## Contents ## Introduction ## Movement of TX/RX ## Movement of scatterers ## Implementation issues ## Impact on system evaluation ## Summary and conclusions
## Introduction ## Current status of model: ## Temporal variations of channel not yet defined
## Basic modeling approach ## Two causes of temporal variation: - TX/RX movement
- Scatterer movement (shadowing)
## Different modeling approaches:
## TX-RX movement ## WSSUS model (Bello 1963) ## Angular spectrum Doppler spectrum ## Doppler spectrum temporal autocorrelation ## Fourier transformation
## Simple model for angular spectrum: rectangular (instead of Laplacian) ## Angular spread increases with delay ## Total angular spread: 38 degree [Cramer et al. 2002]
## Moving scatterers ## Model scenario: person moving through LOS - On straight line 80cm from receiver
- Person modeled as cylinder with 40cm radius
- No transmission through or diffraction around scatterer considered
## Shadowing certain angular region ## Means different attenuation for different delays
## Implementation recipe (I) ## TX/RX movement: - Compute ACF for each delay from angular spectrum
- Generate iid random variables
- Filter (FFT implementation most efficient)
- “standard” implementations for Rayleigh fading do not work
## Implementation recipe (II) ## Scatterer movement - Geometrical approach
- At discrete position of scatterers:
- Linear interpolation between discrete scatterer positions
## Impact on system evaluation ## Current suggestion: independent channel realization for each data block ## Other extreme case: no temporal variations ## Why smooth transitions required? - Channel tracking and synchronization
- How long are outages (needs application model)
- Smart antennas
## Summary ## Simple model for temporal variations ## Two cases: - RX/TX movement, use WSSUS model
- Person walking through LOS; use geometrical approach
## Suggestion for system evaluation: - All proposals: with independent data blocks and complete coherence
- For system with trackers (if desired by proponent): continuous model
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