标签: 信道估计

基于机器学习算法的船舶通信网络信道估计

高速移动通信系统中OTFS分数多普勒信道估计加窗研究.

车联网中的移动可见光通信多径信道估计研究

压缩感知超宽带信道估计中噪声抑制方法的研究

高速移动场景下的毫米波信道估计以及云接入网的研究

数字通信中的盲多径衰落信道估计与均衡

LTE上行信道估计和均衡技术的研究及DSP实现

TD-LTE系统下行信道估计和信号检测的分析与DSP实现

基于DSP的OFDM系统中的信道估计技术实现研究.

WiMAX信道估计插值研究与DSP实现

PROPAGATIONCHANNELCHARACTERIZATION,PARAMETERESTIMATIONANDMODELLINGFORWIRELESSCOMMUNICATIONS XuefengYin   TongjiUniversity,Shanghai,China XiangCheng   PekingUniversity,Beijing,ChinaContents1Introduction11.1BookObjective11.2TheHistoricalContext21.2.1ImportanceofChannelCharacterization21.2.2Single-input,Single-outputChannelModels21.2.3SpatialChannelModels(SCMs)51.2.4ChannelModelsfor5G61.2.5OtherKindsofChannelModel71.3BookOutline82CharacterizationofPropagationChannels152.1ThreePhenomenainWirelessChannels152.2PathLossandShadowing162.3MultipathFading182.4StochasticCharacterizationofMultipathFading222.4.1ReceivedEnvelopeandPhaseDistribution232.4.2EnvelopeLevelCrossRateandAverageFadeDuration242.4.3CorrelationFunctions242.5DualityofMultipathFading262.6WSSUSAssumptionofMultipathFading282.7AReviewofPropagationChannelModeling312.7.1ClassificationofMIMOChannelModels322.7.2ClassificationofV2VChannelModels35Bibliography383GenericChannelModels413.1ChannelSpreadFunction433.2Specular-pathModel463.3Dispersive-pathModel513.4Time-evolutionModel543.5PowerSpectralDensityModel573.6ModelforKeyholeChannel68Bibliography734Geometry-basedStochasticChannelModeling774.1GeneralModelingProcedure774.2Regular-shapedGeometry-basedStochasticModels794.2.1RS-GBSMsforConventionalCellularCommunicationSystems 4.2.2RS-GBSMsforV2VCommunicationSystems824.3Irregular-shapedGeometry-basedStochasticModels834.4SimulationModels844.4.1FilterSimulationModels864.4.2Sum-of-sinusoidsSimulationModels884.5SimulationModelsforNon-isotropicScatteringNarrowbandSISOV2VRayleighFadingChannels904.5.1ATwo-ringSISOV2VReferenceModel914.5.2SoSSimulationModels92Bibliography1035ChannelMeasurements1065.1Channel-soundingEquipment/System1075.2Post-processingofMeasurementData1095.3ImpactofPhaseNoiseandPossibleSolutions1105.3.1MitigatingPhaseNoise:theSlidingWindow1115.3.2MitigatingPhaseNoise:WhiteningandtheSAGEalgorithm1135.4DirectionalRadiationPatterns1175.5Switching-modeSelection1245.5.1Switching-modeforChannelSounding1255.5.2EstimationofDopplerFrequency1265.5.3AmbiguityinParameterEstimation1295.5.4CaseStudy:TDM-SIMOChannelSoundingwithaUniformLinearArray1305.5.5Switching-modeOptimization1325.5.6SimulationStudies134Bibliography1426DeterministicChannel-parameterEstimation1456.1BartlettBeamformer1466.2TheMUSICAlgorithm1486.3TheESPRITandPropagatorMethods1506.3.1ESPRIT1506.3.2ThePropagatorAlgorithm1526.4Maximum-likelihoodMethod1526.5TheSAGEAlgorithm1536.5.1Signalmodel1556.5.2TheSAGEAlgorithmDerived1596.5.3InitializationofParameterEstimatesforExecutingtheSAGEAlgorithm1676.6ABriefIntroductiontotheRiMAXAlgorithm1726.7Evidence-framework-basedAlgorithms1726.7.1Multi-levelEvidenceFramework1736.7.2ExampleI:ExponentialDecayusedinThree-levelEF1746.7.3ExampleII:DelaySpreadinaTwo-levelEF1766.8ExtendedKalman-filter-basedTrackingAlgorithm1786.8.1Overview1786.8.2TheStructureofanEKF1796.8.3ModelMismatchduetotheLinearApproximation1836.8.4TrackingPerformanceandtheInitialPhase1846.9Particle-filter-basedTrackingAlgorithm1886.9.1State-spaceModel1896.9.2ObservationModel1906.9.3TheProposedLow-complexityParticleFilter1906.9.4ExperimentalInvestigation193Bibliography1977StatisticalChannel-parameterEstimation2017.1ABriefReviewofDispersiveParameterEstimators2017.2DispersiveComponentEstimationAlgorithms2037.2.1EffectiveSignalModel2047.2.2Specular–ScattererModelEstimation2057.2.3First-orderGAMModelEstimation2077.2.4NominalAzimuthEstimators2087.2.5AzimuthSpreadEstimator2127.2.6SimulationStudies2137.3PSD-basedDispersiveComponentEstimation2187.4Bidirection-delay-DopplerFrequencyPSDEstimation2197.4.1ChannelPowerSpectrumEstimator2197.4.2MeasurementDataEvaluation225Bibliography2328Measurement-basedStatisticalChannelModeling2368.1GeneralModelingProcedures2378.1.1ChannelMeasurement2378.1.2ChannelParameterEstimation2388.1.3StochasticChannelModeling2408.2ClusteringAlgorithmbasedonSpecular-pathModels2418.2.1StochasticCluster-basedChannelModeling2428.2.2ClusteringAlgorithmsbasedonMultipathComponentDistance2438.3DataSegment-lengthSelection2458.4RelayandCoMPChannelModeling2498.4.1Introduction2498.4.2SSFCross-correlationandModelingMethodology2508.4.3MeasurementsforRelay-channelCharacterization2528.4.4ModelExtraction253Bibliography2579InPractice:ChannelModelingforModernCommunicationSystems2609.1ScenariosforV2VandCooperativeCommunications2609.1.1V2VCommunicationScenarios2609.1.2CooperativeCommunicationScenarios2639.2ChannelCharacteristics2649.2.1ChannelCharacteristicsofV2VCommunicationSystems2649.2.2ChannelCharacteristicsofCooperativeCommunicationSystems2649.3ScatteringTheoreticalChannelModelsforConventionalCellularMIMOSystems2659.3.1AWidebandMultiple-ring-basedMIMOChannelReferenceModel2669.3.2GenericSpace–Time–FrequencyCF2709.3.3MIMOSimulationModels2729.3.4NumericalResultsandAnalysis2759.3.5Summary2789.4ScatteringTheoreticalChannelModelsforV2VSystems2799.4.1ModelingandSimulationofMIMOV2VChannels:Narrowband2799.4.2ModelingandSimulationofMIMOV2VChannels:Wideband3049.5ScatteringTheoreticalChannelModelsforCooperativeMIMOSystems3299.5.1AUnifiedCooperativeMIMOChannel-modelFramework3309.5.2ANewMIMOGBSMforCooperativeRelaySystems3339.5.3Multi-linkSpatialCorrelationFunctions3399.5.4NumericalResultsandAnalysis3429.5.5Summary348Bibliography349AppendixA353

正交频分复用系统中的信道估计技术（英文版）

基于对称扩展DFT变换的OFDM系统信道估计方法

MIMO系统和广义多载波时分双工混台多址蜂窝系统中的信道估计器的设计

正交频分复用系统中的信道估计技术（英文版）[摘要]正交频分复用（OFDM）是多载波传输的一种特殊情况，它可以适应基于多媒体的无线系统的高数据速率要求。由于信道估计是OFDM系统的组成部分，因此理解信道的基础至关重要 OFDM系统的估计技术使得可以应用最适合的方法。在本文中，介绍了OFDM系统中使用的信道估计技术的广泛概述。

多用户ＯＦＤＭ系统基于压缩感知的信道估计中多导频集合设计研究

文中针对无线通信系统中稀疏信道估计算法进行研究，通过对比传统的基于训练序列的信道估计算法LS，对基于压缩感知的稀疏信道估计算法OMP进行分析。讨论了训练信号长度、信道稀疏度及噪声强度对整个估计性能的影响。在相同的实验条件下生成二维稀疏信号，从精确重构概率和信噪比方面对比了两种算法的性能。证明压缩感知方法可以有效的利用稀疏特性，在较短的训练序列情况下实现信道脉冲响应的精确估计。

为了进一步提高LMMSE信道估计算法在工程应用中的实用性和性能，提出了一种实时计算时延扩展的LMMSE的信道估计算法，并仿真完成了对其性能的验证。该算法在信号接收端，通过实时计算时延扩展代替取固定值，得到更准确的信道自相关矩阵RHH。同时使用解码产生出的软信息，通过反馈更新LMMSE信道估计HLMMSE表达式中的LS信道估计HLS，再次计算响应，从而综合提高对信道频率响应的估计准确度，还原发送信息。仿真结果表明，该算法在不显著增加计算复杂度的基础上，误码率比的多种固定值时延扩展的LMMSE算法估计都要小，达到了的提高实用性和性能的目的。