digital signal processing pdf

... Rao, C.R., Effects of estimated noise covariance matrix in optimal signal detection, IEEE Trans. on Acoustics, Speech, and Signal Processing, ASSP-35(5), 671–679, May 1987. [21] Cai, L.andWang, ... algorithm, Proc. Signal and Data Processing of Small Targets, SPIE Proc. Series, Vol. 1305, Paper 16, 180–192, Orlando, FL, April 16-18, 1990. [27] Wang, H. and Cai, L., On adaptive multiband signal detection ... May 1991. [16] DiPietro, R.C., Extended factored space-time processing for airborne radar systems, Proc. 26th Asilomar Conference on Signals, Systems, and Computers, 425–430, Pacific Grove, CA,...

... IntroductionandMotivation Thischapterreviewstheapplicationsofantennaarraysignalprocessingtomobilenetworks.Cellular networksarerapidlygrowingaroundtheworldandanumberofemergingtechnologiesareseentobe criticaltotheirimprovedeconomicsandperformance.Amongtheseistheuseofmultipleantennas andspatialsignalprocessingatthebasestation.ThistechnologyisreferredtoasSmartAntennasor, moreaccurately,asSpace-TimeProcessing(STP).STPreferstoprocessingtheantennaoutputsin bothspaceandtimetomaximizesignalquality. Acellulararchitectureisusedinanumberofmobile/portablecommunicationsapplications.Cell sizesmayrangefromlargemacrocells,whichservehighspeedmobiles,tosmallermicrocellsorvery smallpicocells,whicharedesignedforoutdoorandindoorapplications.Eachoftheseoffersdifferent channelcharacteristicsand,therefore,posesdifferentchallengesforSTP.Likewise,differentservice deliverygoalssuchasgradeofserviceandtypeofservice:voice,data,orvideo,alsoneedspecic STPsolutions.STPprovidesthreeprocessingleverages.Therstisarraygain.Multipleantennas capturemoresignalenergy,whichcanbecombinedtoimprovethesignal-to-noiseratio(SNR).Next isspatialdiversitytocombatspace-selectivefading.Finally,STPcanreduceco-channel,adjacent channel,andinter-symbolinterference. Theorganizationofthischapterisasfollows.InSection68.2,wedescribethevectorchannelmodel forabasestationantennaarray.InSection68.3wediscussthealgorithmsforSTP.Section68.4outlines theapplicationsofSTPincellularnetworks.Finally,weconcludewithasummaryinSection68.5. c 1999byCRCPressLLC ... the blind identification of multichannel FIR filters, IEEE Trans. on Signal Processing, 1995. [29] Orfanidis, S.J., Optimal Signal Processing – An Introduction, Macmillan Publishing Co., New York, ... constant modulus signals: a bilinear approach, in Proc. VII European Signal Processing Conference, Edinburgh, Scotland, Sept. 13-16, 1994. [33] Porat, B. and Friedlander, B., Blind equalization of digital...

... extending this concept to the signal subspace [8]. By sphericalizing and deflating both the signal and the noise subspaces, the cost of tracking the r dimensional signal (or noise) subspace is ... 1995. [35] Reddy, V.U., Mathew, G. and Paulraj, A., Some algorithms for eigensubspaceestimation, Digital Signal Processing, 5, 97–115, 1995. [36] Regalia, P.A. and Loubaton, P., Rational subspaceestimation ... approachthat uses RLS techniques to update the signal subspace. The projection approximation subspace tracker (PAST) algorithm computes an arbitrary basis for the signal subspace in 3nr + O(r 2 ) flops...

... of three compo- nents: the signal y m (n) that is modeled by the adaptive filter, the signal y u (n) that is unmodeled but that depends on the input signal, and the signal v(n) that is independent ... systems, IEEE Trans. Signal Processing, 41(2), 617–628, 1993. [14] Lin, J N. and Unbehauen, R., Bias-remedy least mean square equation error algorithm for IIR parameter recursive estimation, IEEE Trans. Signal Processing, 40(1), ... Trans. Acoustics, Speech, Signal Processing, 38(7), 1222–1227, 1990. [17] Regalia, P.A., Stable and efficient latticealgorithms for adaptive IIR filtering, IEEE Trans .Signal Processing, 40(2), 375–388,...

... Ling, F., and Nikias, C.L., Advanced Digital Signal Processing , Macmillan Publishing, New York, 1992. [3] Widrow, B. and Stearns, S.D., Adaptive Signal Processing , Prentice-Hall, Englewood ... algorithms, IEEE Trans. Signal Processing , 44(8), 1982–1989, Aug. 1996. [19] Sayed, A.H. and Kailath, T., A state-space approach to adaptive RLS filtering, IEEE Signal Processing Magazine , 11(3), ... gradient algorithms, IEEE Trans. on Signal Processing , 44(6), 1428–1439, June 1996. [21] Rupp, M. and Sayed, A.H., Robustness of Gauss-Newton recursive methods: a deterministic feedback analysis, Signal Processing ,...

... (19.2) whereW(n)=[w 0 (n)w 1 (n)ÃÃÃw L1 (n)] T isthecoefcientvector,X(n)=[x(n)x(n 1)ÃÃÃx(nL+1)] T istheinputsignalvector,d(n)isthedesiredsignal,e(n)istheerrorsignal, andà(n)isthestepsize. TherearethreemainreasonswhytheLMSadaptivelterissopopular.First,itisrelativelyeasyto implementinsoftwareandhardwareduetoitscomputationalsimplicityandefcientuseofmemory. Second,itperformsrobustlyinthepresenceofnumericalerrorscausedbynite-precisionarithmetic. Third,itsbehaviorhasbeenanalyticallycharacterizedtothepointwhereausercaneasilysetupthe systemtoobtainadequateperformancewithonlylimitedknowledgeabouttheinputanddesired responsesignals. c 1999byCRCPressLLC [22] Farden,D.C., Tracking properties of adaptivesignal processingalgorithms, IEEETrans.Acoust., Speech, Signal Processing, ASSP-29(3), ... algorithms, IEEE Trans. Signal Processing, 41(9), 2811–2825, Sept. 1993. [25] Douglas, S.C. and Meng, T.H Y., Normalized data nonlinearities for LMS adaptation, IEEE Trans. Signal Processing, 42(6), ... Contr. Signal Processing, 4(3), 219–216, May-June 1990. [28] Harris, R.W., Chabries, D.M., and Bishop, F.A., A variable step (VS) adaptive filter algorithm, IEEE Trans. Acoust., Speech, Signal Processing, ASSP-34(2),...

... T., Bayesian spectrum estimation of harmonic signals, Signal Process. Lett., Vol. 2, pp. 213–215, 1995. [8] Hayes, M.S., Statistical Digital Signal Processing and Modeling, John Wiley & Sons, ... performance of some other signal and noise subspace based methods developed later. 14.5.5 Multiple Signal Classification (MUSIC) A procedure very similar to Pisarenko’s is the MUltiple SIgnal Classification ... which plays a major role in many applied sciences such as radar, speech processing, underwater acoustics, biomedical signal processing, sonar, seismology, vibration analysis, control theory, and...

... Temporal Signals in Gaussian Noise Signal Detection: Known Gains ã Signal Detection: Unknown Gains ã Signal Detection: Random Gains ã Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: ... SpatialCovariance 13.7 Signal Classication Classifying Individual Signals ã Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing ... incorporate nonzero mean [14, 15]. 13.7 Signal Classification Typical classification problems arising in signal processing are: classifying an individual signal wave- form out of a set of possible...

... 209 Chapter 12. The Fast Fourier Transform 225 Chapter 13. Continuous Signal Processing 243 DIGITAL FILTERS Chapter 14. Introduction to Digital Filters 261 Chapter 15. Moving Average Filters 277 Chapter ... 1 Telecommunications 4 Audio Processing 5 Echo Location 7 Imaging Processing 9 Chapter 2. Statistics, Probability and Noise 11 Signal and Graph Terminology 11 Mean and Standard Deviation 13 Signal vs. Underlying ... (Transform Compression) 494 MPEG 501 Chapter 28. Digital Signal Processors 503 How DSPs are different 503 Circular Buffering 506 Architecture of the Digital Signal Processor 509 Fixed versus Floating...

... are the advantages of digital signal processing instead of analog signal processing, even though digital signal processing requires more circuits compared to analog signal processing? x(n) y(n) z −1 z −1 z −1 ΣΣ Σ Σ Figure ... [3]. Hence digital signal processing is restricted to approximately one megahertz, and analog signal processors are necessary for processing signals above that frequency, for example, processing ... sinusoidal signal, which is a continuous-time signal, and a discrete-time signal. We dis- cussed the basic procedure followed to sample and quantize an analog signal ANALOG AND DIGITAL SIGNAL PROCESSING 25 different...

... Application of Digital Signal Processing, Prentice-Hall, 1975. 9. E. C. Ifeachor and B. W. Jervis, Digital Signal Processing, Prentice-Hall, 2002. 10. V. K. Ingle and J. G. Proakis, Digital Signal Processing ... theory of digital signal processing (DSP) and implementation of the theory by devices embedded in what are known as digital signal processors (DSPs). Of course, the theory of digital signal processing ... signals were con- verted to sampled data signals by these filters and the signal processing was treated as analog signal processing. But later, the signals were transitioned as discrete-time signals,...