A Reduced Search Soft-Output Detection Algorithm and Its Application to Turbo-Equalization

To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.

A Novel Sequential Soft Output Viterbi Algorithm

In order to fully utilize the soft decision ability of the outer decoder in a concatenated system, reliability information (called soft output) from the inner decoder or equalizer is required. In this paper, based on the analysis of typical implementations of soft output VA, a novel algorithm is proposed by utilizing the property of Viterbi algorithm. Compared with the typical implementations, less processing expense is required by the new algorithm for weighting the hard decisions of VA. Meanwhile, simulation results show that, deterioration in performance of this algorithm is usually small for decoding of convolutional code and negligible for equalization.

Soft-output stack algorithm with lattice-reduction for MIMO detection

A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure. With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods.

基于Virtual Output Queued交换结构的最大权重匹配算法

信头阻塞（HOL）限制了采用FIFO输入队列交换机的吞吐率，而使用虚输出队列（VOQ）技术可以完全消除HOL阻塞。文章给出了VOQ的交换机模型，介绍了基于最大权重匹配的算法LQF、OCF、LPF及其性能，还描述了更加实用的并行迭代算法i－LQF、i－OCF和i－LPF。文章的结论对于构造高带宽的交换机具有实际意义。

Angle estimation in bistatic MIMO radar using improved reduced dimension Capon algorithm

This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the pro- posed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound （CRB） is also derived. Simulation results verify the usefulness of the proposed algorithm.

ANALYSIS OF THE LEAKY BUCKET OUTPUT PROCESSES FOR BURSTY SOURCES IN ATM NETWORKS

In ATM networks, bursty sources can be described as the Interrupted Bernoulli Process(IBP). With the use of the thin process theory, the Probability Generating Function(PGF) of the IBP is obtained. An iterative algorithm, which can be used to calculate the IBP probability distribution, is presented. The bursty source’s equivalent description is discussed. It is proposed that the leaky bucket output process can be approximately described as the IBP. The accuracy of the analytical results has been largely validated by means of the simulation approach. Moreover, how to improve its accuracy is discussed. The smoothing function of the leaky bucket algorithm is quantitatively analyzed.

基于改进K-means聚类的轨道交通基础设施分布式光伏发电典型场景生成及出力特性分析

受限于自然条件,光伏出力具有很强的随机性。为准确评估轨道交通基础设施分布式光伏发电的光伏出力特性,提出一种基于改进K-means聚类算法的轨道交通基础设施分布式光伏发电典型场景生成方法,并基于此进行光伏出力特性分析。首先,基于分布式光伏发电设施以及气象数据,利用PVsyst软件模拟光伏发电出力数据。然后,针对基本K-means聚类算法聚类参数和初始聚类中心盲目性高的问题,结合聚类有效性指标(Density based index,DBI)和层次聚类对其进行改进并利用改进K-means聚类算法生成光伏典型日出力场景。最后,基于华中地区某地轨道交通基础设施分布式光伏系统对所提方法的有效性和优越性进行验证,并通过定性和定量分析各典型场景的出力特性揭示轨道交通基础设施分布式光伏出力的规律和特点。

A Multiple Model Approach to Modeling Based on LPF Algorithm

Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic idea is to find out, from vast historical system input-output data sets, some data sets matching with the current working point, then to develop a local model using Local Polynomial Fitting (LPF) algorithm. With the change of working points, multiple local models are built, which realize the exact modeling for the global system. By comparing to other methods, the simulation results show good performance for its simple, effective and reliable estimation.

Performance Improvement of Multi-User Multiple-Input Multiple-Output Protocol for WLAN

The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.

基于模糊-遗传算法的粗纱机出料传动系统PID参数优化方法

目前常规的粗纱机出料传动系统PID参数优化方法主要通过结合被控对象的稳态增益情况构建出优化函数,实现PID参数优化整合,忽略了实际运行工况下的干扰成分,导致参数优化效果不佳。对此,提出基于模糊-遗传算法的粗纱机出料传动系统PID参数优化方法。首先结合PID的系统阶跃响应曲线,以被控量的偏差变化率作为主要参数选取性能指标;然后通过引入负荷扰动模块以及频率干扰模块,对PID调节模块进行建模分析;最后结合模糊-遗传算法对最优适应度值进行求解,从而实现PID参数的有效优化,并对提出的方法进行优化效果检验。最终测试结果表明,采用提出的方法对PID参数进行优化整合时,控制输出曲线与理论输出曲线的拟合程度更高,具备较为理想的优化效果。

Design and FPGA-Implementation of Minimum PED Based K-Best Algorithm in MIMO Detector

Minimum Partial Euclidean Distance (MPED) based K-best algorithm is proposed to detect the best signal for MIMO (Multiple Input Multiple Output) detector. It is based on Breadth-first search method. The proposed algorithm is independent of the number of transmitting/receiving antennas and constellation size. It provides a high throughput and reduced Bit Error Rate (BER) with the performance close to Maximum Likelihood Detection (MLD) method. The main innovations are the nodes that are expanded and visited based on MPED algorithm and it keeps track of finally selecting the best candidates at each cycle. It allows its complexity to scale linearly with the modulation order. Using Quadrature Amplitude Modulation (QAM) the complex domain input signals are modulated and are converted into wavelet packets and these packets are transmitted using Additive White Gaussian Noise (AWGN) channel. Then from the number of received signals the best signal is detected using MPED based K-best algorithm. It provides the exact best node solution with reduced complexity. The pipelined VLSI architecture is the best suited for implementation because the expansion and sorting cores are data driven. The proposed method is implemented targeting Xilinx Virtex 5 device for a 4 × 4, 64-QAM system and it achieves throughput of 1.1 Gbps. The results of resource utilization are tabulated and compared with the existing algorithms.

MIMO雷达分组正交波形集优化设计方法

在现代电子对抗中,数字射频存储(DRFM)设备能够快速截获机载脉冲多普勒雷达信号,能够实现对多输入多输出(MIMO)雷达的干扰。MIMO雷达可基于多组相互正交的波形集来对抗DRFM干扰。同时,为最大化MIMO雷达波形分集增益,每个脉冲内发射的波形也需要正交。为了平衡组内和组间的正交性,文中建立了一种分组正交波形集优化模型,其目标函数为组内和组间相关函数性能评估指标值的加权和;为了求解该优化问题,提出了一种分组正交波形集设计方法。所提方法将原优化问题简化为p-范数优化问题,基于MM算法导出了最小化目标函数的迭代求解表达式。仿真结果表明,所提方法可通过改变权重系数来灵活平衡MIMO雷达的干扰抑制性能和距离压缩性能。

基于ADASYN-XGBoost算法的光伏出力预测研究

随着光伏发电大规模并入电网,由此产生的源端可供电量不确定性问题日益突出,精准预测光伏出力,对电网资源优化配置、提升光伏消纳能力起着关键的支撑作用。通过研究影响光伏出力的关键要素,以历史气象特征数据为输入构建光伏出力预测模型,在实践过程中,对存在的主客观问题,从算法层面进行了模型优化。(1)针对光伏电站历史运行样本量小、气象特征变化多,导致诸多稀疏特征样本的问题,引入了ADASYN自适应采用算法,进行数据集重平衡;(2)通过XGBoost算法搭建了基于气象特征的光伏出力模型,并与传统的BP神经网络进行比较。通过某光伏电站的实际历史数据预测结果比较,结合ADASYN过采样和XGBoost算法,能有效提升模型的准确性;较BP神经网络相比,ADASYN-XGBoost算法的MAE、RMSE、MAPE和R2分别提高了66.7%、68.9%、58.0%和1.6%,评估指标明显优化。

H-infinity output feedback control for descriptor systems with delayed states

This paper considers the H-infinity dynamic output feedback control for descriptor systems with delay in states. The controller is a descriptor system without delay. Several equivalent sufficient conditions for the existence of one descriptor dynamic controller without impulsive models are given. Furthermore the explicit expression of the desired controller is obtained. The detailed design of the controller is presented using the cone complementarity linearization iterative algorithm and the LMI method. A ntumerical example is shown to illustrate the designed method.

基于区块链隐私保护的工业网络输出指令安全控制算法设计

当前工业网络输出指令加密机制多为目标式和独立式,导致指令安全控制效果不理想,丢包率增加,为此提出基于区块链隐私保护的工业网络输出指令安全控制算法。设计工业网络指令安全控制辨识目标,将此目标导入初始的安全指令控制加密程序中,设置一个基础的控制指令。以其作为引导,进行安全控制目标的扩展及生成,对指令的内容进行多层级加密。在指令加密处理的基础上,根据输出指令安全控制指标及参数的限制,设计不同的安全控制标准和双向控制条件,将安全控制辨识目标导入当前的区块链双向控制结构中,形成一个具体细化的控制结构。在控制结构中,利用区块链技术将区块控制程序与重叠指令的控制程序关联起来,实现动态化的指令输出安全控制。实验结果表明:这种安全控制算法能够对全部工业网络输出指令数据进行加密处理,能够最大程度上保证工业网络输出指令隐私安全,丢包率在5.2%以下,控制正确率较高,具有较高的实际应用价值。

基于改进FCM聚类的光伏电站出力场景特性研究

为提高新型电力系统的运行安全可靠性,需要对光伏电站的出力特性进行量化评估,并从高不确定性的众多发电场景中掌握光伏电站的运行规律。为此,基于场景聚类和缩减原理对光伏出力进行分析。首先提出光伏出力特性的评价指标,包括波动性和出力效率2个一级指标和相应的二级指标;然后采用模糊C均值(fuzzy C-means,FCM)聚类算法,基于密度思想和距离最大、最小原则确定初始聚类中心,通过不断迭代得到不同场景下光伏出力的聚类结果。考虑到传统聚类算法存在局部收敛性,难以确定最优聚类数目,提出结合聚类有效性指标来确定聚类最优数目,进而采用基于概率距离的前推回代法对得到的聚类结果进行场景削减,最终得到光伏电站季节典型出力场景。最后基于广东省某光伏电站实际出力数据进行分析,验证所提指标和算法的有效性。

基于IPSO-BPNN的楼宇屋顶光伏出力功率超短期预测

在建筑光伏一体化技术的背景下,准确预测屋顶光伏输出功率对于优化建筑能源管理和确保光伏电力的稳定并网至关重要。提出了一种基于IPSO-BPNN的楼宇屋顶光伏出力功率超短期预测模型,该模型引入Sine混沌序列初始化和精英粒子反向学习策略,改进了基本的PSO算法,并利用此算法对基本BPNN模型的超参数进行优化,从而实现了对屋顶光伏出力功率更加准确的预测。预测模型性能测试实验表明,所提出的IPSO-BPNN预测模型在不同季节的预测准确性和稳定性都有显著提高。该模型能够准确预测屋顶光伏发电功率,为建筑光伏一体化系统的稳定运行和能源管理提供切实可行的解决方案。

基于延迟线组合调相的高能效自适应混合预编码方案

针对毫米波大规模多输入多输出系统采用混合预编码技术所带来的高功耗影响,基于延迟线组合调相器件设计了一种间接全连接型混合预编码系统结构,并提出一种自适应连接的混合预编码方案,可大幅减少与天线直连的调相器数量从而降低硬件造成的功耗损失。方案将编码矩阵的求解分为数字编码矩阵、模拟编码矩阵和开关组成的连接交换矩阵3个部分进行交替优化;提出一种基于互补正交投影矩阵的算法来解决模拟编码矩阵的优化;利用功率约束条件和最小二乘算法简化数字编码矩阵的求解;针对连接交换矩阵的离散组合优化问题,利用低复杂度的交叉熵算法进行优化。仿真结果表明,所提方案可以保证系统性能在接近纯数字预编码方案的同时有效提高系统的能量效率。

基于改进的场景分类和去粗粒化MCMC的风电出力模拟方法

为实现风电出力时间序列的高性能模拟,文中提出了一种基于SAGA-KM(simulated annealing and genetic algorithms-K-means)算法实现典型风电场景分类和基于Copula函数进行风电日过程马尔可夫过程建模的风电模拟方法。SAGA-KM算法将传统KM算法与遗传算法和退火算法相结合,能显著提高风电场景分类效果;基于Copula函数建立的马尔可夫链精细概率模型,以去粗粒化方式实现马尔可夫过程蒙特卡洛模拟,克服了粗粒化引起的概率分布偏差。针对甘肃省某风电场数据进行实际模拟,结果表明文中方法生成模拟序列的统计分布特性、自相关函数特性和日均功率的分布特性与实测数据都非常接近,该方法能很好地保留风电序列的概率分布特性和随时间变化的波动特性,具有重要的工程实用价值。

An MMSE Decoding Algorithm without Matrix Inversion in QSTBC

The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding algorithm without matrix inversion is proposed, by which the computational complexity can be reduced directly but the decoding performance is not affected.