基于分布式基站的5G无线网络规划方案

随着科学技术的不断发展,移动通讯技术也取得了很大的进步,截至目前,移动通信网络已经经历了2G、3G、4G三代的发展,并开始向5G转变。而在一定时期内将会出现3G、4G与5G并存的局面,这样数量庞大的基站将会对网络建设与规划带来巨大的挑战。笔者根据我国的网络现状,对5G的推广与普及进行了分析,希望可以提高网络的利用效率,以满足人们对网络的使用需求。     

基于5G小基站的室内定位技术与应用研究

高精度室内定位技术与小基站通信网络结合能够有效降低室内定位的部署及运维成本,为各类定位应用场景提供了重要解决方案。首先归纳对比了常用定位技术,进一步分析了不同定位技术在小基站应用的可行性;然后,提出了基于5G小基站的室内定位方案,并对其架构进行了具体阐述,该方案具备传输网络易扩展、传输路径灵活化、定位接口开放化的优势;最后,给出了基于本方案选定的两种定位技术的应用场景及定位流程。结果表明,基于小基站的室内定位方案能够满足消费者领域及更加广泛的垂直行业的定位需求,具有重大意义及应用前景。     

基于数据-地理位置联合驱动的5G微基站最优分配策略

由于5G网络服务对峰值速率、时延和节点能耗效率的要求不断增加,为了高效地向用户提供资源,必须构建并解决所涉及的复杂优化问题,以便通过云网络分配资源,即通过高容量网络链路互连的分布式计算完成物理资源调度.通过5G微基站的最优部署,最优化分配网络资源(频谱、带宽、通信、计算等).5G微基站的密集部署保证了海量硬件通信设备的...     

基于AI的5G基站节能技术研究

为了响应国家节能减排号召,基站的节能降耗已成为当今通信领域的重中之重,各大运营商的节能技术也必成为核心竞争力。对基于AI的5G基站节能技术进行研究,分析了传统的基站节能技术,探究了结合AI技术的5G基站节能技术前景,深入研究了AI节能技术的发展方向,着重提出了AI节能模型和AI协同节能方案。     

基于大数据的变电工程全生命周期成本自动化辅助估算系统

变电工程全生命周期内涉及的费用类型复杂、属性多样,需要可靠的系统辅助成本估算。由于传统设计下的估算模型估算值与历史实际值之间相对误差偏大,因此设计一个基于大数据的变电工程全生命周期成本自动化辅助估算系统。在硬件方面,重新设计前端信号采集外围电路和ADC驱动电路。在软件方面,基于大数据处理方式,归一化变电工程成本指标,根据变电工程全生命周期成本划分,建立具有自动化估算性能的成本辅助估算模型。仿真测试结果表明,此次设计系统的总成本估算值与历史实际值之间的相对误差小于2%,说明系统的估算性能较好。     

5G大数据网络架构探讨

随着5G信息通信技术的发展,对各个行业的发展都起到了促进作用,我们的生活也因为网络而变得丰富多彩.为了使人们的生活更加便利,满足人们在网络时代的需要,科学工作从未停止过对网络科学的研究.本文主要对基于5G的大数据网络体系结构进行分析,以期为相关人员提供一些参考和建议.     

5G通信网络中大数据技术的应用研究

大数据是一种存储、分析、互联的技术手段,并且大数据已经应用于人们生活的方方面面。通过5G技术的分析可以得知,5G技术具有强大的协调性,并且使用频率较高,大数据结合5G通信技术能够使5G通信技术取得更大的发展,有助于5G通信技术的优化,改善了5G通信中天线较多信号互相干扰的问题。     

A hybrid cost estimation framework based on feature-oriented data mining approach

This paper presents an informatics framework to apply feature-based engineering concept for cost estimation supported with data mining algorithms. The purpose of this research work is to provide a practical procedure for more accurate cost estimation by using the commonly available manufacturing process data associated with ERP systems. The proposed method combines linear regression and data-mining techniques, leverages the unique strengths of the both, and creates a mechanism to discover cost features. The final estimation function takes the user’s confidence level over each member technique into consideration such that the application of the method can phase in gradually in reality by building up the data mining capability. A case study demonstrates the proposed framework and compares the results from empirical cost prediction and data mining. The case study results indicate that the combined method is flexible and promising for determining the costs of the example welding features. With the result comparison between the empirical prediction and five different data mining algorithms, the ANN algorithm shows to be the most accurate for welding operations.     

基于大数据的电力营销远程实时费控系统设计

针对传统电力营销远程实时费控系统存在实时控制效果差的问题,提出基于大数据的电力营销远程实时费控系统设计。构建数据分布式结构模型,采用分块特征采样方法,实现数据采集和样本组合,结合模糊信息特征检测方法完成信息的融合处理,根据直流电压谐波和电压波动的关联性检验结果,提取电力营销远程实时费控信息特征,实现数据的自适应分组匹配。在关联规则约束下,构建信息融合模型,引入综合性调度方法,实现对电力营销远程实时费用的优化控制。在嵌入式的DSP集成处理内核中实现对电力营销远程实时费控系统的硬件开发设计。实验结果表明,设计的电力营销远程实时费控系统智能性较好,自动控制能力较强。     

Software cost estimation with incomplete data

The construction of software cost estimation models remains an active topic of research. The basic premise of cost modeling is that a historical database of software project cost data can be used to develop a quantitative model to predict the cost of future projects. One of the difficulties faced by workers in this area is that many of these historical databases contain substantial amounts of missing data. Thus far, the common practice has been to ignore observations with missing data. In principle, such a practice can lead to gross biases and may be detrimental to the accuracy of cost estimation models. We describe an extensive simulation where we evaluate different techniques for dealing with missing data in the context of software cost modeling. Three techniques are evaluated: listwise deletion, mean imputation, and eight different types of hot-deck imputation. Our results indicate that all the missing data techniques perform well with small biases and high precision. This suggests that the simplest technique, listwise deletion, is a reasonable choice. However, this will not necessarily provide the best performance. Consistent best performance (minimal bias and highest precision) can be obtained by using hot-deck imputation with Euclidean distance and a z-score standardization     

Design of composite data acquisition gateway based on 5G network

The integrated data collection system hasn't changed much over the years. Their growth is dominated by digital filtering, and they get a new avionics bus. Time division multiplexing is still used to communicate with the system's data collector. These schemes make use of commands and data buses. While this method works, it has many drawbacks. These shortcomings are overwhelming the strict system architecture, system bandwidth limits, and expertise to only obtain an avionics bus, otherwise system bandwidth and one-way flow of data and control. This lead for high-end video recorders. To quietly monitor our actions and valuable information provides important warnings. However, it is usually completed by a device with this small processing power, which tracks the device, stores the data of the user's data, and is limited in processing. Therefore, it is important to store and process user data in the cloud to track 5 G network activity. This article proposes a simple 5G network gateway solution for custom user monitoring equipment. Collects charge data on Network 5G and Charging-aware multi-mode based routing protocol (CMRP). The CMRP routing mechanism is not yet stable, but the changes depend on the state of the energy charge sensor. This cannot be complemented by energy-efficient sensors and routing protocols that consume less energy.     

An accurate estimation algorithm for big data streams

Sketch is a memory-efficient data structure, and is used to store and query the frequency of any item in a given multiset. As it can achieve fast query and update, it has been applied to various fields. Different sketches have different advantages and disadvantages. Sketches are originally proposed for estimation of flow size in network measurement. The key factor of sketches for network measurement is the insertion speed and accuracy. In this paper, we propose a new sketch, which can significantly improve the insertion speed while improving the accuracy. Our key methods include on-chip/off-chip separation and partial update algorithm. Extensive experimental results show that our sketch significantly outperforms the state-of-the-art both in terms of accuracy and speed.     

Human health big data evaluation based on FPGA processor and big data decision algorithm

At present, the development of health care industry is also very vigorous and prosperous, and has become one of the most widely developed industries in the world. Medical centers and service centers in various regions have begun to transform from medical model to health care model. This field programmable gate array has great advantages in this respect, and it is also one of the principles of patient-centered nursing. With the vigorous development of machine learning, its application scope is more and more extensive, and its application in medicine is also very common. People use machine learning to process big data in the medical field. In order to better manage patient data and realize patient-centered, we must analyze a large number of health data. The traditional management tools are not enough to support the analysis of modern data. Therefore, we should use advanced big data processing technology for relevant data processing, and use updated tools to meet the current medical needs. The signal processing based big data evaluation is to be done through FPGA. The proposed system contains three process these process are executed through the machine learning based. The first process preprocessing is used eliminate the noise of the image or irrelevant data avoided. The second process feature selection based decision tree technique used and then after the final process classification stage based machine learning technique is used to analysis of the big data accuracy level. FPGA based machine technique used to achieve the better result of the proposed system.     

基于效能概念的成本参数估算模型的改进

传统的成本参数估算模型是以技术变量直接作为成本驱动因子,但是提取表征其技术先进性的参量难度较大。改进的参数估算法将效能概念引入建模过程,用作战效能变量代替了技术变量作为成本驱动因子。定义并计算了系统总体技术度,并在其基础上定义设计水平、先进程度和重新设计,改进的参数模型就是这3个新参数的函数。最后考虑了输入参数随机性对估算的影响,对模型进行了Monte Carlo仿真。改进后的参数估算模型层次可调,适用于各种复杂武器系统的成本估算。     

低成本嵌入式双GPS基准站系统设计

针对高精度GPS定位基准站系统多采用双基准站冗余设备,其系统成本高、缺乏基站独立性的特点,设计了一款低成本嵌入式双GPS基准站系统。该系统内置嵌入式处理器ARM9,选用2套GPS接收机板卡替代主用及备用GPS基准站设备,软件应用Linux操作系统。测试结果表明,该系统在完成播发差分信息的基础上,能根据2套GPS接收机板卡的定位状态自动进行切换,实现双机热冗余,设计主要降低了双基准站系统成本,同时也增加了双机冗余系统的独立性。     

Manufacturing cost estimation based on a deep-learning method

In the era of the mass customisation, rapid and accurate estimation of the manufacturing cost of different parts can improve the competitiveness of a product. Owing to the ever-changing functions, complex structure, and unusual complex processing links of the parts, the regression-model cost estimation method has difficulty establishing a complex mapping relationship in manufacturing. As a newly emerging technology, deep-learning methods have the ability to learn complex mapping relationships and high-level data features from a large number of data automatically. In this paper, two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) training images and voxel data methods for a cost estimation of a manufacturing process are proposed. Furthermore, the effects of different voxel resolutions, fine-tuning methods, and data volumes of the training CNN are investigated. It was found that compared to 2D CNN, 3D CNN exhibits excellent performance regarding the regression problem of a cost estimation and achieves a high application value.