# -*- coding: utf-8 -*-
from __future__ import annotations
import numpy as np
from hermespy.core import SerializableEnum
from .fading import MultipathFadingChannel
__author__ = "Tobias Kronauer"
__copyright__ = "Copyright 2024, Barkhausen Institut gGmbH"
__credits__ = ["Tobias Kronauer", "Jan Adler"]
__license__ = "AGPLv3"
__version__ = "1.3.0"
__maintainer__ = "Jan Adler"
__email__ = "jan.adler@barkhauseninstitut.org"
__status__ = "Prototype"
[docs]
class TDLType(SerializableEnum):
"""Supported model types of the 5G TDL channel model"""
A = 0
B = 1
C = 2
D = 4
E = 5
[docs]
class TDL(MultipathFadingChannel):
"""5G TDL Multipath Fading Channel models."""
yaml_tag = "5GTDL"
__rms_delay: float
def __init__(
self,
model_type: TDLType = TDLType.A,
rms_delay: float = 0.0,
gain: float = 1.0,
doppler_frequency: float | None = None,
los_doppler_frequency: float | None = None,
**kwargs,
) -> None:
"""
Args:
model_type (TYPE):
The model type.
Initializes the :attr:`model_type` attribute.
rms_delay (float):
Root-Mean-Squared delay in seconds.
Initializes the :attr:`rms_delay` attribute.
alpha_device (SimulatedDevice, optional):
First device linked by this :class:`MultipathFading5GTDL` channel instance.
Initializes the :attr:`alpha_device` property.
If not specified the channel is considered floating,
meaning a call to :meth:`realize<Channel.realize>` will raise an exception.
beta_device (SimulatedDevice, optional):
Second device linked by this :class:`MultipathFading5GTDL` channel.
Initializes the :attr:`beta_device` property.
If not specified the channel is considered floating,
meaning a call to :meth:`realize` will raise an exception.
num_sinusoids (int, optional):
Number of sinusoids used to sample the statistical distribution.
doppler_frequency (float, optional)
Doppler frequency shift of the statistical distribution.
\***kwargs (Any):
Additional `MultipathFadingChannel` initialization parameters.
Raises:
ValueError: If `rms_delay` is smaller than zero.
ValueError: If `los_angle` is specified in combination with `model_type` D or E.
"""
if rms_delay < 0.0:
raise ValueError("Root-Mean-Squared delay must be greater or equal to zero")
self.__rms_delay = rms_delay
# ETSI TR 38.900 Table 7.7.2-1: 5G TDL-A
if model_type == TDLType.A:
normalized_delays = np.array(
[
0,
0.3819,
0.4025,
0.5868,
0.4610,
0.5375,
0.6708,
0.5750,
0.7618,
1.5375,
1.8978,
2.2242,
2.1717,
2.4942,
2.5119,
3.0582,
4.0810,
4.4579,
4.5695,
4.7966,
5.0066,
5.3043,
9.6586,
]
)
power_db = np.array(
[
-13.4,
0,
-2.2,
-4,
-6,
-8.2,
-9.9,
-10.5,
-7.5,
-15.9,
-6.6,
-16.7,
-12.4,
-15.2,
-10.8,
-11.3,
-12.7,
-16.2,
-18.3,
-18.9,
-16.6,
-19.9,
-29.7,
]
)
rice_factors = np.zeros(normalized_delays.shape)
# ETSI TR 38.900 Table 7.7.2-2: 5G TDL-B
elif model_type == TDLType.B:
normalized_delays = np.array(
[
0,
0.1072,
0.2155,
0.2095,
0.2870,
0.2986,
0.3752,
0.5055,
0.3681,
0.3697,
0.5700,
0.5283,
1.1021,
1.2756,
1.5474,
1.7842,
2.0169,
2.8294,
3.0219,
3.6187,
4.1067,
4.2790,
4.7834,
]
)
# ETSI TR 38.900 Table 7.7.2-3: 5G TDL-C
power_db = np.array(
[
0,
-2.2,
-4,
-3.2,
-9.8,
-3.2,
-3.4,
-5.2,
-7.6,
-3,
-8.9,
-9,
-4.8,
-5.7,
-7.5,
-1.9,
-7.6,
-12.2,
-9.8,
-11.4,
-14.9,
-9.2,
-11.3,
]
)
rice_factors = np.zeros(normalized_delays.shape)
# ETSI TR 38.900 Table 7.7.2-3: 5G TDL-C
elif model_type == TDLType.C:
normalized_delays = np.array(
[
0,
0.2099,
0.2219,
0.2329,
0.2176,
0.6366,
0.6448,
0.6560,
0.6584,
0.7935,
0.8213,
0.9336,
1.2285,
1.3083,
2.1704,
2.7105,
4.2589,
4.6003,
5.4902,
5.6077,
6.3065,
6.6374,
7.0427,
8.6523,
]
)
power_db = np.array(
[
-4.4,
-1.2,
-3.5,
-5.2,
-2.5,
0,
-2.2,
-3.9,
-7.4,
-7.1,
-10.7,
-11.1,
-5.1,
-6.8,
-8.7,
-13.2,
-13.9,
-13.9,
-15.8,
-17.1,
-16,
-15.7,
-21.6,
-22.8,
]
)
rice_factors = np.zeros(normalized_delays.shape)
# ETSI TR 38.900 Table 7.7.2-4: 5G TDL-D
elif model_type == TDLType.D:
if los_doppler_frequency is not None:
raise ValueError(
"Model type D does not support line of sight doppler frequency configuration"
)
normalized_delays = np.array(
[
0,
0.035,
0.612,
1.363,
1.405,
1.804,
2.596,
1.775,
4.042,
7.937,
9.424,
9.708,
12.525,
]
)
power_db = np.array(
[
-13.5,
-18.8,
-21,
-22.8,
-17.9,
-20.1,
-21.9,
-22.9,
-27.8,
-23.6,
-24.8,
-30.0,
-27.7,
]
)
rice_factors = np.zeros(normalized_delays.shape)
rice_factors[0] = 13.3
los_doppler_frequency = 0.7
# ETSI TR 38.900 Table 7.7.2-5: 5G TDL-E
elif model_type == TDLType.E:
if los_doppler_frequency is not None:
raise ValueError(
"Model type E does not support line of sight doppler frequency configuration"
)
normalized_delays = np.array(
[
0,
0.5133,
0.5440,
0.5630,
0.5440,
0.7112,
1.9092,
1.9293,
1.9589,
2.6426,
3.7136,
5.4524,
12.0034,
20.6519,
]
)
power_db = np.array(
[
-22.03,
-15.8,
-18.1,
-19.8,
-22.9,
-22.4,
-18.6,
-20.8,
-22.6,
-22.3,
-25.6,
-20.2,
-29.8,
-29.2,
]
)
rice_factors = np.zeros(normalized_delays.shape)
rice_factors[0] = 22
los_doppler_frequency = 0.7
else:
raise ValueError("Requested model type not supported")
self.__model_type = TDLType(model_type)
# Convert power and normalize
power_profile = 10 ** (power_db / 10)
power_profile /= sum(power_profile)
# Scale delays
delays = rms_delay * normalized_delays
# Init base class with pre-defined model parameters
MultipathFadingChannel.__init__(
self,
gain=gain,
delays=delays,
power_profile=power_profile,
rice_factors=rice_factors,
doppler_frequency=doppler_frequency,
los_doppler_frequency=los_doppler_frequency,
**kwargs,
)
@property
def model_type(self) -> TDLType:
"""Access the configured model type.
Returns:
MultipathFading5gTDL.TYPE: The configured model type.
"""
return self.__model_type
@property
def rms_delay(self) -> float:
"""Root mean squared channel delay.
Returns: Delay in seconds.
"""
return self.__rms_delay