Parameter Generation & Time Evolution

Geometric value-based scatterer channel modeling, simulating real spatial multipath environments

Real-World Applications

Channel Time Evolution: Simulating users walking from LOS (line-of-sight) areas into NLOS (non-line-of-sight) shadow areas. For example, pedestrians walking from open plazas into alleys, or driving under overpasses. The gradual change of channel parameters (K-factor, delay spread, angular spread) is very important for link adaptation (AMC) and power control algorithm design.

Experiment Steps

Geometric Distribution: Observe each propagation path formed by random scatterers
Delay Spread: Larger DS means wider scatterer distribution and larger delay difference

Simulation Experiment

Geometric Stochastic Channel (GSCM) (Top View / PDP)

Top View (Geometry)

Time: s

UE Velocity: 15 m/s

Reference: 3GPP TR 38.901

Parameter Generation:
Corresponds to 3GPP TR 38.901 Section 7.5.
Uses Log-Normal distribution to generate delay spread (DS) and angular spread (AS), matching real channel statistics.
Time Evolution:
Corresponds to 3GPP TR 38.901 Section 7.6.3 (Spatial Consistency).
Click play button to observe Doppler shift and phase drifting as UE moves.
Phase changes cause PDP power to exhibit Fast Fading.
Scatterers:
Gray dots represent virtual obstacles (buildings, trees) in the environment.
Even without specific shapes, they mathematically act as reflection points, producing multipath signals.

Parameters(Real-time)

Num Clusters8

Delay Spread (DS)30 ∝ns

Carrier Freq3.5 GHz

Functions: models.channel.initGSCMChannel, models.channel.updateGSCMChannel

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