optimisation

This article investigates the optimal results of new improved fractional order integrators (FOIs) of different orders. Mathematical models of FOIs have been first developed by a single-step procedure of direct linear interpolation of fractional integrators based on Al-Alaoui operator in fractional domain itself, instead of using three steps of the well-known conventional method, namely, digital interpolation, series expansion and truncation. Later, these transfer functions (TFs) are optimised for their coefficient values for finding a minimum error function by particle swarm optimisation (PSO) algorithm. Simulation results of magnitude responses, phase responses and relative magnitude errors (dB) for all the proposed half integrators have validated the effectiveness of this new technique of interpolation of fractional order operators, mixed with PSO algorithm. A parallel comparison has been also drawn between the proposed optimised half integrators and those obtained by discretisation of PSO optimised integer order digital integrators (DIs) to properly support the proposed novel combination of interpolation and PSO, both applied together in fractional domain.     

基于粒子群优化算法思想的组合自适应滤波算法

根据粒子群优化(PSO)算法的社会心理学指导思想并结合自适应FIR滤波器的特点,设计了合适的惯性项、认知项与社会项表达式,并将之应用于组合自适应滤波器的子自适应滤波器更新中,提出了基于PSO算法思想的组合自适应滤波算法,分析了新算法的计算复杂度。理论分析与不同条件下的自适应系统辨识仿真结果表明,新算法可以在不明显提高计算量的条件下较好地平衡自适应滤波器的稳态失调与跟踪能力,其收敛性能优于其它几种较新的LMS算法。     

相关衰落MIMO信道下的联合天线子集选择

针对相关衰落MIMO信道下的联合天线子集选择,提出了2种选择算法。首先,在已知精确信道知识的情况下,选择算法选择以使接收数据流的SNR最大化和瞬时误差概率即误符号率最小化的天线子集,从而得到平均SNR增益改善的解析表达式;其次,当信道迅速变化时,在已知信道统计知识的情况下,选择算法选择以使全部可能信道实现的平均误差概率最小化的天线子集,从而得到编码增益,在极端相关信道条件下,也可以得到分集增益;基于2种选择算法的性能分析和仿真结果验证了本文提出算法的有效性。     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.     

一种改进的信号泄露噪声比多用户预编码算法

时分复用(TDD)系统通过信道互异性可以在发送端得到下行信道矩阵。由于上下行信道存在着处理延时,时变信道下,延时带来的信道误差会降低多用户MIMO预编码的系统性能。信道预测在某些情况下能够较好地改善信道误差,但预测误差又会给系统性能带来损失。该文针对预测误差带来的性能损失,提出了一种基于预测误差改进的预编码算法。改进算法根据预测误差的方差对预编码向量进行修正,能够进一步提高系统误码率性能。仿真结果表明,在高车速情况下,该算法比传统的预测预编码算法能带来更大的性能增益。     

基于微粒群算法的LS—SVM时间序列预测

将微粒群算法引入到最小二乘支持向量机(LS-SVM)时间序列预测,建立预测模型。思路来自微粒群算法可以在超平面空间中实现优化搜索,因此,将微粒群算法中的微粒运动公式进行修正,从而实现对LS-SVM的训练。然后用训练过的LS-SVM进行预测,即得到最终结果。将此方法应用于销售量预测,结果表明,此模型有更高的预测精度,而且在不同的LS-SVM学习参数下模型的误差相对稳定。     

STBC-VBLAST混合系统中基于GMD算法的预编码方案

为了获得最大信道容量,采用几何均值分解（GMD）将MIMO信道分解为若干相互独立且具有相同增益的并行子信道,从而使调制解调和编解码过程得到简化,在容量和差错率之间取得折中。将该方法与VBLAST接收机结合起来,在高信噪比下可以获得接近ML接收机的性能。为此,提出将GMD算法应用于STBC-VBLAST混合系统中VBLAST部分,仿真结果表明,该算法能够使混合系统的性能得以极大提高。     

智能天线阵列的误差分析建模与校准算法

分析了智能天线阵列的工作原理,在智能天线实验平台上对阵列误差的校正算法进行了实验验证。通过与方位无关的校正矩阵校正之后,各阵元通道的复增益趋于一致,波达方向（DOA）估计精度也在误差允许范围之内。结果表明,这种阵列误差校正方法是行之有效的。     

基于LS-SVM的光刻过程R2R预测控制方法

针对光刻过程非线性、时变和产品质量不易在线测量的特性,提出了一种基于最小二乘支持向量机预测模型和微粒群滚动优化方法的批次控制预测控制器。通过历史批次样本数据构建光刻过程的最小二乘支持向量机预测模型,解决了复杂光刻过程难以建立精确数学模型的难题,提高了预测模型的精度。通过预测误差的反馈校正和微粒群滚动优化算法求解最优控制律,提高了控制精度。性能分析结果表明,与指数加权移动平均方法及非线性模型预测控制方法相比较,批次控制预测控制器控制器减小了不同批次关键尺寸输出的差异,显著降低了关键尺寸输出的均方根误差,有效抑制了过程扰动影响。     

基于粒子群算法的下行CR-NOMA网络资源分配

将非正交多址接入(Non-orthogonal Multiple Access,NOMA)技术应用于认知无线电(Cognitive Radio,CR)次网络,使次用户的信号在功率域叠加,可以进一步提高次网络的吞吐量。为此,将粒子群算法(Particle Swarm Optimization,PSO)应用于底层模式的CR-NOMA网络进行资源分配,并分为子信道分配和功率分配两个步骤。在子信道分配中,使用结合遗传算法思想的粒子群算法提高算法的全局搜索能力。在此基础上,使用基于罚函数的粒子群算法对子信道功率和信道内用户功率进行分配。仿真结果表明,提出的基于粒子群算法的CR-NOMA网络资源分配相比以往算法能获得更高的次网络吞吐量。     

Channel estimation in ACO-OFDM employing different transforms for VLC

The pronounced eminency of assuring simultaneous illumination and communication has driven Visible Light Communication (VLC) to gain significant ubiquity in recent times. This paper proposes comb type pilot arrangement based channel estimation for Asymmetrically Clipped Optical OFDM (ACO-OFDM) and different multicarrier transmission systems like Discrete Hartley Transform (DHT)-based ACO-OFDM and Fast-Walsh Hadamard transform (FWHT)-based Hadamard Coded Modulation (HCM) over dispersive VLC channel. Various channel estimation algorithms like Least Square (LS), Minimum Mean Square Error (MMSE), and Interpolation techniques namely Linear, Spline, and Low-Pass are evaluated and compared for the aforesaid systems employing different orders of constellation. Here, an elaborate mathematical analysis is accomplished and Cramer Rao Lower Bound (CRLB) is derived for the channel estimation error. Simulated results emphasize that, ACO-OFDM and DHT-based ACO-OFDM have improved Bit Error Rate (BER) performance than HCM at lower Signal to Noise Ratio (SNR), while at higher SNRs HCM dominates the former. Furthermore, the simulated results evidences that, in all multicarrier systems MMSE algorithm has reducible probability of error than LS because, at higher SNRs LS is more susceptible to noise and Spline Interpolation outperforms both LS and MMSE. The simulated results are validated analytically demonstrating good agreement.     

基于BBO-MLP和纹理特征的图像分类算法

为了提高图像分类的准确率,解决多层感知器(MLP )收敛速度缓慢等问题,提出了一种基于生物地理学优化-MLP(BBO-MLP)和纹理特征的 图像分类算法。首先,从图像库中选取 3类不同的图片,对图像分类算法运行环境进行建模;其次,选取角二阶矩(U NI)、熵(CON)、惯性矩(ENT)和 相关性(CDR)4个纹理参数构建一个四维特征矩,根据用户提供的类别号和图像 的纹理特征向量 生成训练样本文件;然后,将提取的数据作为MLP的输入数据,为MLP定义一个评估栖息地的 误差适应度函数并对适应度函数进行全局优化,利用BBO算法训练MLP,得到分类模型;最后 ,利用训练好的MLP对图像进行分类,并引入二次反馈机制进一步提高算法性能。实验结果 表明,与PSO、GA、ACO、ES和PBIL等优化算法相比,本文的BBO-MLP算法具有较高的分类正 确率。