Ultra Wideband Radio Propagation Measurement, Characterization and Modeling
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1 Ultra Wideband Radio Propagation Measurement, Characterization and Modeling Rachid Saadane GSCM LRIT April 14, 2007 achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 1 / 26
2 Outlines Outlines 1 Background 2 Measurement system and parameters 3 First results 4 Conclusions 5 Future works Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 2 / 26
3 Introduction Background Introduction What is UWB? Study and UWB channel... Next slide... achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 3 / 26
4 Introduction count d Background Introduction The goal is not to formulate a channel model for UWB systems or to provide a universal model for all environments in which UWB devices will be operating. But rather to provide a set of tools that can be used to fairly evaluate the performance of different UWB physical layer proposals in real channels such as offices, laboratories and industrial environment. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 4 / 26
5 Background UWB advantages and Disadvantages Wireless Personal Area Networks (WPAN) Large bandwidth Low power More users High data rates Low costs UWB advantages More bandwidth Lower center frequency better penetration through materials UWB disadvantages Lower transmitter power UWB antenna mismatch External interference from other systems achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 5 / 26
6 UWB and FCC Rule Background UWB Regulation Feb 14, 2002 (Federal Communication Commissions :FCC) Figure: (FCC) FCC UWB Emission Limit for Indoor Systems. achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 6 / 26
7 Information theory view Background Channel Capacity Shannon s Equation C = W log(1 + S/N) (1) C = Maximum Channel Capacity W = Channel Bandwidth (Hz) S = Signal Power (watts) N = Noise Power (watts) C is linearly with W, and only logarithmic with S/N Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 7 / 26
8 UWB Indoor Applications Background UWB Applications achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 8 / 26
9 Overview Our Considerations and UWB Characterization Channel modeling including frequency characteristics of antennas Our propagation channel in this work include antennas The channel is considered stationary in time dominae Rachid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007 Modeling 9 / 26
10 Measurement Setup Measurement system and parameters Measurement Setup achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 10 / 26
11 Measurement system and parameters Parameters for measurements Parameters for measurements Parameters Value Frequency band 3 to 9 GHz Center frequency 6.85 GHz Bandwidth (frequency span) 7.5 GHz Number of points 2001 Dynamic power range 80 db Tx antenna height 1500 mm Rx antenna height 1500 mm Distance between Tx and Rx 1 to 12 metres Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 11 / 26
12 Goals Channel parameters to be characterized Channel parameters to be characterized To providing a useful model to fit experimental data, the Nakagami-m model is used. The Nakagami-m distribution offers features of analytical convenience in comparison to an other distribution. 1 Small Scale Fading And Signal Quality Small scale effects Signal Quality Analysis The Dispersive Properties Of UWB Channel 2 Large scales channel modeling Path Loss And Distance Dependency Analysis Path Loss And Frequency Dependency Analysis Shadowing Path Loss And Central Frequency Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 12 / 26
13 Small scale effects Small Scale Fading And Signal Quality Small scale effects The Indoor, Outdoor and Corridor UWB propagations constitute a channel with multiple paths. Many obstacles are present (walls, electric wires,...) Small scale variations in the channels response are caused by the recombination of the multiple paths. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 13 / 26
14 Small Scale Fading And Signal Quality Small scale effects count d Small scale effects count d L 1 h(t) = a l δ(t τ l )e jθ l (2) l=0 In Karedal 2004, it is stated that for an indoor channel the energy that falls within a certain delay bin is m-nakagami distributed. In order to analyze if this is the case for our measurements, the 70 amplitude values hi (i = 1, 2,...70) are fitted to the m Nakagami distribution using the m-estimates given by the inverse normalized variance (INV) estimator Abdi ˆm INV = µ2 2 µ 4 µ 2 2 (3) where µ k = 1 l=l h l k (4) L l=1 Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 14 / 26
15 Small Scale Fading And Signal Quality Small scale effects count d Small scale effects count d Estimated m parameters Estimated m parameters Time in ns Time in ns Figure: NLOS. The m-parameter estimates for each delay bin of a 6 meters LOS and 0.35 Empirical probability density fonction PDF Estimated m parameters Figure: PDF for the m-parameter estimated for each delay bin NLOS. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 15 / 26
16 Small Scale Fading And Signal Quality Signal Quality Analysis Signal Quality Analysis To quantify the variation of the received power for small-scale variations, let us consider the signal quality as defined by Muqaibel et al.: Q = 10 log 10( E E 0 ) E = T 0 r 2 (t)dt (5) LOS 6 meters LOS 9 meters NLOS 6 meters CDF of Signal Quality Signal Quality (db) Figure: CDF of the signal quality based on 130 spatial sample points. This result confirms the robustness of UWB communication systems. Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 16 / 26
17 Small Scale Fading And Signal Quality The Dispersive Properties Of UWB Channel The Dispersive Properties Of UWB Channel The τ m and τ rms are two parameters to be characterize for concluding about channel and large scale. These are useful as single number descriptions of the channel to estimate the performance and potential for inter symbol interference (ISI). The mean excess delay τ m is defined as the first and the τ rms is seen to be the second centralized moment of the normalized power delay profile Real channel LOS 6 GHz Normal distribution 0.9 Real channel LOS 6 GHz Lognormal distribution Cumulative probability Cumulative probability τ x 10 9 rms in ns τ m in ns x 10 9 Figure: CDF of τ m and τ rms fit to normal distribution under the LOS setting. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 17 / 26
18 Small Scale Fading And Signal Quality The Dispersive Properties Of UWB Channel The Dispersive Properties Of UWB Channel count d In Muqaibel an other important parameter ρ = τ m /τ rms measure of the time dispersion. If 1 ρ = 1 the multipath delay profile decays exponentially the situation corresponds to two multipath components with equal power where the second path is 2τ m away from the first component 2 ρ < 1 high concentration of power when the excess delay is small 3 When energy arrives at the mid point of the power delay profile and not at the earliest part then ρ > 1. LOS CM1 LOS corridor LOS CM NLOS Laboratory CM2 NLOS Laboratory CM1 τ m in ns τ m mean excess in ns τ rms in ns τ rms delay spread in ns Figure: Ratio of τ m to τ rms for LOS and NLOS cases. Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 18 / 26
19 Path Loss Definition Large scales channel modeling Path Loss Analysis Definition The path loss PL defines the relationship between transmitted power P TX and received power P RX in a far field RF link. This relation was first given by Harald Friis: P(f, d) = P RX P TX = G TX G RX λ 2 (4π) 2 d 2 (6) For UWB systems the path loss modeling can be given: P(f, d) = PL(f ) PL(d) (7) Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 19 / 26
20 Large scales channel modeling Path Loss And Distance Dependency Analysis Path Loss And Distance Dependency Analysis The path loss PL(d) can be calculated directly from the measured channel function transfer H(f, d, t n ). PL k sp(d) = m=m 1 1 M N m=0 N 1 n=0 H k (f m, t n ; d) 2, (8) with k = 1,...K. where H k (f m, t n ; d) denotes the n th channel function transfer snapshot at frequency f m and at local point k in a distance d. The local path loss: PL lc (d) = 1 K The PL in db as function distance m=k 1 k=0 PL k sp(d) (9) PL(d) = PL n log 10 d d 0 + S; d 0 < d (10) UWB channel Eurecom measurements we have obtained n = 1.4 for LOS case. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling / 26
21 Large scales channel modeling Path Loss And Frequency Dependency Analysis Path Loss And Frequency Dependency Analysis Most of the measurement results in the literature reported that narrowband model can be used to approximate the PL for UWB systems that is, the PL is independent on the frequency expect two published works in references Alvares 2003 and Kunisch Two models of path loss frequency dependence are mainly used: PL(f ) k.e δ 1f (11) PL(f ) f δ 2 (12) Measured data Alvarez Method Kunisch Method Measured data Alvarez Method Kunisch Method Magnitude in db Magnitude in db Frequence in GHz Frequence in GHz Figure: Path loss as a function of frequency for LOS and NLOS. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 21 / 26
22 Large scales channel modeling Path Loss And Central Frequency Path Loss And Central Frequency In order to assess the relation between the central frequency and the frequency channel behavior we evaluate the path loss versus central frequency the used equation is given by PL(f c, B) = 1 m=m MN m=1 f c+b/2 f i =f c B/2 H(f i, t m, f c ) 2. (13) Path Loss in db y = 0.001*x 51 LOS case Measured Data Linear Path Loss in db y = *x 61 Corridor case Measured Data Linear Path Loss in db Central Frequency in MHz NLOS case 50 Measured Data y = *x 58 Linear Path Loss in db Central Frequency in MHz Outdoor case 55 Measured Data Linear y = *x Central Frequency in MHz Central Frequency in MHz Figure: Path loss versus central frequency: LOS, NLOS, Corridor and Outdoor. achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 22 / 26
23 Shadowing Large scales channel modeling Shadowing Due to variations in the surrounding environments, PL observed at any given point will deviate from its average value Rappaport. The statistical analysis of S gives σ S = 1.1 LOS 6 meters 0.9 Normal Fit 0.9 NLOS 6 meters Normal Fit CDF 0.5 CDF S in db S in db LOS Corridor 6 meters Normal Fit 0.9 LOS 9 meters Normal Fit CDF 0.5 CDF S in db S in db Figure: Cumulative distribution functions (CDFs) of shadowing fading fit to lognormal distribution under the LOS and NLOS scenarios for different achid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 23 / 26
24 Conclusions Conclusions We have investigated UWB propagation channels in different environments We established a statistical model that describes the behavior of the channel We found that the power variation and the path loss can be well described by a Weibull distribution model. We have observed that no correlation between the path loss and the frequency bandwith of interest A correlation between the central frequency and path loss is indicated for most measured data The calculated time dispersion parameters for the measured results indicate high concentration of power at low excess time delays that for all scenarios. Rachid Saadane rachid.saadane@gmail.com ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 24 / 26
25 Thank You for attention! Any Questions? Rachid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 25 / 26
26 Selected References Federal Communications Commission: FCC press release, Feb T. S. Rappaport, S. Y. Seidel, and K. Takamizawa, Statistical channel impulse response models for factory and open plan building radio communication system design, IEEE Transaction Communication on, Vol. 39, 1991, pp J. Kunisch and J. Pamp, Measurement results and modeling aspects for the UWB radio channel, IEEE Conference on Ultra Wideband Systems and Technologies, Baltimore, MD,USA, May, 2002, pp Chia-Chin Chong and Young-Eil Kim and Su Khiong Yong and Seong-Soo Lee, Statistical characterization of the UWB propagation channel in indoor residential environment, 2005, pp J. Keignart and J. B. Pierrot and N. Danièle and Á. Álvarez and M. Lobeira and J. L. Garcí and G. Valera, R. P. Torres (CAN), Radio channel sounding results and model, U.C.A.N., 2003, Deliverable number: D31, IST achid Saadane ( GSCM Ultra Wideband LRIT) Radio Propagation Measurement, Characterization April 14, and 2007Modeling 26 / 26
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