Key Technologies for 5G Wireless Systems
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
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匀速直线运动模糊图像复原的改进算法
一篇很好的关于模糊图像复原的论文，含详细需要的函数，讲解清楚易懂。闫永存,等匀速直线运动模糊图像复原的改进算法级的概率密度函数P(s)如下式所示:P(s)=P(rdr(6)8对于连续图像,当直方图均衡化(并归一化)后有P(s)即ds=p (r)dr =dT(r)3两边取积分得x=7(=P)h(8)式(8)就是所求的变换函数对于离散图像,假定数字图像中的总像索为N,灰度级总图3最优窗法区域分布图数为L,第k个灰度级的值为r,图像中具有灰度级r的像素Fig 3 Areal distribution of optimal window method数目为n,则该图像中灰度级n的像素出现的概率为取值范围为V-P+1,V1-11;在水平方向上,区域1、2、3的横P(r)="O≤r≤1;h=0,1,…(9)坐标取值范围1O,P1-2],区域4.8、9的横坐标取值范围为P3-1,V1-P3m,区域5、67的横坐标取值范围为[V1-Psn+对其进行均匀化处理的变换函数为:VL-11在图3中,每一个区域都有各白独立的边界,即各个S=7(r)=∑P(n)=2N子窗区域的尺寸不一定相同(10)最优窗计算公式为利用式(10)对图像做灰度变换,即可得到直方图均衡化后的图像。∑∑h(p,g)∑∑h(p.q)∑∑(p,q)D=O 0该方法可以将滤除高频噪声,提高有用信号的嗝度,增加∑h(p∑∑h(P)(12)对比度,同时缩小叠加噪声信号的动态范围,抑制振铃效应有效的结合起来,高文硕等证明了这一点。但不能完全去除振220)2A)铃效应,因此文中在滤波前用最优窗法对图像进行处理最优窗对模樹图像的边界进行加杈处理,以致像素值向3.4最优窗法外逐步过渡到零,其目的是待处理图像的边界结合处不会出在恢复图像过程中,由于图像边缘的像素没有足够的相现灰度的跳变,振铃效应因此得到抑制。邻像素可以利川,所以会导致恢复图像的边缘变差,并且整幅图像有明暗相间的条纹,即振铃效应。为了解决这个问题,早4实验结果及分析期学者常采用边界修正法,但效果不够令人满意。 Aghasi在1996年提出循环边界法,其缺点是图像尺寸变为原来的4文中通过实验验证了改进算法的可行性和有效性,以sadhna模糊图像的复原为例,图4(a)是原始图像,对其进行倍,运算量增加很多。基于循坼边界法的缺点, Bimetal提出了对二维模糊图像四进行恢复的最优窗。其具体实胞过程为:模糊加噪运算,模糊角度为53°,模糊长度为45,高斯噪声为0.0l图4(b)是降质后的模糊图像,采用本文算法估计岀的模恢复窗a,k将图像平面分成9个区域,每个区域编号如图3糊方向为51°,模糊长度为46,图4(c)是普通维纳滤波复原所示。标号为9的中央区域o:=1。图像,图4(d)是人工调整参数为真实值的复原结果。利用本区域1,8、7的纵坐标取值范围为[0,P-2],区城2,6、9文的改进算法得到的复原结果如图4()所示。实验结果如图的纵坐标取值范围为P-1,V4-P,区域3、4.5的纵坐标4所示。(a)原始图像(b)模糠图象(c)普通维纳波复原图象d)取冥际参数值的复原图像(e)太文算法的复原图(a)original image(t)blurred image (c)restored image of ordinary (d)res tored image fcr the (e)restored image ofiner filteringactua l )arameter valles algorithm in this pa per图4运动模糊图像及复原结杲Fig. 4 Motion blurred images and results of restoration147C1994-2012ChinaAcademicJournalElcctronicPublishingHousc.Allrightsrescrvcd.http://www.cnki.net电子设计工程》2012年第3期出实验结果可知,方向徵分法可以很人程度地提高模糊2005,10(5):590-595角度的估计准确性,利用自相关函效负尖峰值可以较准确地「31贺卫国,黎绍发,匀速直线运动模糊长度的精硝估计[鉴别出模糊长度,从而可以提高图像还原质量。最优窗法对振计算机应用,2005,25(6):1316-1320铃效应可以有较好的抑制作用,最后得到了复原效果较为理HIE Wei-guo, LI Shao-fa. Estimating the blurring length of想的图像。uniform linearmotion blurred images[J]. Computer Applications5结束语2005,25(6:1316-1320[4]吴振字.模糊图像复原方法研究[D]长沙:国防科学技术文中对运动模糊图像的退化模型、维纳滤波复原原理、点大学,2009扩散函数的求取过程选行了详细削述,提出了一种改进的模[5]高文硕.郊伟伟,杨磊运动模糊图像复原技术的改进算法糊图像复原算法,并对振铃效应进行处理,以 sadhna图像的中国传媒大学学报自然科学版,2010,7(1):72-76复原为例进行了实验验证结果表明,文中方法可以较准确地GAO Wen-shuo, ZHENG Wei-wei, YANG Lei. Improved估计出运动模糊参数,并且提高了运算速度,振铃效应得到有algorithm for restoration of the imagemotion blur IJ]. Journal效抑制。of Communication L niversity of China Science and Technology参考文献2010,17(1):72-76[1] Cannon M. Blind deconvolution of spatially invariant image [ 6] Aghdasi F, Ward R K. Reduction of boundary artifacts inblurs with phase [ J]. IEEE Trans on Acoustics, Speech andimage restoration[J]. IEEE Trans. Image. Proc. 1996. 5(4)signal Processing, 1976(24): 58-63611-6182]陈前荣,陆启生,成礼智,基于方向微分的迈动模糊方向鬥叶海.基于统计特征加权的模糊聚类方法及其应用鉴别!中国图象图形学报,2005,10(5):590-595现代电子技术,2009(1299-102chen Qiall-rong, LU Qi-sheng, CHENG Li-zhi. IdentificalionYE Hai-jun. Fuzzy clustering method and its applicationof motion blur direction from motion blurred image bybased on statistical characteristics weighting [J]. Moderndirection derivation method [J Journal of Image and GraphicsElectronics Technique, 2009(12): 99-102具ⅢP2优化和DC偏移消除的宽带丨/Q解调器可改善接收器性能加利福尼亚州米尔皮塔斯( MILPITAS,CA)推出超宽带宽直接转換lQ解调器LIC∶5S35,该件具卓越的线性性能(在1.95GIlκ时,ⅢP3-25.7dBm,IP2-60dBm)。LTC585能提供超过530MILz的基带输岀解调带宽,可满足新-代霓带LTE多模式接收器和毅宇预失真(DPD)接收器的带宽需。Q解调器在700WHx至3GHz的宽频率范围内二作,几乎覆盖了所有蜂窝基站频毁。这款器伫的独狩之处是两个内置的校准功能。其一是允许系统设计人员优化接收器IP2性能的高级也路以60dBm标称值提升至前所未有的80dBm或更高。另一个则是用于消除I和Q输出端上的DC偏移电压的片内电路。这两个功能电路均起到了増强接收嚣性能的作用。此外,LTC5585还可提供超卓的16 dBm pldB。为了进一步加强该器件在直接转换接收器应用中的使用,LTC5585捉供非常低的IQ幅度和相位失配ε幅度失配的典型值是0.05dB,而相位误差的典型值是0.7度,两个数值都是在1.95GHz频率上测得的。这两者的结合产生了一个43dB的接收器镜頰抑制能力。因为LTC5585能湜供非常宽的带宽,所以尢其适用于多模式LTE、 W-CDMA和TD- SCDMA基站DPD接收器以及于主收器的应門。尤其是对;DPD,这些最新一代基站正在将解调带宽推进到超过300MHz。LTC5585可以非常方便地配置应对这些带宽的挑战。除了无线基础设施应用,LrC5585还适用于军用接收器、宽蒂通信、点对点微波数据链路、镜频抑制接收器和长距离RFID阅读器。LTC5585内置了一个RF变压器以减少外部组件,再加上24引线4mm×4 mm QFN封装,因而可提供高度紧凑的解决方案。该器件规格在_40-105℃C的外壳工作温度范围。LTC585用单—5Ⅴ电源供电,吸取200mA的总电源电流。该器件提供数竽输λ以启用或停用该芯片。当俘用时,该IC吸取的典型溻电流为11μA。解调器的200ms快谜接通时间和800rs断开时间使该器件能在突发模弌接收器中使用。咨询编号:2012031009心·心;心·心,心·心普心心心·心·心心·心分·心心···心·心心心心·心·心心·心欢迎订阅2012年度《电子设计工程》(半月刊)国内邮发代号:52-142国际发行代号:M2996订价:15.00元/期360.00元/年148C1994-2012ChinaAcademicJournalElcctronicPublishingHousc.Allrightsrescrvcd.http://www.cnki.net
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