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88E1116R_Datasheet

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88E1116R_Datasheet,marvell以太网phy芯片手册,全本88E1116RM A RV E LL. Alaska Gigabit Ethernet TransceiverOVERVIEWFEATURESThe Alaska 88E1116R Gigabit Ethernet Transceiver is10/100/1000BASE-TIEEE 802.3 complianta physical layer device containing a single GigabitSupports reduced pin count GMII(RGMID)interfaceEthernet transceiver. The transceiver implements theFour RGMii timing modesEthernet physical layer portion of the 1000BASE-T,100BASE-TX. and 10base-t standards. t is manufacIntegrated mdi interface termination resistors thateliminate twelve passive componentstured using standard digital CMOS process and con-tains all the active circuitry required to implement theEnergy Detect and Energy Detect+ low powerphysical layer functions to transmit and receive data onmodesstandard Cat 5 unshielded twisted pairThree loopback modes for diagnosticsThe 88E1116R device has two regulators to generateDownshift"mode for two-pair cable installationsall required voltages. The 88E1116R device can beFully integrated digital adaptive equalizers, echopowered by a single 1.8V, 2.5V, or 3. 3V supply Alternacancellers, and crosstalk cancellerstively, if the regulators are not used, then the 88E1116RAdvanced digital baseline wander correctiondevice can be powered by a 1. 8v and 1.2V supplyAutomatic MDi/MDIX crossover at all speeds ofThe 88E1116R device incorporates the Marvell@ VirtualoperationCable Tester (VCTTM)feature, which uses TimeAutomatic polarity correctionDomain Reflectometry(TDR)technology for the remotelEEE 802. 3u compliant Auto-Negotiationidentification of potential cable malfunctions, thusSoftware programmable LEd modes including LEDreducing equipment returns and service calls. UsingtestingVCT, the alaska 88E1116R device detects and reportspotential cabling issues such as pair swaps, pair polar-Supports IEEE 1149.1 JTAGity and excessive pair skew. The device will also detectMDC/MDIO Management Interfacecable opens, shorts or any impedance mismatch in theCRC checker, packet countercable and reporting accurately within one meter the disPacket generationtance to the faultVirtual Cable Tester(VCT)The 88E1116R device integrates MDI interface terminaAuto-Calibration for MAc Interface outputstion resistors into the Phy. this resistor integrationComa Mode supportfacilitates board layout and reduces board cost byRequires a single 1.8v supplyreducing the number of extenal components. The new10 pads can be supplied with 1.8V, 2.5V, or 3. 3VMarvell calibrated resistor scheme will achieve andexceed the accuracy requirements of the IEEE 802.3Two regulators generate all required voltagesRegulator can be supplied with 1.8V,2.5V or 3.3Vreturn loss specificationsCommercial gradeThe 88E1116R device supports the reduced gmll64-Pin QFN package(RGMI)for direct connection to a MAC/Switch portThe 88E1116R device uses advanced mixed-signal processing to perform equalization, echo and crosstalkcancellation, data recovery, and error correction at agigabit per second data rate. The device achievesrobust performance in noisy environments with very lowpower dissipationThe 88E 1116R device is offered in a 64-pin QFn pack-The 88E1116R device is footprint compatible with the88E1116 device As the 88E 1116R device employs integrated MDi interface terminations, all external mDIinterface termination resistors and capacitors must beremoved when migrating from the 88E1116 to88E1116R. See 88E1116 to 88E1116R Migration Appli-cation note for more detailsCopyright o 2007 MarvellCONFIDENTIALDoC. No. MV-S104224-00. Rev.March 1. 2007. AdvanceDocument Classification: Proprietary InformationPage 388E1116RMARVELLo Alaska Gigabit Ethernet TransceiverMagnMedia Types10/1001000Mbps88E1116R|a盖10BASEEthernet macRJ-45Device100BASE-TX1000BASE-TMAC InterfaceRGMII88E1116R Device used in Copper ApplicationDoc. No. MV-S104224-00. Rev.CONFIDENTIALCopyright o 2007 MarvellPage 4Document Classification: Proprietary InformationMarch 1. 2007. AdvanceTable of contentsSECTION 1. SIGNAL DESCRIPTION1.1 Pin Description101.1.1 Pin Type Definitions1264 Pin QFN Pin Assignment List- Alphabetical by Signal Name.……,…,,…,161.3 O State at Various Test or reset modes .mmm.,17SECtION 2. FUNCTIONAL SPECIFICATIONS2.1 Copper Media Interface..国面画192.2 MAC Interface(RGMII)4192.2.1 10/100 Mbps Functionality2.2.2 TX ER and RX ER Codingaaaaaiiaia t23Lo。 pback……………,….….….,.,…….…,…,….….……,…….……………212.3.1 MAC Interface Loopback212.3.2 Line Loopback.222.3.3 EXternal Loopback24 Synchronizing F|FQ….…,,…,…,,,,,,…,,,,,…,,,…,,,,…,……242.5 Copper Media Transmit and receive Function.man..m日a252.5.1 Transmit side Network Interface252.5.2 Encoder2.5.3 Receive Side Network Interface2.5. 4 Decoder2.6 Regulators and Power Supplies282.6.1 AVDD2.6.2 AVDDC282.6.3 AVDDR292.6.4 AVDDX2.6.5DVDD…292.6.6 VDDO26.7 VDDOR.292.7 Power Management302.7.1 Low Power Modes2.72 Low Power Operating Modes……2.7.3 RGMl Effect on Low Power modes3228Auto- Negotiation.........……33Copyright o 2007 MarvellCONFIDENTIALDoC. No. MV-S104224-00. Rev.March 1. 2007. AdvanceDocument Classification: Proprietary InformationPage 588E1116RMARVELL Alaska Gigabit Ethernet Transceiver2.9 Downshift Feature…352.10 Advanced virtual Cable Tester362.10.1 Maximum Pe2.10.2 First Peak372.10.3 Offsetp2. 10. 4 Sample Poin2.10.5 Pulse Amplitude and Pulse Width392. 10.6 Drop Link...392.10.7 VCTTM With Link Up392.11 Data Terminal Equipment (DTE)Detect........2.12 CRC Error Counter and frame Counter412.12. 1 Enabling the crc error counter and frame counter.412.13 Packet generator412.14 MDI/MDIX Crossover422.15P。 olarity Correction..…432.16LED,,,,,,,,,,,…,…,,442.16.1 LED Polarity452.16.2 Pulse Stretching and Blinking.462. 16.3 Bi-Color LED Mixing472.16.4 Modes of Operation482.17 EEE 1149.1 Controller522.17.1 BYPASS Instruction522.17.2 SAMPLE/PRELOAD Instruction.52217.3 EXTEST Instruction552,17.4 The clamP Instruction552,17.5 The high-z Instruction552.17.6 ID CODE Instruction552.18 Interrupt.552.19 Automatic and Manual Impedance Calibration.……,…,…,…,…,…,…,……562. 19. MAC Interface calibration circuit562.19.2 MAC Interface Calibration Register Definitions2. 19.3 Changing Auto Calibration Targets2. 19. 4 Manual Settings to The Calibration Registers“““582.20 Configuring the 88E1116R Device..2.20. 1 Hardware Configuration612.20.2 Software Configuration-Management Interface632.21 Temperature sensor64Doc. No. MV-S104224-00. Rev.CONFIDENTIALCopyright o 2007 MarvellDocument Classification: Proprietary InformationMarch 1. 2007. AdvanceSECTION 3 REGISTER DESCRIPTION65SECTION 4, ELECTRICAL SPECIFICATIONS1104.1. Absolute Maximum Ratings,…,…,…,…,,…,…,…,…,…,…,,…,…,……,1104.2. Recommended Operating Conditions..,,.,……,,……1114.3. Package Thermal Information.………….……….…………1124.3.1 Thermal Conditions for 64-pin QFn Package1124. 4. Current Consumption...........面量量…1134.4.1 Current Consumption AVDD..1134.4.2 Current Consumption AVDDC..1134.4.3 Current Consumption AVDDR1144.4.4 Current Consumption AVDDX1144.4.5 Current Consumption DVDD4.4.6 Current Consumption VDDo1154.4.7 Current Consumption VDDOR1154.4.8 Current Consumption Center Tap1154.5. DC Operating Conditions1164.5.1 Non-RGMlI Digital Pins1164.5.2 Internal resistor Description4.5.3 Stub-Series Transceiver LogIc (55/.21174.5 4 EEE DC Transceiver Parameters1194.6. AC Electrical Specifications1204.6.1 Reset Timing ..1204.6.2 XTAL IN/XTAL OUT Timing1214.6.3 LED to CONFIG Timing1214.7 RGMII Interface Timing……,,…1224.7.1 RGMl AC Characteristics4.7.2 RGMII Delay Timing for different RGMiI Modes1234.8. MDC/MDIO Timing…12549. JTAG Timing…,,…1264.10.EEE AC Transceiver parameters1274.11. Latency Timing........….…1284.11.1 RGMII to 1000BASE-T Transmit Latency Timingaa“aa1284.11.2 RGMII to 100BASE-TX Transmit Latency Timing1284.11.3 RGMiI to 10BASE-T Transmit Latency Timing4. 11. 4 1000BASE-T to RGMll Receive Latency Timing1304. 11.5 100BASE-TX to RGMII Receive Latency Timing.1304.11.610 BASE-T to RGMll Receive Latency Timing……….….…………,130SECTION 5. PACKAGE MECHANICAL DIMENSIONS1315.1 64-Pin QFN Package...131Copyright o 2007 MarvellCONFIDENTIALDoC. No. MV-S104224-00. Rev.March 1. 2007. AdvanceDocument Classification: Proprietary InformationPage 788E1116RMARVELL Alaska Gigabit Ethernet TransceiverSECTION 6. ORDER INFORMATION1336.1 Ordering Part Numbers and Package Markings1336.1.1 RoHS 5/6 Marking Example1346.1.2 RoHS 6/6 Marking Example135Doc. No. MV-S104224-00. Rev.CONFIDENTIALCopyright o 2007 MarvellPage 8Document Classification: Proprietary InformationMarch 1. 2007. AdvanceSignal DescriptionSection 1. Signal DescriptionThe 88E1116R device is a 10/100/1000BASE-T Gigabit Ethernet transceiverFigure 1: 88E1116R Device 64-Pin QFN Package(Top view)文gg9廿廿廿廿廿廿凵廿廿廿凵廿守令导好寸守哥导$85#將RX CTRL4932TSTPTRXDIO5031MDIPIORXD[51EPAD-VSS30d MDIN[O]VDDOR52290 AVDDRX CLK5328叫NCRXD[2]54AVDDRXD]5526MD|P[1VDDOR56VREF57MARVEL L③24E MDIP[2TXD0]□5823MDIN[2TXD[1]B5988E1116R22AVDDTX_CLK F6021AVDDTXD[2Top ViewMDIP[3TXD3]□62190 MDIN[3]TⅩCTRL6318□NCCONFIG[O]64CTRL18三s回口cc×O百口口艺艺安安Copyright o 2007 MarvellCONFIDENTIALDoc. No. MV-S104224-00 RevMarch1.2007. AdvanceDocument Classification: Proprietary InformationPage 988E1116RMARVELL. Alaska Gigabit Ethernet Transceiver1.1 Pin Description1.1.1 Pin Type DefinitionsPin Ty peDefinitionHInput with hysteresisVOInput and outputInput onlOutput onlPUIntemal pullPDInternal pull downOpen drain outputTri-state outputADC sink capabilityDoC. No. MV-S104224-00 RevCONFIDENTIALCopyright o 2007 MarvellPage 10Document Classification: Proprietary InformationMarch.2007. Advance

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    凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer 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game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. 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