Propagation Modelling

The lab is equipped with 3D Ray Tracing algorithm for deterministic propagation modelling.

3D Ray Tracing

Ray Tracing AlgorithmParallel Ray TracingStatistical Modelling
The algorithms features:

  • 3D RT predictions for different propagation scenarios.
  • Parallel processing with illumination zones to speed-up predictions for very large vector building databases (with multiple ray interactions).
  • Predictions for received power and/or EM field vector (depolarization considered). Full Impulse Response: frequency, time, angle of arrival and angle of departure in azimuth and elevation.
  • Consideration of any antenna pattern.
  • Point-Route-Area analyses for multiple system and network planning parameters e.g. clearance, power, delay spread, coherence bandwidth, K-factor, angular spread (azimuth and elevation), outage, etc.

While it considers all the main aspects of different wireless environments:

Large cell scenarios
Multiple reflections
Multiple roof top and terrain diffractions
Foliage attenuation
Loss due to Fresnel zone blockage for long distance propagation
Microcells
Multiple reflections and corner diffractions
Foliage attenuation
Indoor Scenarios
Indoor, indoor-to-outdoor and outdoor-to-indoor propagation
Walls with different materials (e.g. walls with large windows)
Multiple wall reflections and corner diffractions
Floor-ceiling reflections

 

A parallel ray tracing algorithm for radio-wave propagation prediction based on the electromagnetic theory of images is also developed. The implementation of the algorithm is based on the message passing interface (MPI). The decomposition of the problem is conducted by partitioning the image tree, while dynamic load balancing techniques are employed by means of the master–worker and the work–pool patterns. The analysis of the parallel implementation performance for different problems and task assignment schemes, has shown that high speedups are achieved.

computation_decomposition

Computation Decomposition

data_base
efficiency_vs_processors
speedup
Outage probability of a triple-branch SC receiver as function of the outage threshold for a linearly arbitrary correlated Weibull model.

Outage probability of a triple-branch SC receiver as function of the outage threshold for a linearly arbitrary correlated Weibull model.

Hypothesis testing distribution using the K–S goodness-of-fit test for the N∗Nakagami to approximate the lognormal distribution with 5% significance level

Hypothesis testing distribution using the K–S goodness-of-fit test for the N∗Nakagami to approximate the lognormal distribution with 5% significance level

Key Publications: