Channel Modelling ETIN10. Directional channel models and Channel sounding

Transcription

1 Channel Modelling ETIN10 Lecture no: 7 Directional channel models and Channel sounding Ghassan Dahman / Fredrik Tufvesson Department of Electrical and Information Technology Lund University, Sweden Fredrik Tufvesson - ETIN10 1

2 Why directional channel models? The spatial domain can be used to increase the spectral efficiency of the system Smart antennas MIMO systems Need to know directional properties How many significant reflection points? Which directions? Model independent on specific antenna pattern Fredrik Tufvesson - ETIN10 2

3 Double directional impulse response Fredrik Tufvesson - ETIN10 3

4 Physical interpretation l Fredrik Tufvesson - ETIN10 4

5 Angular spread E s,,, s,,, P s,,, double directional delay power spectrum DDDPS,, P s,,, d angular delay power spectrum ADPS, DDDPS,, G MS d l angular power spectrum APS APDS, d power P APS d Fredrik Tufvesson - ETIN10 5

6 Directional models The double directional delay power spectrum is sometimes factorized w.r.t. DoD, DoA and delay. DDDPS,, APS BS APS MS PDP Often in reality there are groups of scatterers with similar DoD and DoA clusters DDDPS,, P c k APS c,bs k APS c,ms k PDP c k k Fredrik Tufvesson - ETIN10 6

7 Angular dispersion At the base station the angular spread is often modeled as Laplacian 0 APS( ) exp( 2 ) S Typical rms angular spread: Indoor office: deg Industrial: deg Microcell 5-20 deg LOS, deg NLOS Rural: 1-5 deg Fredrik Tufvesson - ETIN10 7

8 Laplacian distribution, example Angular spreads 5, 10, 20, 40 degrees Fredrik Tufvesson - ETIN10 8

9 Geometry-based stochastic channel models Assign positions for scatterers according to given distributions Derive impulse response given the scatterers and distributions for the signal properties. Used in the COST 259 model, the COST 273 model the 3GPP spatial channel model, and the WINNER model Fredrik Tufvesson - ETIN10 9

10 Geometry-Based Stochastic Channel Model (GSCM) Create an imaginary map for radio wave scatterers (clusters) Cluster Local cluster MS 2 MS 1 Local cluster BS Cluster Fredrik Tufvesson - ETIN10 Courtesey: K. Haneda, Aalto Uni. 10

11 Properties of channel models used for Beyond-3G (B3G) MIMO simulations 3GPP Spatial Channel Model (SCM) SCM extension (SCME) WINNER I, and WINNER II Copied from: Narandzic, M.; Schneider, C.; Thoma, R.; Jamsa, T.; Kyosti, P.; Xiongwen Zhao;, "Comparison of SCM, SCME, and WINNER Channel Models," Vehicular Technology Conference, VTC2007-Spring. IEEE 65th, vol., no., pp , April Fredrik Tufvesson - ETIN10 11

12 Properties of channel models used for Beyond-3G (B3G) MIMO simulations 3GPP Spatial Channel Model (SCM) SCM extension (SCME) WINNER I, and WINNER II Copied from: Narandzic, M.; Schneider, C.; Thoma, R.; Jamsa, T.; Kyosti, P.; Xiongwen Zhao;, "Comparison of SCM, SCME, and WINNER Channel Models," Vehicular Technology Conference, VTC2007-Spring. IEEE 65th, vol., no., pp , April Fredrik Tufvesson - ETIN10 12

13 Considered scenarios (WINNER II) A1 Indoor office A2 Indoor to outdoor B1 Urban micro-cell B2 Bad Urban micro-cell B3 Indoor hotspot B4 Outdoor to indoor B5 Stationary Feeder C1 Suburban macro-cell C2 Urban macro-cell C3 Bad urban macro-cell C4 Urban macro outdoor to indoor D1 Rural macro-cell D2 Moving networks Fredrik Tufvesson - ETIN10 13

14 MIMO stochastic channel models Channel matrix h τ = h 11 (τ) h 21 (τ) h MRx 1(τ) h 12 (τ) h 22 (τ) h MRx 2(τ) h 1MTx (τ) h 2MTx (τ) h MRx M Tx (τ) Signal model y t = τ h τ x(t τ) Kronecker model H = 1 E tr HH 1/2 1/2 R Rx GG R Tx Fredrik Tufvesson - ETIN10 14

15 Channel measurements In order to model the channel behavior we need to measure its properties Time domain measurements impulse sounder correlative sounder Frequency domain measurements Vector network analyzer Directional measurements directional antennas real antenna arrays multiplexed arrays virtual arrays Fredrik Tufvesson - ETIN10 15

16 Working principle Courtesy of MEDAV Requirement for efficient measurements: Large bandwidth (high delay resolution) Large time bandwidth TW product (i.e., (signal duration (1/W))>1), to achieve high SNR by transmitting more energy even if the allowed transmit power is limited) Signal duration should be adaptive to channel properties (e.g., TW product vs. coherence time) Power spectral density should be uniform across the bandwidth to have same quality for the channel estimation Low Crest Factor (Peak to Average ratio) Good correlation properties Fredrik Tufvesson - ETIN10 16

17 Can the channel be measured in a unique way? f rep 2v max T rep 1/2v max 2τ max v max 1 A channel that fulfills these requirements is known as underspread. Furtionately, the majority of wirless channels are underspread (i.e., slow time variant channels) Fredrik Tufvesson - ETIN10 17

18 Impulse sounder impulse response of sounder impulse response of channel Comparable with the impulse radar Sends a sequence of short pulses to achieve good spatial resolution Fredrik Tufvesson - ETIN10 18

19 Correlative sounder Transmit a pseudo-noise sequence and correlate with the same sequence at the receiver Compare conventional CDMA systems Correlation peak for each delayed multipath component p( ) h() ˆ( ) h -T c T c correlation peak impulse response measured impulse response Fredrik Tufvesson - ETIN10 19

20 Frequency domain measurements Use a vector network analyzer or similar (sounders) are used to determine the transfer function of the channel H ( f ) H ( f )* H ( f )* H ( f ) meas TXantenna channel RXantenna Time domain properties via FFT Using a large frequency band it is possible to get good time resolution Fredrik Tufvesson - ETIN10 20

21 Channel sounding directional antenna Measure one impulse response for each antenna orientation Fredrik Tufvesson - ETIN10 21

22 Channel sounding antenna array Measure one impulse response for each antenna element Ambiguity with linear array d d d linear array h( ) h( ) h( ) h( ) x=0 x=d x=2d x=(m-1)d Signal processing spatially resolved impulse response Fredrik Tufvesson - ETIN10 22

23 Real, multiplexed, and virtual arrays Real array: simultaneous measurement at all antenna elements RX RX RX Multiplexed array: short time intervals between measurements at different elements Digital Signal Processing RX Digital Signal Processing Virtual array: long delay RX Digital Signal Processing Fredrik Tufvesson - ETIN10 23

24 Directional analysis The DoA can, e.g., be estimated by correlating the received signals with steering vectors. An element spacing of d=5.8 cm and an angle of arrival of =20 degrees gives a time delay of s between neighboring elements Fredrik Tufvesson - ETIN10 24

25 High resolution algorithms In order to get better angular resolution, other techniques for estimating the angles are used, e.g.: MUSIC, subspace method using spectral search ESPRIT, subspace method MVM (Capon s beamformer), rather easy spectral search method SAGE, iterative maximum likelihood method Rather complex, one measurement point may take few minutes on a decent computer Fredrik Tufvesson - ETIN10 25

26 RUSK LUND, our broadband MIMO channel sounder A fast switched measurement system for radio propagation investigations at 300 MHz, 2 GHz and 5 GHz. Financed by Knut and Alice Wallenbergs stiftelse, FOI and LTH Fredrik Tufvesson - ETIN10 26

27 It s all about measuring some delays... In MIMO systems we use the fact that there are several paths between the transmitter and receiver These paths are characterized by a time delay, phase shift, attenuation, angle of departure and angle of arrival The angle of departure and angle of arrival result in a slight difference in time delay (phase) for each of the antenna elements Fredrik Tufvesson - ETIN10 27

28 It s all about measuring some delays... In practice we measure the transfer functions between each of the antenna elements, and we calculate the parameters of interest Fredrik Tufvesson - ETIN10 28

29 Working principle Courtesy MEDAV Fredrik Tufvesson - ETIN10 29

30 Timing diagram Fredrik Tufvesson - ETIN10 30

31 norm. magnitude norm. magnitude Multicarrier spread spectrum sequence (MCSSS) 1 Tx signal in frequency 1 Tx signal in time frequency [GHz] time [µs] MCSSS - Test Squence periodic broadband Signal high Correlation Gain low Crest Factor or peak-to-average ratio band limited (almost rectangular shape in frequency domain) flexible in generation simultaneous sounding of different bands (Up-/Downlink) Fredrik Tufvesson - ETIN10 31

32 The measurement system 200 kg of batteries to allow for 6 hours of mobile measurements 640 MHz sampling frequency, to allow high Doppler frequencies 2 separate PCs to manage the data flow from the A/D converters Oven controlled rubidium clocks to maintain synchronization during wireless measurements GPS and wheel sensors to position the system Broadband patch antennas with 128 antenna ports at 2.6 GHz Circular 300 MHz antennas with a diameter of 1.5 m Cylindrical 2 GHz array with 128 elements Fredrik Tufvesson - ETIN10 32

33 RUSK LUND transmitter Baseband (Arbitrary Wave Form) Signal Generator Frequency Synthesizer Rubidium Reference Modulator Power Amplifier MIMO Control Unit GPS bandwidths: up to 240 MHz frequency grid 10 MHz max. power 500 mw, with possibility for 10 W external amplifier carrier frequency ranges MHz, MHz MHz (20W) Power Supply 24 V DC and 230 V AC Fredrik Tufvesson - ETIN10 33

34 RUSK LUND receiver GPS Receiver Odometer Interface total amplification 72 db AGC dynamic range 51 db, adjustable in 3 db steps, intermediate frequency 160 MHz bandwidth 240 MHz RF-Tuner High Speed ADC Automatic Gain Control (AGC) MIMO Control Unit Rubidium Reference High Speed Data Recorder 320 MByte/s, 500 GByte Fredrik Tufvesson - ETIN10 34

35 Antennas To get good resolution we want large size arrays 4x16 dual polarized circular patch array 4x8 dual polarized rectangular array Fredrik Tufvesson - ETIN10 35

36 Some real world examples Fredrik Tufvesson - ETIN10 36