Table 1.
State-of-the-art related works on path-loss models in tunnel environments.
| Type | Methods | Models | Pros and Cons | Ref. |
|---|---|---|---|---|
| Straight tunnel |
Fit measurement results using regression method |
FI model | Low complexity Insufficient accuracy |
[21] |
| Superpose multiple modes in both near and far region | Multimode model | High accuracy Limited applicability |
[22] | |
| Calculate Per-ray cone angle | Improved RT model | High accuracy Low computational efficiency |
[23] | |
| Extract rectangular waveguide model using VPE | Mixed model based on waveguide and VPE |
Reduced complexity Limited Validity |
[24,25] | |
| Curved tunnel |
Introduce a break point distance into the CI model |
Improved CI model | High accuracy Less stability |
[26] |
| Divide propagation region into LOS and NLOS | Two-slope model | Realistic scenario Large deviation |
[27] | |
| Define the break point between two waveguiding effects | Improved FI model with break point | High accuracy Calculations of break point required |
[28] | |
| Estimate the main effects of the curvature on multimode | Mixed model based on waveguide and RT | Low complexity Insufficient accuracy |
[29] | |
| Combine RT method with neural network | Improved RT model | High applicability High complexity |
[30] | |
| Cascaded tunnel |
Fit measurement results using regression method |
CI model | Low complexity Insufficient accuracy |
[31] |
| Reconstruct a high-precision 3D model of measurement tunnel | RT model | High accuracy High-precision 3D model required |
[32] | |
| Divide space into segments to solve stability constraint | Improved FDTD model |
High accuracy high complexity |
[33] |