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  imulaio o igial Radio Modiale ael Model Kui Li GxSOC Research Institute Communication University of China Beijing, China liki Abstract-Th paper analyzes the channel model of igital Radio Mondiale and discusses the parameters given by the specication. Watterson model is used to simulate the radio channel. We describe the implementation of Watterson channel model in details, including the channel multi-path and timing-variability. Multi-level interpolator is used in our realization scheme to improve calculation eciency. The simulation results are given and indicate that the timing and frequency selective fading effect of the DRM channel are well modeled. Keywords D WSSUS; Watterson Model; Multi path; Doppler Spread; Muliinterpolate . INOON Digita Radio Mondiae (DRM) is a word-wide specication for Amplitde odlation (AM) broadcasting. It is used to repace te traditiona anaogue AM roadcasting for its advantage such as robustness in fading channels, beter adio qality, power sings at the  transmiting side. The eqency of DRM is below 30MHz. t  this roadcasting equency band, te signals are  transmited by sky-wave or ground-wave or both of them. The characteristics of DRM channel is complex becase of te instability of the ionospere and the muti-hop propagation beteen the eart s surface and te ionospere. It is a typical multi-path and time-varying channel that presents deep time and eqency selective fading. The channel modeing and simulation is signicant for the systems design, impementation and performance evauation. The DRM specication gives the channel model [1]. In this paper,  we discss  the model realization in detail based on the given channe caracteristics and parameters. I. DRM CHANNEL M OEL DRM channel given by the specication is described as an adaptable parameters Wide Sense Stationary Uncorrelated Scatering (WSSUS) model.  is a stocastic  time-varying mode wit a stationary statistics and several sets of appropriate parameter vales. The parameters include the nmbers of multi-path, path delay, path gain, oppler shi, and Dopper spread. Tae-I shows one of the six sets parameters given by the specication. The nmber of discrete multi-path, each path delay and gain describes the equency seective fading of the channel. Te Doppler si and spread presents e channe s  time seective fading. In 98--8-8-/$00 ©0 IEEE 35 Van Ming GxSOC Research Institute Communication University of Cina Beijing, China  the WSSUS model, each path of the channel is regarded as independent. TABLE  D HANNL PAATS Path t Path  Path  Path 4 el 0 2 ms 4 ms 6 ms ath Gain, nS 0.5 1 0.25 0.0625 Doppler Shi 0 1.2 Hz 2.4 Hz 3.6 Hz Doppler Spread 0.1 Hz 2.4 Hz 4.8 Hz 7.2 Hz The WSSUS channel model is expressed as: h T    L   T -Tn (1)  P k is the attenuation of the k-t pat, describing te  relative path gain of each path. r k is the relative delay of the k-th path. {e k (t)} are the time-variant tap  weights, and they are zeros mean complex-vaued stationary Gaussian random processes  with nit  variance. The magnitudes Ie k (t)1 are Rayeig-distriuted and te phases (t) are uniformy distribted. Each {e k (t)} is caracterized y its Power Density Spectrum (PDS) that determines the average speed of  variation in time. The wi of the PDS is quantied by Dopper spread wich is specied as 2-side PDS and contains 68% of the power. The PDS has a Gassian shape for the ionosphere path based on real observation. There might be a non-zeros center equency of te PDS, wich is an average equency si dened y Doppler shi. III. MPLEMENTAION OF CHAEL ODEL The WSSUS channel Model can be implemented by Watterson mode as sown in Fig. I. Waterson channe mode is a transversa lter were  taps g k (t) are complex and vary with time [2].  is an ideal discrete time delay model and the vaues of deay  refer to Tae-. The taps of each path is independent [3][4][5]. { g k(t)} are complex  valued stationary Gaussian random processes and teir PDS can be descried y:  P f)=   P k  exp[_� f-fDSh k)2]  (2)  k _k 5  _k   P k is te atenuation of te k-t pat, f  DS  P _k  the Dopper spread and h k is te Doppler shi. se l - r t) Figure 1. Watterson hannel Model. The generation of time-varying taps can be divided into  three independent parts:  the path gain, oppler shi simulation and oppler spread simlation, as shown in Fig. 2. r-------------- igure 2. Three Pars of Taps in Waterrson Model Te g k (t) can be expressed as: g k(t) = P k . c k(t)· }J _k (3) P k and Ck(t) are descried in Section I. The path gain directy multipies on each path. Multipy }_k  to eac pat  to reaize  the Dopper shi effect. The key of oppler spread eect simlation is the taps C k (t) generation. Te zero-means, unit varance stochastic processes {Ck (t)} ave Gaussian Probaiity Density Function (PDF) and Gaussian shapes PDS which are controled y  the Doppler spread, as described in Section I. We generate C k (t) trogh  the following three steps: 1  Generate a zero mean and unit variance compexvaued Gaussian wite noise. 2. Filter the Gassian  white noise to forming a color noise with the required PDS. 3. Ierpoate te tered sampes for matc  the samping  rate. This process illustrates in Fig. 3. 36 Figure 3. Generation of Time-varing Taps Te compex-vaued Gaussian white noise is produced by Box-Muler algoritm. In step-2, because ltering is a linear operation,  the color noise has the same PDF with the srcinal wite noise. Te lter is designed based on the PDS  requirements and its coefcients are dened y: r; 22 h(t) = e J { (4)  f  DS  P is the Doppler spread. The pass band width of the Gaussian lter is controlled by oppler spread We can calclate  the taps by a lower speed for efciency ecause te channe  timing seective fading is far lower than  the system sampling eqency, bt it mst be at least 32  times than the Doppler spread  values for accuracy [6]. At the same time, an ierpoator is used for match te sampling equency of taps and signa sampes. F or different values of oppler spread, the taps updating  rate maybe different to dozens of times. At this condition, use the same sets of lter coefcients and through adjustable mlti-level interpolator control the lter pass band can simpi  the simuation design. IV. IULATION RESULTS The simulation of multi-path and Doppler shi of the channel is straightforward. The key point of the model simuation is te  time-varying taps generation. The magnitude and phase of the compex-valued stationary Gaussian random processes sample nction C k (t) is shown in Fig and Fig. 5. Te magnitude is Rayeigdistribted and the phase is uniformly distribted as expected. igure 4. The magnitudes distribution of C k   Figure 5. The angles distribution of C k  The power density spectrum of ck (t) is shown in Fig. 6 and Fig. 7. The magnitude in time-domain is sown in Fig. 8 and Fig. 9. The vale of the Doppler spread decides the timeselective fading degree. Te simulation results show tey matc  well. Figure 6. The PDS of Ck , Doppler spread = 7 2 H  z Figure 7. The S of Ck  (t), oppler sp rea d   = AHz  V. CONLUSION In is paper, we discuss the DM channe model in detail. Te aterson model is used to simuation  the channel. The tapped delay line with time-varying taps simuates te equency and time seective fading. 37 Figure 8. Magnitude of C k  in time domain, Doppler spread = 7 2 H  z Figure 9. Magnitude of C k  in time domain, Doppler spread = 2AHz e generate the taps y ltering Gaussian wite noise to form color noise with required PDS which derives om the channel Doppler spread. In our simulation scheme, mltievel interpolate is used to adapt wide dynamic range of Doppler spread. Te simulation resuts show tat te scheme can simlate  the DRM channel accurately and efciently. REFEENES [I] ETSI ES 201980 V2.1.1 (2004-06), igital adio Mondiale (M) System Specification. [ 2] C C Watterson, J. R Juroshek, W. D. Bensema, Experimental onrmation of an H hannel odel , IEEE Trans. On omm. Tech, Vol. OM-18,No. 6,Dec. 1970. [ 3] Yip, K. W, Ng, T.S., An analytic discrete-time model for a fading dispersive WSSUS channel, IEEE Vehicular Technology onference, 1994 I EEE 44th, 8-10 June 1994 age(s): 180-184 vo!. 1. [ 4] Angling, M l, Davies, N. C An assessment of a new ionospheric channel model driven by measurements of multi-path and Doppler spread, IEEE Frequency Selecltion and Management Techniques for HF ommunications (Re No. 1999/017), EE olloquium on, 29-30 March 1999 Page(s): 41 4/6. [ 5] Behm, C 1 A narrowband high equency channel simulator with delay spread, IEEE HF Radio Systems and Techniques, Seventh Inteational onference on (on Pub . No. 441),7-10 July 1997 Page(s) 388-391. [ 6] W Furman, 1 Nieto, Harris orporation, Understanding HF channel simulator requirements in order to reduce H moderm performance measurement variabilit, Proceedings of HF I, the Nordic HF onference, August 2001.
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