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Smart Antennas: Introduction, SDMA, Beamforming, DOA Estimation

This lecture provides an overview of smart antennas, including SDMA, beamforming, DOA estimation, and signal reconstruction. It covers the concepts of line-of-sight propagation, multipath propagation, MIMO channel modeling, and source separation. Related topics such as CDMA multi-user detection and MIMO transmission are also discussed.

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Smart Antennas: Introduction, SDMA, Beamforming, DOA Estimation

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  1. Module-3 : TransmissionLecture-10 (18/5/00) Marc Moonen Dept. E.E./ESAT, K.U.Leuven marc.moonen@esat.kuleuven.ac.be www.esat.kuleuven.ac.be/sista/~moonen/ Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven/ESAT-SISTA

  2. Lecture 10 : Smart Antennas -Overview • Introduction: Smart Antennas SDMA (`driver application’) • SDMA v1.0 Line-of-sight propagation & beamforming DOA estimation and signal reconstruction • SDMA v2.0 Multi-path propagation MIMO channel modeling & source separation • Related Topics CDMA multi-user detection (see Lecture-9) MIMO transmission (see Lecture-2) Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  3. Introduction : Smart Antennas • Antenna arrays (hardware) with (software) `beam-forming’ (`beam-steering’), or similar (in multi-path scenario, see below). • `Antenna diversity’ Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  4. Introduction : Smart Antennas • Aim : increase signal-to-interference-and-noise ratio, hence improved performance/increased capacity (e.g. in CDMA systems) • Antenna arrays mostly considered for base station systems, not (often) for mobile terminals. • Currently simple systems with switching between antenna signals (=select best signal), fixed directional antennas for sectorization (e.g. GSM), ... • More advance systems considered for WLANs, for W-CDMA, etc... • Will consider SDMA as `driver application’ Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  5. radio channel x bits/sec/Hz/km2 transmitter receiver Introduction : SDMA • `Conventional’ wireless communications (`SISO’, TDMA/FDMA/CDMA) • What we have in mind is …. (MIMO transmission, SDMA) transmitter receiver radio channel 2x bits/sec/Hz/km2 transmitter receiver Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  6. Introduction : SDMA • Example : cellular mobile telephony (e.g. GSM) • Basic network architecture : -country covered by a grid of cells -each cell has a base station -base station connected to land telephone network and communicates with mobiles via a radio interface Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  7. Introduction : SDMA • Why cellular ? Capacity increase by spectrum reuse , pico-cells, etc. • Capacity increase by multiplexing : - GSM (900MHz) has 125 frequency channels/cell (FDMA) 8 time slots/channel (TDMA) In practice, capacity per cell << 8*125 ! - Spatial multiplexing : allows different users in 1 cell to use the same freq./time slot - Tool = DSP algorithms for signal separation, equalization Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  8. Introduction : SDMA • PS: in GSM neighboring cells cannot use same frequency bands (intercell interference). Same frequency band used in each 7th cell. Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  9. Introduction : SDMA • SDMA (`spatial division multiple access’) allows different users in the same cell to use the same frequency channel/time slot/code, and thereby offers substantial capacity increases when superimposed on a current system! • SDMA supports multiple directional connections on a single conventional radio channel through the usage of antenna arrays and advanced signal processing. Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  10. Introduction : SDMA PS: SDMA ~ `dynamic sectorization’ WARNING: • Major practical impediment is computational complexity (cfr. linear algebra-type operations at high sampling rates). …Gflops requirement…. • Major challenge for VLSI/ASIC design • First products probably in WLAN-type applications instead of cellular/mobile AIM: • Illustrate (near) future system design concepts…. Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  11. SDMA v1.0: Beamforming Approach • Assumptions: - sources are in the far-field - line-of-sight (LOS) connections - no multi-path effects - homogeneous medium/ideal channel characteristics - additive white Gaussian noise (AWGN) - no inter-symbol interference Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  12. SDMA v1.0: Beamforming Approach • Beamforming (`spatial filtering’): PS: compare with regular temporal (FIR) filtering Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  13. antenna outputs sources array gain matrix = * steering vector source-1 steering vector source-2 time samples for antenna-1 time samples for antenna-2 time samples for source-1 time samples for source-2 SDMA v1.0: Beamforming Approach • Data Model: Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  14. SDMA v1.0: Beamforming Approach • Data Model: `Steering vector’ a(theta) = array response vector, contains gains and phase shifts for a narrow-band wavefront impinging from direction-of- arrival (DOA) theta (and for a certain carrier frequency) The collection of `steering vectors’ for all possible angles theta, is referred to as the `array manifold’ Knowledge of `array manifold’ is crucial is beamforming approach Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  15. d angle SDMA v1.0: Beamforming Approach • Array manifold example: Uniform Linear Array where f = phase shift = Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  16. SDMA v1.0: Beamforming Approach • Significance of array manifold: -array manifold is a parametrization of the steering vector as a function of the DOA -if array manifold is known (by calibration or physical modeling), `channel modeling’ is reduced to DOA estimation. If the DOA for one particular source is identified, its complete steering vector is known. Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  17. antenna outputs sources array gain matrix = * SDMA v1.0: Beamforming Approach • Problem Statement: Given antenna outputs & array manifold, compute : -directions-of-arrival (DOA’s) -source signals ? Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  18. antenna outputs sources array gain matrix = * SDMA v1.0: Beamforming Approach • Solution (Part-1): DOA estimation `low-resolution algorithms’ : Fourier-based (e.g. for ULA’s) `high-resolution algorithms’ : -MUSIC [Schmidt 1979]: search for DOA such that steering vector optimally matches `column space’ of antenna output matrix -ESPRIT [Roy et al, 1987]: DOA’s identified as generalized eigenvalues of a matrix `pencil’ Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  19. SDMA v1.0: Beamforming Approach • Solution (Part-2): Beamforming and signal reconstruction Given steering vectors of signal-of-interest and interferers, compute beamformer weights such that interference signals are eliminated (`null steering’) Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  20. w1 w1 w2 w2 : : SDMA v1.0: Beamforming Approach • Solution (Part-2): Beamforming and signal reconstruction compute weight vector w1, w2,…. such that…. antenna outputs sources array gain matrix = * * * 1 0 : 0 Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  21. SDMA v1.0: Beamforming Approach • Solution (Part-2): - Compute weight vector w1, w2,…. that cancels all interferers, and retains the signal of interest (cfr. supra) - This is `zero-forcing’ solution. With additive noise, a minimum-mean-squared-error solution is preferred. - Other : Generalized sidelobe canceller, minimum variance distortionless response beamforming, Griffiths-Jim beamforming : adaptive beamformers, based on knowledge of steering vector of (only) the signal-of- interest, and where noise environment (incl. interferers) may be time-varying. Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  22. SDMA v1.0: Beamforming Approach • Beamforming approach deficiencies : - not always line-of-sight (LOS) connection - multi-path effects long/short term fading (e.g. wavelength=30cm @ 900MHz) - inter-symbol-interference (e.g. symbol ~ 1km @ 270kbits/sec) • Conclusion: - array manifold concept no longer useful - need more sophisticated data models/algorithms Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  23. SDMA v2.0: Channel Modeling Approach • Instead of this….. • we have this….. • now what?? Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  24. SDMA v2.0: Channel Modeling Approach • SDMA with multi-path corresponds to multi-user (multiple-input/multiple output) channel equalization problem : a) identify channel model b) reconstruct channel inputs from outputs+model single-user (e.g. GSM) multi-user (SDMA) Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  25. SDMA v2.0: Channel Modeling Approach • Step-1 is a channel modeling, i.e. identify... • Training sequence based versus `blind algorithms’ (see Lecture 5-6) Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  26. SDMA v2.0: Channel Modeling Approach • Step-2 is equalizer design, i.e. identify… • Zero-forcing (ISI=MUI=0) versus MMSE (see Lecture 5-6) • This is combined equalization & source separation Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  27. SDMA v2.0: Channel Modeling Approach • Step-1 & -2 may be combined : direct (training sequence based) equalizer design (see Lecture-5/6). Only training sequence for user-of-interest needed (not for other users). • Recursive vs batch processing (Lecture-5/6) • `Oversampling’ (i.e. having more outputs (antennas) than inputs (users)) is crucial for the existence of zero-forcing solutions (for FIR channels). • Connections with fractionally spaced equalization theory and filter bank theory. • Active area of research (blind algorithms based on 2nd order statistics, finite alphabet properties, etc.). • Commercial use: probably first WLAN, etc... Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  28. Related Topics CDMA multi-user detection algorithms (Lecture-9) MUD algorithms are conceptually similar : -Spreading viewed as a (transmit) filtering operation and part of the `channel’. -Nyquist-rate sampling at the receiver is symbol-rate oversampling, which is equivalent to spatial oversampling (multiple antennas). -etc... Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  29. Related Topics MIMO Transmission (Lecture-2) - Point-to-point transmission, where both transmitter and sender have antenna array - additional flexibility for sender (beamforming, …) - with M antennas at both ends, allows for M-fold channel capacity increase with the same transmit power budget (!) - example : V-BLAST Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  30. Conclusions • Smart Antennas - Advantages : improved signal-to-interference-and-noise ratio, increased capacity (CDMA). - Considered for W-CDMA, ... • SDMA v1.0 - Beamforming approach - Conceptually simple, but not applicable in multi-path environment • SDMA v2.0 - Multi-path/MIMO channel modeling approach - Powerful but complex • Related Topics - CDMA multi-user detection, MIMO transmission Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  31. Assignment 5.1 `Brain Teaser’ : • In Lecture-2, we have considered MIMO-transmission from a channel capacity point of view. Look at the conclusions again. One of the conclusions was that `one has to be lucky with the channel characteristic’. • Think of a similar channel capacity analysis for SDMA. Does one again have to be `lucky with the channel’ ? • What would be a most advantageous channel, in terms of channel capacity ? What would be the obtained channel capacity ? Is it `what we had in mind’ (cfr slide-5) ? Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

  32. Assignment 5.2 `Brain Teaser’: • In Lecture 7-8 we have considered multi-tone transmission, where a (high-rate) bit stream is split up into (low-rate) parallel bit streams, which are then used to QAM modulate different carriers. • Now consider these low-rate streams as being different users, accessing the same transmission channel. The carrier modulation may be viewed/compared with a spreading operation a la DS-CDMA. • Based on this, compare DMT with CDMA and MIMO, both from a capacity and a receiver structure point of view. Look for similarities and differences. • In a similar fashion, compare DMT with MIMO transmission Module-3 Transmission Marc Moonen Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA

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