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Introduction to Smart Antenna Techniques and Algorithms RAWCON `99 Smart Antenna Workshop

Introduction to Smart Antenna Techniques and Algorithms RAWCON `99 Smart Antenna Workshop. Adrian Boukalov Helsinki University of Technology Communications Laboratory Otakaari 5 A, FIN-02150 Espoo, Finland e-mail: adrian.boukalov@hut.fi fax/ voice: : int. + 358-9 - 4512359/17.

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Introduction to Smart Antenna Techniques and Algorithms RAWCON `99 Smart Antenna Workshop

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  1. Introduction to Smart AntennaTechniques and AlgorithmsRAWCON `99 Smart Antenna Workshop Adrian Boukalov Helsinki University of Technology Communications Laboratory Otakaari 5 A, FIN-02150 Espoo, Finland e-mail: adrian.boukalov@hut.fi fax/ voice: : int. + 358-9 - 4512359/17

  2. Overview • Introduction • Motivation for smart antennas (review) • System integration of smart antenna (SA) technology • Cellular network components influenced by smart antenna technology • Spatial Processing • Classification by reference type • Receiver structures and algorithms • Space-Time Processing • Space-Time CDMA receivers • SA integration into macrocell and microcell environment

  3. "Spatial Processing remains as the most promising,if not the last frontier, in the evolution of multiple access systems" Andrew Viterbi

  4. Smart Antenna Technology:Motivation Link level improvements System improvements - Interference cancellation on the up and down links - SNR improvement due to antenna gain - Multipath mitigation capacity coverage Quality of service (QoS), bit rate, mobility rate

  5. Smart Antenna Technology: Benfactors Network capacity, coverage, filling “dead spots”, fewer BSs, higher QoS, new services...-> revenues New market for more advanced BSs, flexible radio network control... Higher QoS, more reliable, secure communication, new services, longer battery life... Operator OEM User

  6. System Integration of Smart Antennas Different types of environments Propagation Maps Network Planning: Network Infrastructure (BS position, system parameters, fixed network topology) = - offered traffic spatial distribution, services. -mobility Radio channel, interference environment, mobility, services Receiver, antennas parameters Expected cells load variations Layers structure …... Smart Ant. Tech. Radio Network Management Radio Interface -Receiver structure & algorithms Protocols, dynamics C U DSP tech. Air Interface

  7. Cellular Network Components Influencedby Smart Antenna Technology Smart Ant. Tech. Network Planning - Capacity, coverage, interference planning - Joint fixed and radio network optimization, planning - System upgrade, economical issues 1G- analog systems 2G- digital systems 2.5G- digital+packet +.. (GPRS,..) 3G - W-CDMA 4G- cellular+ gigabit WLAN Radio Interface Receiver structure, Tx, Rx algorithms - Spatial proc. - Time domain proc. - Coding - Detection - Diversity - ……….. Radio Network Management DSP tech. SW Radio Services -> MS location Network control - R.resource management - call control Cell control - admission control - broadcast channel control - handover control - macro-diversity control 1G 2G Air Interface - Multiple access - Duplexing - Modulation - Framing - Availability of pilots 2.5G Link level control - Power Control - Quality Control - Tracking 3G 4G

  8. Smart Antenna Receivers: Many choices! - Switched beam, adaptive algorithms.. - Side reference information available (spatial reference, reference signal, signal structure and their combinations) for spatial processing - Narrowband , broadband (CDMA) - Optimization method (if any): maximum likelihood-ML, minimum mean square error- MSE, minimum variance-MV, maximum a-posteriori probability -MAP - Domains -> Space-only, space-time, space-frequency … - Amount and type of channel knowledge available - Combination of space/space-time processing with other technologies (diversity, interference cancellation, channel coding, space-time coding …) - Smart antennas at the mobile

  9. Spatial Processing Approaches - Sectorization - Macro-diversity with: *Combining maximum ratio combining - MRC optimum combining -OC,.. *Prefiltering/Coding Space -Time Coding V-BLAST - Beamforming (BF) Switched-beam Smart Antenna Adaptive beamforming These approaches can be/should be combined/mixed together ! Sectorization Macro-diversity Switched-beam ant. Adaptive BF

  10. Spatial Processing: Integration with Air Interface Antennas elements geometry, numbers of elements - M. Radio Transmission Technologies MS Internetworking Physical Channel Definition, Multi- plexing Multiple Access Technology Frame Structure Duplexing Technology RF- Channel parameters Modulation Technology Channel Coding Source Coding Availability of the training signal Frame length - T Mapping control, traffic channels FDD TDD Modulation type CM... Finite Alphabet Linearity FDMA CDMA Combination with Space Processing Bandwidth-B Carrier frequency fo UL->DL link Wide/narrow band SA rec, BF, AoA est Blind methods SSBF, ST Ref. Signal based BF, S-T

  11. BF/OC Techniques Classified by Reference Type Data-independent beamforming - Spatial reference based beamforming, Direction of arrival based beamforming (DoABF) - Reference signal based/time reference beamforming (TRB) and/or optimum combining (OC) - Signal structure (temporal /spectral) based beamforming, SSBF/property restored beamforming Statistically optimum beamforming

  12. Adaptive algorithms Tracking in time Data independent BF AoA estimation.. AoA(s)tracking ML, ... - DoABF - TRB and/or OC - SSBF Calibration Ref. multiplexed with des. signal or reconstr. from detected symbol Adaptive Alg. DMI, LS (LMS, RLS),non-linear Synchronization Constant Modulus (CMA), FA,... CM-”LMS” Statistically Optimum BF

  13. W W W W W W W W W Parameters that can be optimized Possible SA receivers realizations Data, BER SINR Time Ref. post det. CIR Time Ref. Demod. Detection RF IF RF-BF IF- BF BB- BF/OC

  14. Direction of Arrival Based Beamformers (DoABF ) - require angle of arrival (AoA) estimation - sensitive to AoA estimation errors, calibration problem - estimates output power at the output or eigen-decomposition of correlation matrix - problem with coherent multipath - Angular spread (As) to array resolution (A) ratio should be low - FDD applications Array Processor Array Output

  15. W X(t)=sV( )+n  DoABF :Theory in brief 

  16. AoA estimation methods 1.Conventional techniques poor angular resolution limited by aperture, search of peaks in spatial spectrum - MV (some degrees of freedom spent on interference cancellation, improved resolution) 2.Based on statistical model of signal and noise (optimal) - ML, MLM - data samples <-> AoA - Block ML(comb. with Eigen decomposition) joint pdf of sampled data needed, very computationally extensive, can work well in low SNR (or number of signal samples is small) work well in correlated signal conditions, number of sources should be known, non-linear multi-dimensional optimisation (coincides with LS estimator when assumption about noise do not hold) array data input matrix spatial signature matrix signal wave form matrix noise matrix

  17. AoA estimation methods (cont’d) U(t)=As(t)+n(t) 3. Based on the model of the received signal vector high resolution methods , fail in coherent multipath (suboptimal, BB only ) - MUSIC, WSF - ESPRIT subarraying (relaxed computational and calibration requirements) Supplementary techniques:N sources, R- correlation matrix estimation DOA estimation under coherent conditions: Spatial smoothing, multi-dimensional MUSIC, ILSP-CMA, (integrated approach) signal subspace noise subspace Rss-signal correlation matrix

  18. Time-Reference Signal Based Beamformers and/or optimal combiner (TRB) - requires reference signal or the replica correlated with desired signal - based on Wiener solution (MSE) - reference signal multiplexed with desired signal or reconstructed signal obtained from detected symbols (detection and BF are interdependent but attractive for tracking) - better for varying radio channel - diversity - more processing extensive methods - receiver is simpler at expense of spectral efficiency - synchronization problem - Delay spread (Ds) to frame length (T) ratio should be low - TDD applications LS Beamformer 1 X1(t) W1 Array output 2 X2(t) W2 y(t) N Xn(t) Wn Control algorithm Ref. Error - + + Signal processor Adaptive processor

  19. W X(t)=sA( )+n  Spat. covar. matrix Spat. signature MMSE TRB: Theory in brief w=R-1p Ref. Error Signal d(t) LS Wk=(AHA)-1 AHdk

  20. Signal Structure Based Beamforming (SSBF) - Does not require reference signal, thus increased spectral efficiency - constant modulus (CM)property of phase modulated signals, - finite alphabet (FA) property of digitaly modulated signals , - spectral coherence restoral SCORE (only information needed - bit rate) - Useful method for tracking between references - Convergence properties ? - Methods based on partial information are usually non-linear - Performance from robustness point of view similar to reference signal based methods BF (W) CMA

  21. Improvements available using spatial processing - Improvement in SNR due to beamforming array gain. (improved coverage. ) - Reduced ISI. (depends on angular spread of multipath) - Enhanced spatial diversity. - Interference cancellation. In Tx and Rx. Capacity. These goals may be conflicting. Need balancing to achieve synergy with propagation environment, offered traffic, infrastructure.

  22. SNR CCI Diversity ISI Time Diversity BS MS SNR maximization due to antenna gain Beamforming ~1/M

  23. SNR CCI DiversityISI Time Diversity Interfering MS 2 BS MS 1 Co-Channel Interference (CCI) Cancellation Beamforming Combining M-1 interferers cancellation. independent of the environment M-1

  24. Beamforming Multi-path SNR CCI Diversity ISI Time Diversity MS BS Ang. Div. Combining Space Div. Diversity (Angle- and Space-) Gain M ~M

  25. Beamforming SNR CCI Diversity ISI Time Diversity Multipath Delayed Signals Path with ISI Combining BS ISI Cancellation M-1 M-1 delayed signals cancellation (M-1)/2 symbol due to delay spread

  26. SNR CCI Diversity ISI Time Diversity Delayed Signals Combining Optimal Spatial Algorithms Beamforming Multi-path BS Interfering MS 2 MS 1 Path with ISI, uncorrelated paths

  27. SNR CCI Diversity ISI Time Diversity Delayed Signals Delayed Signals Beamforming Multi-path BS Time Interfering MS 2 MS 1 Combining Path with ISI, uncorrelated paths Optimal S-T Algorithms + Spatial domain processing Temporal domain processing

  28. SNR CCI Diversity ISI ~1/M (M-1) ~M ang. div (M-1) Optimum BF (M-1) M spat div. (M-1)/2 interferers gain del. symb. Optimum Combining Degrees of freedom number of SA elements - Number of SA elements (M) can be considered as a “resource”, i.e., degrees of freedom which can be spent for SNR, CCI, diversity, ISI, either separately or jointly (optimum) - M determines “spatial selectivity” of SA

  29. Beamforming Methods Data independent beamforming (conventional beamformer -CBF,..) Optimum BF - Based on the cost function maximization/minimization (max SINR,…) - Based on statistical estimation ML (likelihood function) Squared function based MSE (Reference ) -Adaptive algorithms - Least Square (LS), Maximum A-posteriori Probability (MAP),… ( for example, GSLC,…)

  30. Optimization Criteria - Based on cost function maximization/minimization (max SINR,…)-> difficult to obtain - Based on Statistical Estimation ML (Likelihood function)-> treats interference as temporally and spatially white Gaussian. Balance effect of noise. MSE (Reference )-> more attractive in presence correlated CCI. -> More efficient in interference dominant environment. Do not balance effect of noise

  31. Spatial processing: Summary DoABF - better perform in environments with low angular spread - require AoA estimation and calibration - well suit for FDD applications - macrocell environment - CDMA AoA estimation and beamforming TRB or/and OC - well perform in environments with high angular spread - require reference signal (spectrum efficiency), synchronization - well suit for TDD (micro/pico cells), FDD is more problematic micro and picocell - more robust methods in changing environment (adaptive algorithms)can be/should be combined with blind methods

  32. Space-Time (S-T) Processing - Space domain processing: Efficient CCI mitigation Space Diversity ISI mitigation depends on angular spread of multipath and M and cannot be very efficient - Time domain processing Very limited against CCI Time/path div., ISI mitigation - S-T Processing Simultaneous operations in Time and Space domains can combine strength of the both - Multi-User-S-T Processing Channel ST-MLSE Vector VA Sk + Training ST-MMSE yk Sk Demod. W = ST-MMSE/MLSE STF W Scalar VA MLSE

  33. Space-Time (S-T) processing techniques Decoupled S-T processing Joint S-T processing Path diversity BF Combining Single user MU Narrowband Wideband Up-link Down-link

  34. Relations between spreads and relative quantities of interests for different types of cells. Location of scatters Spread Type at MS at BS Remote Critical system parameter Macrocell Doppler spread Dp fd = fo(v/c) Delay spread Ds Angular spread As B fd/B MS motion T Micro cell Ds/T Array Resolution A=1/M As/A static

  35. Macrocell and Microcell Channel Response Remote scatters 1800 1800 Scatters local to BS -1800 0 1 0 20 Delay (microsec) Delay (microsec) Scatters local to MS Macrocell Microcell As Dp As? Ds After A.Paulraj

  36. Space-Time MLSE and MMSE S-T MLSE CCI statistic needed Delay spread (Ds) can’t handle large Ds Doppler spread difficult to handle Blind methods more problematic MU S-T MLSE - optimum S-T MMSE not needed (Rxx) less problematic can handle by channel tracking applicable S-T MMSE (V-BLAST)

  37. TDMA Rx Structures (Ch. Knowledge <-> Optimality) S-DIV CCI ISI T-DIV X X X X X X X X X X X X X X X X X MU-MLSE H1 H2 H1 RS-T ST-MMSE-MLSE S-MMSE-MLSE H1 RS Decreasing Channel Knowledge MMSE H1 ANT-HOP Nil After A. Paulraj

  38. CDMA Rx Structures (Ch. Knowledge <-> Optimality) S-DIV T-DIV MUI X X X X X X X X X X X X ST-MU H1 H2 H1 RS-T ST-MMSE ST-RAKE H1 RS Decreasing Channel Knowledge BF-RAKE H1 ANT-HOP Nil After A. Paulraj

  39. Space-Time channel estimation Spatial Structure Non-blind (Ref.) - unstructured channel - structured channel - parametric channel Blind - High order statistic - Second order statistic (SOS) - ML - MLSE -> ST- JCDE - MMSE -> tracking by DD adaptive alg. Temporal structure: - CM - FA Block Modems Adaptive Modems Underlying channel/signals structures Channel Estimation methods Tracking of fast varying channel Reference

  40. Space Domain Only and Space-Time SA Algorithms

  41. Space-Time CDMA receivers - Non- coherent combining (equal gain diversity combining improves SNR, but CCI cancellation not possible.) - Coherent combining Beamforming- RAKE (1D, 2D) Reference signal based beamformer - RAKE DoABF- RAKE (max. SINR, ML, ..) SSBF- RAKE Combing - RAKEOC, IRC,.. - Multi-user ST (MU-ST-MMSE, MU-ST-MLSE) - Space -frequency RAKE (RS-F) joint, and decoupled

  42. Bank of matched filters Multi-user VA ST MU-MLSE - computational complexity linear to the number of users - same degree of the near-far resistance and error rate performance as optimum MU receiver - require knowledge of the all users channels - optimum in Gaussian noise only

  43. SA integration into macrocell and microcell environments

  44. TDMA Tx Structures S-DIV CCI T-DIV X X X X X X X X X MRC-Adaptive ST - Nulling H1 H2 MRC-Adaptive nulling H1 RS-T CBF Decreasing Channel Knowledge R11 ANT-HOP After A. Paulraj

  45. CDMA Tx Structures S-DIV T-DIV MUI X X X X X X HST ST-MRC HS S-MRC CBF Decreasing Channel Knowledge Rss ST-Coding After A. Paulraj

  46. - Spatial structure based algorithms can work in higher Doppler spread but are affected by angular spreads - Temporal structure based algorithms can better handle delay spread, but higher speed can be problem - Single and multi-user combination may be needed - Training signal <---> receiver complexity trade-off - Environment (spreading) <--> receiver and algorithmic complexity, (how models corresponds to reality) Summary

  47. Summary (cont’d) • Best solutions: Combine trade-offs between: • - Beamforming <---> combining • - Algorithms (ML<---> MSE) , subspace • - Optimum <---> Data independent approaches • - Base band beamforming <---> RF/or IF beamforming • - Combination with other methods like multi-user detection (MUD), diversity, ST coding, adaptive modems • Air interfaces should be not only “friendly” for S-T • processing but flexible / adaptive to be able to exploit • advantages of spatial processing in variable environments

  48. Glossary

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