Lecture 7/8: UWB Channel. Kommunikations

Transcription

1 Lecture 7/8: UWB Channel Kommunikations Technik

2 UWB Propagation Channel Radio Propagation Channel Model is important for Link level simulation (bit error ratios, block error ratios) Coverage evaluation and link budget Evaluation of localization and tracking services Different from narrowband signal, UWB radio signal shows quite different characteristic due to its huge occupied spectrum. Severe Frequency Selectivity Existence of null path tap Slide 93

3 UWB Propagation Channel (Contents) UWB Radio Propagation Channel Description Saleh-Valenzuela (S-V) Multipath channel Model Parameterization and Simulation of IEEE a reference channel Reference: [Sal87] Saleh, A.A.M. ; Valenzuela, R.A. : A statistical model for indoor multipath propagation, IEEE Journal on Selected Areas in Communications, vol. 5, no. 2, pp , [Par00] Parsons, J. D. : The Mobile Radio Propagation Channel, 2nd ed. West Sussex, England: Hohn Wiley & Sons Ltd, [Ars06] Arslan, H. ; Cheng, Z. ; Benedetto, M.D. : Ultra Wideband Wireless Communication. Hoboken, new Jersey: John Wiley & Sons, Inc, 2006 [Foe03] Foerster, J.: Channel Modeling Sub-committee Report Final, IEEE /490, Slide 94

4 UWB Radio Propagation-Free Space In free space, the propagation of the UWB signal does not show significant difference with a narrowband signal, because in free space wireless channel is a linear time invariant system, and the wideband signal does not suffer frequency selective fading in this environment. Signal propagation follows Friis formula [Par00] P P c G G 2 R T R = 2 T ( 4π fcd ), f c G T G R c P R P T is the central frequency of the signal is the transmit antenna gain is the revive antenna gain speed of light received signal power transmitted signal power Slide 95

5 UWB Radio Propagation- Smooth Ground Slide 96

6 UWB Radio Propagation- Smooth Ground Line of Sight (LOS) component propagation path length Reflected component propagation path length ( ) d LOS Propagation time difference t = dr dlos c Narrowband symbols, t is much smaller than symbol duration, LOS component and reflected component. UWB symbols, t is larger than UWB symbol duration, LOS component and reflected component do not overlap with each other. d R Slide 97

7 UWB Radio Propagation-Multiple Path Slide 98

8 UWB Radio Propagation-Multiple Path signal propagation in clusters of obstacles environment. Confocal Ellipses describe the set of potential multipath components (MPCs) which have the same propagation delay from transmitter to receiver. The two neighboring ellipses stand for the bounds for the resolvable signals. superposition of these signals makes the received signal more complicated than the ideal scenarios mentioned above. Slide 99

9 UWB Radio Propagation-Multiple Path Comparison between GSM narrowband and WiMedia UWB symbol Sub-channel Bandwidth: 200 KHz (GSM), 528MHz (WiMedia) Channel tap window length: 5 us (GSM), 1.9 ns (WiMedia). Typical number of path in one tap: large enough statistic independent paths (GSM), limited number of paths (WiMedia) Multiple path model: according to Central Limit Theorem, Rayleigh distribution is used to model the received signal amplitude (GSM), for UWB signal, statistic is not enough to fulfill Central Limit Theorem. (Nakagami distribution, log-normal distribution were reported [Asr06]) Slide 100

10 Propagation Channel Description Delay Spread and Coherence Bandwidth Delay Spread describes the time dispersive nature of the wireless propagation channel. Coherence Time and Doppler Spread Coherence Time and Doppler Spread characterize the time varying nature of the propagation channel. Channel Impulse Response (CIR) CIR is the time domain description of a radio propagation channel, whose Fourier transform is Channel Transfer Function (CTF). ( τ ) αδ( t τ ) ht, = l l, l l : path index, t: time variable, αl : l-th path gain, τ l propagation delay of the l-th path, δ t Dirac delta function. () Slide 101

11 Propagation Channel Description Power Delay Profile (PDP) PDP is defined as the squared magnitude of the CIR, averaged over the small-scale fading, and it is formulated as P ( 2 ) ( τ) = h( t τ) E,. t PDP is one of the most important parameters to describe the multipath propagation channel. The delay spread and power delay profile of UWB channel show quite different characters to the narrowband channel. Slide 102

12 S-V Channel Model The S-V model [Sal87] was proposed as prototype for IEEE a reference channel [Foe03]. two-level cascaded Poisson process Slide 103

13 S-V Channel Model Continuous-time CIR of S-V model is given by T l τ kl K 1 L 1 k= 0 l= 0 ( ) ( ) ht () = X α exp jθ δ t T τ kl, kl, l kl, : arrival time of the l-th cluster, : time delay of the k-th ray in the l-th cluster kl, : uniformly distributed phase, in the range of 0,2π Λ : Cluster arrival rate λ : Ray arrival rate, : multipath gain coefficients of the k-th ray in the l-th cluster X : describes the log-normal shadowing fading of the total multipath θ [ ] α kl Slide 104

14 S-V Channel Model The cluster arrival time and the ray arrival time are Poisson processes pt T = exp Λ T T, l> 0 p ( ) ( ) Applying log-normal distribution, ( ) l l 1 l l 1 ( ) ( τ τ ) λ( τ τ ) = exp, k > 0 k, l k 1, l k, l k 1, l ( 2 ) ( 2 2 α ) kl μkl σ + σ log ~N, 10,, 1 2 ( ) α kl 2 where N u, σ is normal distribution, mean value of, is, α 0,0 μ 2 kl, 2 10ln( α 2 2 0,0 ) 10 Tl / Γ 10 τk, l / γ ( σ1 + σ2)ln(10) = ln(10) 20 2 σ 1 : mean energy of the first path of the first cluster, and variance of the cluster decay ray decay process, 2 σ 2 Slide 105

15 Channel Parameterization and Simulation Scenarios CM1 CM2 CM3 CM4 Parameters (LOS 0-4m) (NLOS 0-4m) (NLOS 4-10) (NLOS) Cluster arrival rate ns ns ns ns -1 Ray arrival rate 2.5 ns ns ns ns -1 Cluster decay factor 7.1 ns 5.5 ns 14 ns 24 ns Ray decay factor 4.3 ns 6.7 ns 7.9 ns 12 ns Std. dev. of cluster lognormal fading 3.4 db 3.4 db 3.4 db 3.4 db Std. dev. of ray lognormal fading Std. dev. of lognormal fading for total multipath realizations 3.4 db 3.4 db 3.4 db 3.4 db 3 db 3 db 3 db 3 db Slide 106

16 Channel Parameterization and Simulation Slide 107

17 MATLAB Simulation IEEE a Reference channel, matlab script. uwb_sv_params.m: contain the channel parameters for channel scenarios cm1-cm4. uwb_sv_model_ct.m: Generate the continuous CIR. The parameters of the channel environment represent the input of function uwb_sv_cnvrt_ct.m: Convert the continuous CIR to digital sampled form using polyphase filter uwb_sv_eval_ct.m: The main program which execute the process of channel model simulation. The number of realizations is defined as input. This program evaluate the spread delay time, PDP, and other channel characteristics. Slide 108

18 CIR and PDP Continuous and discrete CIR for CM1. The PDP of channel environments cm1-cm4 over 100 realizations. Slide 109

19 Noise Sources /1 Interference From other transmitters From other equipment E.g., microwave ovens 20dBm 50% duty-cycle with 16ms period. Noise in the electronics e.g., digital circuit noise on analogue parts. Non-linearities in circuits. Often modeled as white Gaussian noise, but this is not always a valid assumption. Slide 110

20 Noise Sources /2 Thermal noise Due to thermal agitation of electrons. Present in all electronics and transmission media. kt(w/hz) k Boltzmann s constant = T temperture in Kelvin (C+273) ktb(w) B bandwidth E.g., Temp = 293,=> -203dB, -173dBm /Hz Temp 293 and 22MHz => -130dB, -100dBm Slide 111

21 Noise Sources /3 The simple observation for the noise is the white Gaussian noise. Generated by Gaussian distribution random numbers with mean zero and unity variance. N σ The nosie energy calculte as follow: pow_noise = (Eb/(10^(ebno/10))); sigma_noise E = b 10 N = 2 b 0 ( E / N ) db 2 0 Let Eb=1, ebno=10db, sigma_noise=? 0 = sqrt(pow_noise/2); noise power sample number Slide 112

22 Noise Sources /4 Simple addition for the noise signal to the transmitted signal integrate the information through AWGN channel model. yt ( ) = xt ( ) + nt ( ) Tx nt ( ) xt ( ) AWGN y( t) 0.4 Tx signal 1.5 noised signal x x 10-7 Slide 113

23 Integration of Channel the channel realization which represent the CIR of the multipath environments. The CIR sampled at system sampling time. First method: Integrate the channel by convolving the transmitted signal with the CIR: r t = x t h t τ + n t ( ) ( ) ( ) ( ) Second method: Filter the transmitted signal through the CIR of the channel. CIR_out = conv (Tx_sig,h); CIR_out = filter (Tx_sig,1,h); power db power (watt) cahnnel CIR Delay time (time) x 10-7 Tx signal affected by channel time (s) 6 8 x 10-7 Slide 114

24 Exercises: Create a C-Mex function that: 1. Simulate the white Gaussian noise. 2. Find the reverse vector of size j. 3. Calculate the convolution of two signal (complex and real signal). Test the convolution function by matlab and compare your results with matlab command conv. Slide 115