LEADER 05143nam 2200613Ia 450 001 9910830025103321 005 20230617004032.0 010 $a1-280-27622-3 010 $a9786610276226 010 $a0-470-01004-5 010 $a0-470-01003-7 035 $a(CKB)1000000000357466 035 $a(EBL)239031 035 $a(SSID)ssj0000249236 035 $a(PQKBManifestationID)11214066 035 $a(PQKBTitleCode)TC0000249236 035 $a(PQKBWorkID)10223500 035 $a(PQKB)10009743 035 $a(MiAaPQ)EBC239031 035 $a(OCoLC)85820798 035 $a(EXLCZ)991000000000357466 100 $a20040915d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSpace-time processing for MIMO communications$b[electronic resource] /$fedited by A.B. Gershman, N.D. Sidiropoulos 210 $aChichester $cJohn Wiley$dc2005 215 $a1 online resource (389 p.) 300 $aDescription based upon print version of record. 311 $a0-470-01002-9 320 $aIncludes bibliographical references and index. 327 $aSpace-Time Processing for MIMO Communications; Contents; List of Contributors; Preface; Acknowledgements; 1 MIMO Wireless Channel Modeling and Experimental Characterization; 1.1 Introduction; 1.1.1 MIMO system model; 1.1.2 Channel normalization; 1.2 MIMO ChannelMeasurement; 1.2.1 Measurement system; 1.2.2 Channel matrix characteristics; 1.2.3 Multipath estimation; 1.3 MIMO ChannelModels; 1.3.1 Random matrix models; 1.3.2 Geometric discrete scattering models; 1.3.3 Statistical cluster models; 1.3.4 Deterministic ray tracing; 1.4 The Impact of Antennas onMIMO Performance 327 $a1.4.1 Spatial diversity1.4.2 Pattern (angle and polarization) diversity; 1.4.3 Mutual coupling and receiver network modeling; References; 2 Multidimensional Harmonic Retrieval with Applications in MIMO Wireless Channel Sounding; 2.1 Introduction; 2.2 Harmonic Retrieval DataModel; 2.2.1 2-D harmonic retrieval model; 2.2.2 N-D harmonic retrieval model; 2.2.3 Khatri-Rao product of Vandermonde matrices; 2.3 Identi.ability of Multidimensional Harmonic Retrieval; 2.3.1 Deterministic ID of N-D harmonic retrieval; 2.3.2 Stochastic ID of 2-D harmonic retrieval 327 $a2.3.3 Stochastic ID of N-D harmonic retrieval2.4 Multidimensional Harmonic Retrieval Algorithms; 2.4.1 2-DMDF; 2.4.2 N-DMDF; 2.4.3 N-D unitary ESPRIT; 2.4.4 N-DMUSIC; 2.4.5 N-D RARE; 2.4.6 Summary; 2.5 Numerical Examples; 2.5.1 2-D harmonic retrieval (simulated data); 2.5.2 3-D harmonic retrieval (simulated data); 2.6 Multidimensional Harmonic Retrieval for MIMO Channel Estimation; 2.6.1 Parametric channel modeling; 2.6.2 MIMO channel sounding; 2.6.3 Examples of 3-D MDF applied to measurement data; 2.7 Concluding Remarks; References 327 $a3 Certain Computations Involving Complex Gaussian Matrices with Applications to the Performance Analysis of MIMO Systems3.1 Introduction; 3.2 Performance Measures of Multiple Antenna Systems; 3.2.1 Noise-limitedMIMO fading channels; 3.2.2 MIMO channels in the presence of cochannel interference; 3.2.3 MIMO beamforming; 3.3 SomeMathematical Preliminaries; 3.4 General Calculations with MIMO Applications; 3.4.1 Main result; 3.4.2 Application to noise-limited MIMO systems; 3.4.3 Applications to MIMO channels in the presence of interference; 3.5 Summary; References 327 $a4 Recent Advances in Orthogonal Space-Time Block Coding4.1 Introduction; 4.2 Notations and Acronyms; 4.3 Mathematical Preliminaries; 4.4 MIMO System Model and OSTBC Background; 4.5 Constellation Space Invariance and Equivalent Array-Processing-Type MIMOModel; 4.6 CoherentML Decoding; 4.7 Exact Symbol Error Probability Analysis of Coherent ML Decoder; 4.7.1 Probability of error for a separable input constellation; 4.7.2 Probability of error for a nonseparable input constellation; 4.8 Optimality Properties of OSTBCs 327 $a4.8.1 Suf.cient conditions for optimal space-time codes with dimensionconstrained constellations 330 $aDriven by the desire to boost the quality of service of wireless systems closer to that afforded by wireline systems, space-time processing for multiple-input multiple-output (MIMO) wireless communications research has drawn remarkable interest in recent years. Exciting theoretical advances have been complemented by rapid transition of research results to industry products and services, thus creating a vibrant new area. Space-time processing is a broad area, owing in part to the underlying convergence of information theory, communications and signal processing research that brought it to fru 606 $aMIMO systems 606 $aSpace time codes 615 0$aMIMO systems. 615 0$aSpace time codes. 676 $a621.382 701 $aGershman$b Alex B$01653805 701 $aSidiropoulos$b N. D$g(Nikos D.)$01653806 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830025103321 996 $aSpace-time processing for MIMO communications$94005286 997 $aUNINA