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用压缩感知技术来做信道估计，并与传统信道估计方法，LS,MMSE做比较。

使用stm32f334单片机HRTIMER产生BUCK电源电路的PWM和用于全桥逆变的SPWM波。

S7 200 OPC Server V1.0 PC Acceess 包含SP3

基于VLC（2.1.0）开发的MFC播放器，详情：http://www.cnblogs.com/luxiaoxun/p/3462153.html

5G无线通信系统关键技术（剑桥大学出版社） 2017年出版 对于5G所有最新技术进行了详细说明 很全的工具书Key Technologies for5G Wireless SystemsVINCENT W. S, WONGUniversity of British ColumbiaROBERT SCHOBERUniversity of Erlangen-NurembergDERRICK WING KWAN NGUniversity of New South WalesLI-CHUN WANGNational Chiao-Tung University即CAMBRIDGEUNIVERSITY PRESSCAMBRIDGEUNIVERSITY PRESSUniversity Printing House. Cambridge CB2 SBS. United KindomOne Liberty Plaza, 20h Floor New York, NY I(H0X, USA477 williamstown Road, port Melbourne, yic 3207 australia48424, 2nd Floor, Ansar Rod, Daryaganj. Delhi- I l4XH2, India79 Anson Road, #o6-(/ 00, Singapore 079%MCambridge University Press is part of the Lniversity of CambridgeIt furthers the University s mission by disseminating knowledge in the pursuit ofeducation, leaming and research at the highest international levels of excellence.www.cermbrid吧eInformtiononthistitlewww.cambridgeorg/978110713241810,1017③781316771655C Cambridge University Press 2017This puhlication is in copyright. Subjcct to sututonry exceptionand to the provisions of relewant collective licensing agreementsno reproduction of any part may take place without the writtenpermission of Cutmbridgre University Press.First published 2(117Printed in the United Kingdom by TJ International Ltd. Padstow, CornwallA catalogue recor for this pudlieafiove is aailable fromm the British LibraryLibrary of Congress Cataloging- in Pi hlicaiomz dataNames: Wong, Vincent W.S., editorTitle: Key technologies for 5G wireless systems/edited by Vincent W.S. Wong [and 3 otherOther titles key technologies for five g wireless svstemsDescription: Carmbrisige: New York, NY: Cambridge Lniversity Press, 2017.Identifiers: l CCN 2016045220)1 ISBN 9781 172418 (hardback)Subjects: LCSH: Wireless communication systems, I Machine-to-machinecommunications. Internet of things.Classitication: LCC TKs1032K49 2(17 DDC 621.38450-dc23LcrecordavailaBleathttps://lccnioc-gov/2016m5220)ISBN 978-1-107-17241- HardbackCambridge University Press has no responsibility for the persistence or accuracy ofURLs for extermal or third-party Internet websites referred to in this puhlication,and does not guarantee that any content on such websites is, or will remainaccurate of appropriateContentsList of Contributorspage xvIPrefaceKXIOverview of New Technolog ies for 5G SystemsVincent W S, Wong, Robert Schober, Derrick Wing Kwan Ng, and Li-Chun Wang1.1 Introduction1.2 Cloud Radio Access Networks1.3 Cloud Computing and Fog Computing1. 4 Non-orthogonal Multiple Access1. 5 Flexible Physical Layer Design334.4671. 6 Massive MIMo1. 7 Full-Duplex Communications1. 8 Millimeter wave1.9 Mobile Data Offloading, LTE-Unlicensed, and Smart Data Pricing131. 10 IoT M2M. and D2D1. I1 Radio Resource Management, Interference Mitigation, and Caching61. 12 Energy Harvesting Communications1. 13 Visible Light Communication19Acknowledgments20ReferencesPart I Communication Network Architectures for 5G Systems25Cloud Radio Access Networks for 5G Systems27Chih-Lin I, Jinn Huang, Xueyan Husang, Rongwved Ren, and Yami. Chen2.1 Rethinking the Fundamentals for 5G Systems272 User- Centric Networks2923 C-RAN Basics292.3.1 C-RAN Challenges Toward SGI302.4 Next Generation Fronthaul Interface (NGFI: The FH Solutionfor SGC-RAN312. 4.1 Proof-of-Concept Development of NGFI33Contents2.5 Proof-of-Concept Verification of Virtualized C-RAN2.5.1 Data packets3725.2 Test Procedure382.5.3 Test Results392. 6 Rethinking the Protocol Stack for C-RAN2.6.1 Motivation402.6.2 Multilevel Centralized and Distributed Protocol Stack402.7 Conclusion45AcknowledgmentsReferencesFronthaul-Aware Design for Cloud Radio Access Networks48Liang Liu, Wei Yu, and Osvaldo Simeone3. 1 Introduction483.2 Fronthaul-Aware Cooperative Transmission and Reception493. 2.1 Uplink513.2.2 Downlink573.3 Fronthaul-Aware Data Link and Physical layers61.3. I Uplink633.3.2 Downlink693.4 Conclusion73Acknowledgments74References74MobEdge computing76Ben Liang4.1 Introduction764.2 Mobile Edge Computing774.3 Reference architecture794.4 Benefits and Application Scenarios804 4.1 User-Oriented Use cases4. 4.2 Operator-Oriented Use Ca814 5 Research challenges824.5.1 Computation Offloading824.5.2 Communication Access to Computational Resources834.5.3 Multi-resource Schedulin844.5 4 Mobility Management854.5.5 Resource Allocation and Pricing4.5.6 Network functions virtualization864.5, 7 Security and Pri864.5.8 Integration with Emerging Technologies874.6 Conclusion88ReferencesContentsDecentralized Radio Resource Management for Dense HeterogeneousWireless networksAbolfazl Mehhodniya and Fumiyuki Adach5.1 Introduction925.2 System Model935.2.1 SINR Expression5.2.2 Load and Cost Function Expressions955.3 Joint BSCSA/UECSA ON/OFF Switching Scheme965.3.1 StrateTy Selection and Beacon Transmission53.2 UE AssocIation5.3.3 Proposed Channel Segregation Algorithms985.3.4 Mixed-Strategy Update3.4 Computer Simulation5.5 Conclusion104Acknowledgments04References105Part ll Physical Layer Communication Techniques107Non-Orthogonal Multiple Access(NOMA)for 5G Systems109Wei Llang, Zhiguo Ding, and H. Vincent Poor6.1 Introduction1106.2 NOMA in Single-Input Single-Output(SISO)Systems1126.2.1 The basics of nomaI126. 2. 2 Impact of User Pairing on NOMA136.2,3 Cognitive Radio Inspired NOMA6. 3 NOMA in MIMO Systems1206.3.1 System Model for MIMO-NOMA Schemes1216.3.2 Design of Precoding and Detection Matrices with Limited CSIT 1236.3.3 Design of Precoding and Detection Matrices with Perfect CSIT 1266.4 Summary and Future Directions128ReferencesFlexible Physical Layer Design133Maximilian Matthe, Martin Danneberg, Dan Zhang, and Gerhard Fettweis7.1 Introduction1337. 2 Generalized Frequency Division Multiplexing357.3 Software-Defined waveform1377. 3. 1 Time Domain Processing1387.3.2 Implementation Architecture1387.4 GFDM Receiver Design14174 Synchronization unit1427. 4.2 Channel Estimation Unit1474.3 MIMo-GFDM Detection Unit145Contents7.5 Summary and Outlook147Acknowledgments148References488Distributed Massive MIMO in Cellular Networks15IMichail Matthaiou and Shi Jin8. I Introduction15l8. 2 Massive MIMO: Basic Principles1528.2.1 Uplink Downlink Channel Models1538.2.2Favorable Propagation1548.3 Performance of Linear Receivers in a Massive MIMO Uplink1548.4 performance of linear precoders in a massive mimo downlink1578. s Channel estimation in massive mimo systems1588.5.1 Uplink Transmission1598.5.2 Downlink Transmission1608.6 Applications of Massive MIMO Technology1618.6.1 Full-Duplex Relaying with Massive Antenna Arrays1618.6.2 Joint Wireless Information Transfer and Energy Transfer forDistributed massive mimo1638.7 Open Future Research Directions1678. 8 Conclusionl68References169Full-Duplex Protocol Design for 5G Networks172Tanelf Ahonen and Risto wichman9.1 Introduction1729. 2 Basics of Full-Duplex Systems1739.2.1 In-Band Full-Duplex Operation Mode1739.2.2 Self-Interference and Co-channel Interference1749.2.3 Full-Duplex Transceivers in Communication Links1759. 2. 4 Other Applications of Full-Duplex Transceivers1789.3 Design of Full-Duplex Protocols1799.3, 1 Challenges and Opportunities in Full-Duplex Operation1799.3.2 Full-Duplex Communication Scenarios in 5G NetworksR9.4 Analysis of Full-Duplex Protocols1829.4.1 Operation Modes in Wideband Fading Channels1829. 4, 2 Full- Duplex Versus Half-Duplex in Wideband Transmission1849.5 Conclusion1849.5.1 Prospective Scientific Research DirectionsI849.5.2 Full-Duplex in Commercial 5G Networks185RLItrtncekl8610Millimeter Wave Communications for 5G Networks188Jiho Song, Miguel R Castellanos, and David J. LoweContentsⅸx10.1 Motivations and Opportunities18810.2 Millimeter Wave Radio Propagation18910. 2.1 Radio Attenuation1890. 2. 2. Free-Space Path LOSs19I10.2.3 Severe shadow19310.2 4 Millimeter Wave Channel model19310.2.5 Link Budget Analysis19410.3 Beamforming Architectures19510.3, Analog beamforming solutions19610.3.2 Hybrid Beamforming Solutions20010.3.3 Low-Resolution Receiver Architecture2010.4 Channel Acquisition Techniques20110.4.1 Subspace Sampling for Beam Alignment20210.4.2 Compressed Channel estimation Techniques20510.5 Deployment Challenges and Applications20710.5.1 EM Exposure at Millimeter Wave Frequencies20710.5.2 Heterogeneous and Small-Cell Networks208Acknowledgments209References209Interference Mitigation Techniques for Wireless Networks214Koralia N Pappi and George K, Karag annidis1 1.1 Introduction21411.2 The Interference Management Challenge in the 5G vision21411. 2. 1 The 5G Primary Goals and Their Impact on Interference2141 1.2.2 Enabling Technologies for Improving Network Efficiencyand Mitigating Interference21611.3 Improving the Cell-Edge User Experience: Coordinated Multipoint218I 1.3.1 Deployment Scenarios and Network Architecture2181 13. 2 CoMP Techniques for the Uplink22011.3.3 CoMP Techniques for the Downlink2211 1.4 Interference Alignment: Exploiting Signal Space Dimensions2231 1.4.1 The Concept of Linear Interference Alignment224L1. 4.2 The Example of the X-Channel225I 1. 4.3 The K-User Interference Channel and Cellular NetworksAsymptotic Interference Alignment22611.4.4 Cooperative Interferenee Networks22711.4.5 Insight from IA into the Capacity Limits of Wireless Networks 22711.5 Compute-and-Forward Protocol: Cooperation at the ReceiverSide for the Uplink22811.5.1 Encoding and Decoding of the CoF Protocol22811.5.2 Achievable-Rate Region and Integer Equation Selection23011.5.3 Advantages and Challenges of the CoF Protocol232IL6 Conclusion233References233

使用cordic算法实现了verilog求解对数的算法，仿真，工程均经过验证。为了节省资源，采用的是串行cordic的方式，并附上相关文献。

基于Matlab读取标准RINEX格式的GPS星历数据，采用Matlab直接读取Rinex文件张妮,等基于 Matlab读取标准 RINEX格式的GPS星历数据navdata(i). day str2num(line(10: 12));Le Edit yie seb finds Helpnavdata(i). hour=str2num (line(13: 15));navdata(i). minute str2num(line(16: 18))navdata (i).second str2num (line(19: 22));navdata(i). af0= str2num(line(23: 41))2:s10122-0041.4e012]【5.917e012]t30e95a-012navdata(i). afl str2num (line(42: 60));4.480-00navdata(i). af2 str2num (line(61: 79));4.600T+00619.8348←[6.419e007line fgetl(fid); %o second linem…2107007]navdata(i). aode str2num(line(4: 22))205-00【--007[7.2643c0090.9161【0.922】navdata().crs str2num(line(23: 41))navdata (i).dn= str2num(line(42: 60))图3读取星历数据结果navdata(i) Anomaly str2num (line(61: 79))Fig 3 Reading result of ephemeris dat4)关闭文件 status= fclose(fid)。骤,利用 Matlab矩阵的计算优势,很方便地计算不同时刻卫22星历数据的读取星的坐标,此外,还可方便查看卫星导航表层信息,判断导航星历数据的读取采用结构体数组显示的相关命令读取,数据的质量。若要获取某个卫星的相关参数可输入如示例命令:>>navdata(1)。获取结果如图2所示,为1号卫星参数读取结果3结论利用 Matlab以矩阵为单位进行计算的优势对 RINEX文件进行读取,较其他语言简单易行,结果精确,程序可移植性好,便于后续数据处理,同时还可利用 Matlab的仿真功能,实现卫星动态变化的实时模拟afl:1.47T9e-012参考文献:[1]陈东银,刘立龙,陈雷.GPS导航定位技术中面向对象读取 RINEX格式数据!测绘与空间地理信,2009(6):41-43.chen DorInCHen Lei. An ohmethod of reading rineX formatdat in GPS navigation tech-图21号卫星参数读取结果nology [J]. Geomatics and Spatial Information TechnologyFig. 2 Reading result of parameter of satliete NO.2009(6):41-43也可利用 Matlab元包数组,将数据存放并显示出来,具|2 I Gurtner W. RINEX: The receiver independent exchange for体实现代码如下mat: Version 2.10[M] Canada: Astronomical Institute UniverFN=fieldnamesnavdata)sity of Berne, 2002size=size(FN)3]孟广祥,郭标明.CPS接收机(OEM)二进制文件向 RINEXnavdata year;文件的转换[,测绘工程,2009(10:18-21.nav data=cell(oh+1);MENG Guang-xiang. Guo Biao-ming. The transformationsIzefrom GPS receiver (OEM) binary data to RINEX file[J]. Enfor i= 2: noph +1gineering of Surveyying and Mapping, 2009(10): 18-21onavcell=char(navdata year)[4]陈桂珍,戴建军.GPS-OEM原始数据向 RINEX格式转换nav_data(n, 1= char(FN(n));的方法[测绘技术装备,2006(4):26-27nav_data n, i= navdata(i-1).(char(FN(n)))CHENG Gui-zhen, DAI Jian-jun. The transformation fromGPS-OEM orignal data to RINEX Format[J]. Surveying techendnical equipment, 2006(4): 26-27读取结果如图3所示。5]张志涌.精通 Matlab6.5[M]北京:北京航空航天大学出版采用 Matlab软件读 RINEX导航文件,可以将文件所有社,2004的数据用矩阵保存,数据的显示精度不仅不会影响计算精6 Chapman Stephen J. Matlab Programming for engineers [M度,而且可以随时修改,并可根据卫星坐标的计算公式和步北京:科学出版社,200325基于Ma1ab读取标准RINX格式的GPS星历数据旧万据WANFANG DATA文献链接作者张妮,王标标, ZHANG NI, WANG Biao-biao作者单位张妮, ZHANG Ni(西安工业大学北方信息工程学院,陕西,西安,710025),王标标, WANG Biaobiao(中国人民解放军96275部队,河南洛阳,471003)刊名:电子设计工程sTc英文刊名ELECTRONIC DESIGN ENGINEERING年,卷(期):2010,18(8参考文献(6条)1. Chapman Stephen J Matlab Programming for engineers 20032.张志涌精通 Matlab6.520043.陈桂珍;戴建军 GPS-OEM原始数据向 RINEX格式转换的方法[期刊论文]测绘技术装备2006(044.孟广祥;郭标明GPS接收机(OEM二进制文件向 RINEX文件的转换2009(10)5. Gurtner W RINEX: The receiver independent exchange format: Version 2.10 2002陈东银;刘立龙;陈雷GPS导航定位技术中面向对象读取 RINEX格式数据2009(06)本文链接http://d.g.wanfangdata.comcn/periodiCaldzsjgc201008007.aspx

基于飞思卡尔MC9S12G128芯片的uds诊断代码，可以在FreeScale CodeWarrior编译器中，编译成功。烧写到板子上，通过上位机CANTest发送诊断报文，可以正确应答。

A*算法，A*（A-Star)算法是一种静态路网中求解最短路径最有效的直接搜索方法，也是解决许多搜索问题的有效算法。算法中的距离估算值与实际值越接近，最终搜索速度越快。该资源是用WinForm实现的，可以更直接明了的查看到寻路路径

【实例简介】恒润内部培训材料，基于CANape软件介绍、工程创建、测量标定