# -*- coding: utf-8 -*-
from __future__ import annotations
from enum import Enum
from typing import Mapping, Set, Tuple, Type
from math import exp, log10
import numpy as np
from h5py import Group
from hermespy.core.factory import Serializable
from .cluster_delay_lines import (
ClusterDelayLineBase,
ClusterDelayLineSample,
ClusterDelayLineSampleParameters,
ClusterDelayLineRealizationParameters,
ClusterDelayLineRealization,
LOSState,
)
from ..channel import ChannelSampleHook
from ..consistent import ConsistentGenerator, ConsistentRealization
__author__ = "Jan Adler"
__copyright__ = "Copyright 2024, Barkhausen Institut gGmbH"
__credits__ = ["Jan Adler"]
__license__ = "AGPLv3"
__version__ = "1.3.0"
__maintainer__ = "Jan Adler"
__email__ = "jan.adler@barkhauseninstitut.org"
__status__ = "Prototype"
[docs]
class OfficeType(Enum):
"""Type of office."""
MIXED = 0
"""Mixed office"""
OPEN = 1
"""Open office"""
[docs]
class IndoorOfficeRealization(ClusterDelayLineRealization[LOSState]):
"""Realization of an indoor office cluster delay line model."""
__los_realization: ConsistentRealization
__nlos_realization: ConsistentRealization
__office_type: OfficeType
def __init__(
self,
expected_state: LOSState | None,
state_realization: ConsistentRealization,
los_realization: ConsistentRealization,
nlos_realization: ConsistentRealization,
parameters: ClusterDelayLineRealizationParameters,
office_type: OfficeType,
sample_hooks: Set[ChannelSampleHook[ClusterDelayLineSample]],
gain: float = 1.0,
) -> None:
"""
Args:
expected_state (O2IState | None):
Expected large-scale state of the channel.
If not specified, the large-scale state is randomly generated.
state_realization (ConsistentRealization):
Realization of a spatially consistent random number generator for the large-scale state.
los_realization (ConsistentRealization):
Realization of a spatially consistent random number generator for small-scale parameters in the LOS state.
nlos_realization (ConsistentRealization):
Realization of a spatially consistent random number generator for small-scale parameters in the NLOS state.
parameters (ClusterDelayLineRealizationParameters):
General parameters of the cluster delay line realization.
office_type (OfficeType):
Type of the modeled office.
sample_hooks (Set[ChannelSampleHook[ClusterDelayLineSample]]):
Hooks to be called when a channel sample is generated.
gain (float, optional):
Linear amplitude scaling factor if signals propagated over the channel.
"""
# Initialize base class
ClusterDelayLineRealization.__init__(
self, expected_state, state_realization, parameters, sample_hooks, gain
)
# Initialize class attributes
self.__los_realization = los_realization
self.__nlos_realization = nlos_realization
self.__office_type = office_type
# Table 7.4.4-1 in TR 138.901 v17.0.0
@staticmethod
def _pathloss_dB(state: LOSState, parameters: ClusterDelayLineSampleParameters) -> float:
PL_LOS = (
32.4
+ 17.3 * log10(parameters.distance_3d)
+ 20 * log10(parameters.carrier_frequency * 1e-9)
)
if state == LOSState.LOS:
return PL_LOS
PL_NLOS = (
38.3 * log10(parameters.distance_3d)
+ 17.3
+ 24.9 * log10(parameters.carrier_frequency * 1e-9)
)
return max(PL_LOS, PL_NLOS)
def _small_scale_realization(self, state: LOSState) -> ConsistentRealization:
if state == LOSState.LOS:
return self.__los_realization
else:
return self.__nlos_realization
def _sample_large_scale_state(
self, state_variable_sample: float, parameters: ClusterDelayLineSampleParameters
) -> LOSState: # pragma: no cover
# Implementation of TR 138.901 v17.0.0 Table 7.4.2-1
if self.__office_type == OfficeType.MIXED:
if parameters.distance_2d <= 1.2:
los_probability = 1.0
elif parameters.distance_2d < 6.5:
los_probability = exp(-(parameters.distance_2d - 1.2) / 4.7)
else:
los_probability = exp(-(parameters.distance_2d - 6.5) / 32.6) * 0.32
elif self.__office_type == OfficeType.OPEN:
if parameters.distance_2d <= 5:
los_probability = 1.0
elif parameters.distance_2d <= 49:
los_probability = exp(-(parameters.distance_2d - 5) / 70.8)
else:
los_probability = exp(-(parameters.distance_2d - 49) / 211.7) * 0.54
if state_variable_sample < los_probability:
return LOSState.LOS
else:
return LOSState.NLOS
@staticmethod
def __parameter_dependency(carrier_frequency: float, factor: float, summand: float) -> float:
"""An implementation of the frequently used equation
.. math::
y = a \\log_{10}(1 + f_c) + b
Args:
carrier_frequency (float): Carrier frequency
factor (float): Factor scaling the logarithmic frequency dependency.
summand (float): Added constant.
Returns: The result.
"""
# Note that the standard does not lower-bound the frequency for the indoor-office scenario
# This might be an error!!!!
fc = carrier_frequency * 1e-9
return factor * log10(1 + fc) + summand
# Parameters for computing the mean delay spread
# TR 138.901 v17.0.0 Table 7.5-6
__delay_spread_mean: Mapping[LOSState, Tuple[float, float]] = {
LOSState.LOS: (-0.01, -7.629),
LOSState.NLOS: (-0.28, -7.173),
}
@staticmethod
def _delay_spread_mean(state: LOSState, carrier_frequency: float) -> float:
mean_parameters = IndoorOfficeRealization.__delay_spread_mean[state]
return IndoorOfficeRealization.__parameter_dependency(carrier_frequency, *mean_parameters)
# Parameters for computing the standard deviation of the delay spread
# TR 138.901 v17.0.0 Table 7.5-6
__delay_spread_std: Mapping[LOSState, Tuple[float, float]] = {
LOSState.LOS: (0.0, 0.18),
LOSState.NLOS: (0.1, 0.055),
}
@staticmethod
def _delay_spread_std(state: LOSState, carrier_frequency: float) -> float:
std_parameters = IndoorOfficeRealization.__delay_spread_std[state]
return IndoorOfficeRealization.__parameter_dependency(carrier_frequency, *std_parameters)
# Parameters for computing the mean angle of departure spread
# TR 138.901 v17.0.0 Table 7.5-6
__aod_spread_mean: Mapping[LOSState, float] = {LOSState.LOS: 1.60, LOSState.NLOS: 1.62}
@staticmethod
def _aod_spread_mean(state: LOSState, carrier_frequency: float) -> float:
return IndoorOfficeRealization.__aod_spread_mean[state]
# Parameters for computing the standard deviation of the angle of departure spread
# TR 138.901 v17.0.0 Table 7.5-6
__aod_spread_std: Mapping[LOSState, float] = {LOSState.LOS: 0.18, LOSState.NLOS: 0.25}
@staticmethod
def _aod_spread_std(state: LOSState, carrier_frequency: float) -> float:
return IndoorOfficeRealization.__aod_spread_std[state]
# Parameters for computing the mean angle of arrival spread
# TR 138.901 v17.0.0 Table 7.5-6
__aoa_spread_mean: Mapping[LOSState, Tuple[float, float]] = {
LOSState.LOS: (-0.19, 1.781),
LOSState.NLOS: (-0.11, 1.863),
}
@staticmethod
def _aoa_spread_mean(state: LOSState, carrier_frequency: float) -> float:
mean_parameters = IndoorOfficeRealization.__aoa_spread_mean[state]
return IndoorOfficeRealization.__parameter_dependency(carrier_frequency, *mean_parameters)
# Parameters for computing the standard deviation of the angle of arrival spread
# TR 138.901 v17.0.0 Table 7.5-6
__aoa_spread_std: Mapping[LOSState, Tuple[float, float]] = {
LOSState.LOS: (0.12, 0.119),
LOSState.NLOS: (0.12, 0.059),
}
@staticmethod
def _aoa_spread_std(state: LOSState, carrier_frequency: float) -> float:
std_parameters = IndoorOfficeRealization.__aoa_spread_std[state]
return IndoorOfficeRealization.__parameter_dependency(carrier_frequency, *std_parameters)
# Parameters for computing the mean zenith of arrival spread
# TR 138.901 v17.0.0 Table 7.5-6
__zoa_spread_mean: Mapping[LOSState, Tuple[float, float]] = {
LOSState.LOS: (-0.26, 1.44),
LOSState.NLOS: (-0.15, 1.387),
}
@staticmethod
def _zoa_spread_mean(state: LOSState, carrier_frequency: float) -> float:
mean_parameters = IndoorOfficeRealization.__zoa_spread_mean[state]
return IndoorOfficeRealization.__parameter_dependency(carrier_frequency, *mean_parameters)
# Parameters for computing the standard deviation of the zenith of arrival spread
# TR 138.901 v17.0.0 Table 7.5-6
__zoa_spread_std: Mapping[LOSState, Tuple[float, float]] = {
LOSState.LOS: (-0.04, 0.264),
LOSState.NLOS: (-0.09, 0.746),
}
@staticmethod
def _zoa_spread_std(state: LOSState, carrier_frequency: float) -> float:
std_parameters = IndoorOfficeRealization.__zoa_spread_std[state]
return IndoorOfficeRealization.__parameter_dependency(carrier_frequency, *std_parameters)
@staticmethod
def _rice_factor_mean() -> float:
return 7.0
@staticmethod
def _rice_factor_std() -> float:
return 4.0
# Delay scaling factors for different LOS states
# TR 138.901 v17.0.0 Table 7.5-6
__delay_scaling: Mapping[LOSState, float] = {LOSState.LOS: 3.6, LOSState.NLOS: 3.0}
@staticmethod
def _delay_scaling(state: LOSState) -> float:
return IndoorOfficeRealization.__delay_scaling[state]
# Mean cross-polarization power ratio for different LOS states
# TR 138.901 v17.0.0 Table 7.5-6
__cross_polarization_power_mean: Mapping[LOSState, float] = {
LOSState.LOS: 11.0,
LOSState.NLOS: 10.0,
}
@staticmethod
def _cross_polarization_power_mean(state: LOSState) -> float:
return IndoorOfficeRealization.__cross_polarization_power_mean[state]
@staticmethod
def _cross_polarization_power_std(state: LOSState) -> float:
# TR 138.901 v17.0.0 Table 7.5-6
return 4.0
# Number of clusters for different LOS states
# TR 138.901 v17.0.0 Table 7.5-6
__num_clusters: Mapping[LOSState, int] = {LOSState.LOS: 15, LOSState.NLOS: 19}
@staticmethod
def _num_clusters(state: LOSState) -> int:
return IndoorOfficeRealization.__num_clusters[state]
@staticmethod
def _cluster_aod_spread(state: LOSState) -> float:
return 5.0
# RMS cluster azimuth of arrival spread for different LOS states in degrees
# TR 138.901 v17.0.0 Table 7.5-6
__cluster_aoa_spread: Mapping[LOSState, float] = {LOSState.LOS: 8.0, LOSState.NLOS: 11.0}
@staticmethod
def _cluster_aoa_spread(state: LOSState) -> float:
return IndoorOfficeRealization.__cluster_aoa_spread[state]
@staticmethod
def _cluster_zoa_spread(state: LOSState) -> float:
return 9.0
# Standard deviation of the shadowing for different LOS states in dB
# TR 138.901 v17.0.0 Table 7.5-6
__cluster_shadowing_std: Mapping[LOSState, float] = {LOSState.LOS: 6.0, LOSState.NLOS: 3.0}
@staticmethod
def _cluster_shadowing_std(state: LOSState) -> float:
return IndoorOfficeRealization.__cluster_shadowing_std[state]
@staticmethod
def _zod_spread_mean(state: LOSState, parameters: ClusterDelayLineSampleParameters) -> float:
# TR 138.901 v17.0.0 Table 7.5-10
# ToDo: Check if f_c is the carrier frequency in Hz or GHz
if state == LOSState.LOS:
fc = max(
6.0, parameters.carrier_frequency * 1e-9
) # See note 4 in TR 138.901 v17.0.0 Table 7.5-10
return -1.43 * log10(1 + fc) + 2.228
else:
return 1.08
@staticmethod
def _zod_spread_std(state: LOSState, parameters: ClusterDelayLineSampleParameters) -> float:
# TR 138.901 v17.0.0 Table 7.5-10
# ToDo: Check if f_c is the carrier frequency in Hz or GHz
if state == LOSState.LOS:
fc = max(
6.0, parameters.carrier_frequency * 1e-9
) # See note 4 in TR 138.901 v17.0.0 Table 7.5-10
return 0.13 * log10(1 + fc) + 0.30
else:
return 0.36
@staticmethod
def _zod_offset(state: LOSState, parameters: ClusterDelayLineSampleParameters) -> float:
# TR 138.901 v17.0.0 Table 7.5-10
return 0.0
[docs]
def to_HDF(self, group: Group) -> None:
ClusterDelayLineRealization.to_HDF(self, group)
self.__los_realization.to_HDF(group.create_group("los_realization"))
self.__nlos_realization.to_HDF(group.create_group("nlos_realization"))
group.attrs["office_type"] = self.__office_type.value
if self.expected_state is not None:
group.attrs["expected_state"] = self.expected_state.value
[docs]
@classmethod
def From_HDF(
cls: Type[IndoorOfficeRealization],
group: Group,
parameters: ClusterDelayLineRealizationParameters,
sample_hooks: Set[ChannelSampleHook[ClusterDelayLineSample]],
) -> IndoorOfficeRealization:
state_realization = ConsistentRealization.from_HDF(group["state_realization"])
los_realization = ConsistentRealization.from_HDF(group["los_realization"])
nlos_realization = ConsistentRealization.from_HDF(group["nlos_realization"])
gain = group.attrs["gain"] if "gain" in group.attrs else 1.0
if "expected_state" in group.attrs:
expected_state = LOSState(group.attrs["expected_state"])
else:
expected_state = None
office_type = OfficeType(group.attrs["office_type"])
return IndoorOfficeRealization(
expected_state,
state_realization,
los_realization,
nlos_realization,
parameters,
office_type,
sample_hooks,
gain,
)
[docs]
class IndoorOffice(ClusterDelayLineBase[IndoorOfficeRealization, LOSState], Serializable):
"""3GPP cluster delay line preset modeling an indoor office scenario."""
yaml_tag = "IndoorOffice"
"""YAML serialization tag."""
__office_type: OfficeType
def __init__(self, office_type: OfficeType = OfficeType.MIXED, **kwargs) -> None:
"""
Args:
office_type (OfficeType, optional):
Type of the modeled office.
If not specified, a mixed office is assumed.
\**kwargs:
Additional arguments passed to the base class.
"""
# Initialize base class
ClusterDelayLineBase.__init__(self, **kwargs)
# Initialize class attributes
self.__office_type = office_type
@property
def max_num_clusters(self) -> int:
return 19
@property
def max_num_rays(self) -> int:
return 20
@property
def office_type(self) -> OfficeType:
"""Type of the modeled office."""
return self.__office_type
@office_type.setter
def office_type(self, value: OfficeType) -> None:
self.__office_type = value
@property
def _large_scale_correlations(self) -> np.ndarray:
# Large scale cross correlations
# TR 138.901 v17.0.0 Table 7.5-6
return np.array(
[
# LOS NLOS
[+0.60, +0.40], # 0: ASD vs DS
[+0.80, +0.00], # 1: ASA vs DS
[-0.50, -0.40], # 2: ASA VS SF
[-0.40, +0.00], # 3: ASD vs SF
[-0.80, -0.50], # 4: DS vs SF
[+0.40, +0.00], # 5: ASD vs ASA
[+0.00, +0.00], # 6: ASD vs K
[+0.00, +0.00], # 7: ASA vs K
[-0.50, +0.00], # 8: DS vs K
[+0.50, +0.00], # 9: SF vs K
[+0.20, +0.00], # 10: ZSD vs SF
[+0.30, +0.00], # 11: ZSA vs SF
[+0.00, +0.00], # 12: ZSD vs K
[+0.10, +0.00], # 13: ZSA vs K
[+0.10, -0.27], # 14: ZSD vs DS
[+0.20, -0.06], # 15: ZSA vs DS
[+0.50, +0.35], # 16: ZSD vs ASD
[+0.00, +0.23], # 17: ZSA vs ASD
[+0.00, -0.08], # 18: ZSD vs ASA
[+0.50, +0.43], # 19: ZSA vs ASA
[+0.00, +0.42], # 20: ZSD vs ZSA
],
dtype=np.float_,
).T
def _initialize_realization(
self,
state_generator: ConsistentGenerator,
parameter_generator: ConsistentGenerator,
parameters: ClusterDelayLineRealizationParameters,
) -> IndoorOfficeRealization:
# Generate realizations for each large scale state
# TR 138.901 v17.0.0 Table 7.6.3.1-2
state_realization = state_generator.realize(10.0)
los_realization = parameter_generator.realize(10.0)
nlos_realization = parameter_generator.realize(10.0)
return IndoorOfficeRealization(
self.expected_state,
state_realization,
los_realization,
nlos_realization,
parameters,
self.__office_type,
self.sample_hooks,
self.gain,
)
def _recall_specific_realization(
self, group: Group, parameters: ClusterDelayLineRealizationParameters
) -> IndoorOfficeRealization:
return IndoorOfficeRealization.From_HDF(group, parameters, self.sample_hooks)