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1、Outline2012.6Chapter 0Basic elements of communication systems (p.2)Primary communication resources (p.3)The mobile radio channel (p.18)Block diagram of digital communication system (p.22)Shannon s information capacity theorem (p.23-24)Chapter 1Definition and basic concepts of random processStationar
2、y and non-stationaryMean, correlation, and covariance functions, the mean-square value and varianceThe concept of ergodic processTransmission of a random process through a linear time-invariant filterY (t )h( ) X (t)dThe mean, autocorrelation function, and mean-square value of Y Power spectral densi
3、tyDefinition (Equ. 1.38)Input-output relation (Equ. 1.39)Einstein-Wiener-Khintchine relations (Equ. 1.42, 1.43)PropertiesGaussian process (Equ. 80)Concept of white noiseRepresentation of narrowband noiseThe canonical form (Equ. 1.100)Properties of the in-phase and quadrature components (p. 65-66)Rep
4、resentation using envelop and phase components (Equ. 1.105-1.107)Basic concepts of Rayleigh distribution and Rician distributionUncorrelated and statistically independent (p.58)Uncorrelated: Covariance is 0Statistically independent: defined by joint probability density functionChapter 2Concepts of a
5、mplitude modulation and angle modulation (FM and PM)AMAM signal (Equ. 2.2 and Fig. 2.3), and the amplitude sensitivity ka Conditions of correct detection (p. 90)Spectrum of AM wave (Equ. 2.5 and Fig. 2.4)Transmission bandwidth BT = 2WVirtues and limitations of AMLinear modulation schemesThe general
6、form (Equ. 2.7)DSBDSB signal (Equ. 2.8 and Fig. 2.5)Spectrum of DSB wave (Equ. 2.9 and Fig. 2.6)Coherent receiverBasic knowledge of costas receiverBasic concept of quadrature-carrier multiplexing Basic concepts of SSB and VSBConcepts of mixer (Fig. 2.16)Concepts of FDMDefinitions of angle modulation
7、FMA nonlinear modulation processSingle-tone FM modulationDefinitions off,Basic knowledge of narrowband and wideband FM Transmission bandwidthCarson s rule (Equ. 2.55)Know the universal curveDemodulationFrequency demodulation (a direct method) (Fig. 2.30)Know phase-locked loop (an indirect method)Def
8、initions of SNRs(SNR)I, (SNR)O, and (SNR)CFigure of merit (Equ. 2.81)Comparison of figure of merits between DSB-SC (Equ. 2.88) and AM (Equ.2.95)Basic concepts of threshold effect of AM (p.138) and FM systems (p.149)Chapter 3SamplingDefinitions of the sampling period and sampling rateInstantaneous sa
9、mpling and the ideal sampled signal (Equ. 3.1-3.3, Fig.3.2)Derivation of the interpolation formula (Equ. 3.4-3.9)The sampling theorem and definitions of Nyquist rate and Nyquist intervalThe methods of combat aliasing effect (p.187)PAMThe difference between PAM and natural samplingThe concept of“ sam
10、ple and hold”The PAM signal (Equ. 3.10-3.19)The aperture effectKnow PPM and PDMQuantizationQuantization noise and (SNR)O of a uniform quantizer (Equ. 3.25-3.33)PCMBasic conceptsDiscrete in both time and amplitudeSampling, quantizing, and encodingNon-uniform quantizers-law and A-lawPiecewise linear a
11、pproximation to the companding circuit Five types of line codes and their waveformsDifferential encodingNoise in PCM systemsKnow that noise including channel noise and quantization noise, and that performance is essentially limited by the quantization noiseConcepts of TDM (Fig. 3.19)Know the basic c
12、oncept of digital hierarchy (p.214) and that the basic rate is 64 kbpsConcepts of DM and delta-sigma modulationConcepts of linear prediction and linear adaptive prediction DPCM and its processing gain (Equ. 3.82)Chapter 4Two sources of bit errors: ISI and noiseMatched filterFrequency response (Equ.
13、4.14) and impulse response (Equ. 4.16) Properties: the peak SNR dependents only on signal energy-to-noise psdratio at the filter inputError rate due to noiseDerivation of Equ. 4.35The complementary error function (Equ. 4.29)The result with equiprobable input signals (Equ. 4.38-4.40)The baseband data
14、 transmission system model (Fig. 4.7 and Equ. 4.44-4.48)Nyquist s criterionThe Nyquist s criterion (p.262)The ideal Nyquist channel (Equ. 4.54-4.56 and Fig. 4.8, 4.9)Raised cosine spectrum (Equ. 4.59, Fig. 4.10)The definition of and the bandwidth BT Correlative-level coding (partial response signali
15、ng)Duobinary signaling (class I partial response)Basic concepts (Fig. 4.11, 4.13, Equ. 4.66, 4.71)The concept of decision feedbackError-propagation and precodingGeneralized form of correlative-level codingBaseband M-ary PAM transmission (Equ. 4.84)ADSL (Fig. 4.26)Optimum linear receiverFor linear ch
16、annel with both ISI and noiseThe MMSE receiver (Equ. 4.110 and Fig. 4.27)Adaptive equalizationThe LMS algorithm (Equ. 4.114, 4.115)The basic concept of decision-feedback equalization (Fig. 4.32)Chapter 5Geometric representation of signals (Equ. 5.5-5.7 and Fig. 5.3)The vector form (Equ. 5.8) and def
17、initions of length, Euclidean distance, and angleGram-Schmidt orthogonalization procedureConversion of the continuous AWGN channel into a vector channel Basic formulations (Equ. 5.28-5.34)The vector representation represents sufficient statistics for detection Log-likelyhood functions for AWGN chann
18、el (Equ. 5.51)Maximum likelihood decodingThe concept of signal constellationThe maximum likelihood rule (Equ. 5.55), for AWGN channel, the rule is Equ. 5.59 and 5.61Equivalence of correlation and matched filter sampled at time T Probability of errorKnow the invariance to rotation and translationThe
19、concept of the minimum energy signalsKnow how to use union bound to derive a upper bound (p. 332 335)(Equ. 5.89)Know that there is, in general, no unique relationships between symbol error probabilities and BERChapter 6Basic concepts of keying and ASK, FSK, and PSKThe relationship between baseband a
20、nd passband power spectral density (Equ. 6.4)Bandwidth efficiency (Equ. 6.5)The passband transmission modelCoherent PSKBPSKBasic definitions (Equ. 6.8-6.14, Fig. 6.3)Error probability (Equ. 6.20)QPSKBasic definitions (Equ. 6.23-6.27)Error probability (Equ. 6.34, 6.38)Generation and detection (Fig. 6
21、.8)M-PSKBasic definitions (Equ. 6.46)Bandwidth efficiencyKnow that the power spectra of M-PSK has no discrete frequency componentM-QAMBasic definitions (Equ. 6.53-6.55)QAM square constellations (Fig. 6.17)Coherent FSKCoherent BFSKBasic definitions (Sunde s FSK) (Equ-6.91,.6.Fig86. 6.25)Error probabi
22、lity (Equ. 6.102)Know that the power spectra of BFSK has discrete frequency componentsMSKThe concept of CPFSKThe concept of MSKThe phase trellisSignal-space diagram (Fig. 6.29)Error probability (Equ. 6.127)Bandwidth efficiency of M-FSK signalsNoncoherent receivers (Fig. 6.37)The reason of envelop de
23、tection (Fig. 6.38)Error probability of noncoherent receiver (Equ. 6.163)Noncoherent BFSKReceiver structure (Fig. 6.42)Error probability (Equ. 6.181)DPSKBasic concepts (Fig. 6.43, 6.44)Error probability (Equ. 6.184)Comparison of digital modulation schemesRelationship among the error probabilities (Table 6.8 and Fig. 6.45)Bandwidth efficiencies of M-PSK, M-QAM, and M-FSK