Ray Tracing and Site-Specific Modeling
From Statistical to Deterministic Models
The models in Sections 5.3β5.5 predict average behaviour over a class of environments. Ray tracing takes the opposite approach: given a detailed 3D model of the environment (buildings, terrain, vegetation), it traces individual wave paths from transmitter to receiver, applying reflection, diffraction, and scattering at each interaction. The result is a site-specific prediction of received power, delay spread, and angle of arrival.
Definition: Ray Tracing
Ray Tracing
Ray tracing (or ray launching) is a deterministic propagation prediction method based on geometrical optics (GO) and the uniform theory of diffraction (UTD).
Algorithm outline:
- Launch rays from the transmitter in many directions
- At each surface intersection:
- Compute reflected ray (Fresnel coefficients)
- Compute diffracted rays (UTD coefficients)
- Optionally compute scattered rays
- Collect rays arriving at the receiver location
- Sum contributions (amplitude and phase) to compute:
- Total received power
- Channel impulse response
- Angle of arrival/departure
Modern ray tracers use GPU acceleration and can handle millions of rays in urban environments.
Definition: Method of Images
Method of Images
For specular reflections from planar surfaces, the image method efficiently finds all reflection paths. For a single reflecting surface, the reflected path from TX to RX via the surface is equivalent to the direct path from the image of TX (mirrored through the surface) to RX.
For reflecting surfaces, there are up to image combinations per reflection order. In practice, the number of significant paths is limited by the maximum number of reflections (typically 3β6).
Historical Note: Ray Tracing in Wireless β From Luxury to Necessity
Early ray tracing for wireless (1990s) required hours of computation for a single building. By the 2010s, GPU acceleration made city-scale ray tracing feasible. Today, ray tracing is essential for:
- 5G mmWave planning: the high directionality and sensitivity to blockage at mmWave frequencies demand site-specific models
- Digital twins: real-time ray tracing enables continuous network optimisation using live 3D maps
- 3GPP channel models: the 3GPP TR 38.901 ray-tracing-based approach generates realistic channel realisations for system-level simulations
Statistical vs. Deterministic Propagation Models
| Aspect | Statistical models | Ray tracing |
|---|---|---|
| Input required | Environment type only | Detailed 3D geometry |
| Computation | Milliseconds | Minutes to hours |
| Accuracy (urban) | 6β10 dB RMSE | 3β6 dB RMSE |
| Frequency range | Model-specific | Any (with material data) |
| Channel parameters | Path loss only | Full CIR, AoA, AoD |
| Use case | Initial planning, system-level | Detailed site-specific |
Why This Matters: Ray Tracing and Digital Twins for 6G
Digital twins β real-time virtual replicas of the physical network β are a key technology vision for 6G. Ray tracing provides the propagation engine: given up-to-date 3D maps (from LiDAR, cameras, or crowdsourced data), ray tracing predicts how the channel will behave if a base station is repositioned, a beam is steered, or a user moves. Combined with machine learning for acceleration, ray tracing enables proactive resource management and predictive handover.
Common Mistake: Ray Tracing Is Not Exact
Mistake:
Treating ray-tracing results as ground truth for channel behaviour.
Correction:
Ray tracing relies on approximate models (GO, UTD) and imperfect environment data (building materials, vegetation, interior layouts). Typical prediction errors are 3β6 dB RMSE in urban environments β better than statistical models but still significant. Always validate with measurements when possible.
Quick Check
Which of the following can ray tracing predict but the Hata model cannot?
Average path loss at a distance
Multipath delay spread and angle of arrival
Free-space path loss
Frequency of operation
Correct. Ray tracing computes individual paths with their delays, angles, and amplitudes, giving the full channel impulse response. Statistical models like Hata only predict average path loss.
Why This Matters: From Propagation to Channel Models
Ray tracing does not just predict path loss β it produces the full channel impulse response with individual multipath components, their delays, angles of arrival/departure, and Doppler shifts. This makes it a key tool for generating realistic MIMO channel realisations (Chapter 15), validating stochastic channel models, and studying the spatial multiplexing gains that depend on the angular spread of arrivals. In the RFI book, ray tracing provides the forward model for RF imaging: the sensing matrix is constructed from the propagation paths between transmit/receive antennas and the target voxels.
See full treatment in The MIMO Channel Matrix
Ray Tracing
A deterministic propagation method that traces individual wave paths through a 3D environment model, computing reflections, diffractions, and scattering.
Related: Geometrical Optics, Utd, Digital Twin
Digital Twin
A real-time virtual replica of the physical network, using ray tracing and sensor data for continuous optimisation.
Related: Ray Tracing, Ray Tracing and Digital Twins for 6G, Network Planning