#
# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
"""Urban macrocell (UMa) channel model from 3GPP TR38.901 specification"""
import tensorflow as tf
from . import SystemLevelChannel
from . import UMaScenario
[docs]class UMa(SystemLevelChannel):
# pylint: disable=line-too-long
r"""UMa(carrier_frequency, o2i_model, ut_array, bs_array, direction, enable_pathloss=True, enable_shadow_fading=True, always_generate_lsp=False, dtype=tf.complex64)
Urban macrocell (UMa) channel model from 3GPP [TR38901]_ specification.
Setting up a UMa model requires configuring the network topology, i.e., the
UTs and BSs locations, UTs velocities, etc. This is achieved using the
:meth:`~sionna.channel.tr38901.UMa.set_topology` method. Setting a different
topology for each batch example is possible. The batch size used when setting up the network topology
is used for the link simulations.
The following code snippet shows how to setup an UMa channel model assuming
an OFDM waveform:
>>> # UT and BS panel arrays
>>> bs_array = PanelArray(num_rows_per_panel = 4,
... num_cols_per_panel = 4,
... polarization = 'dual',
... polarization_type = 'cross',
... antenna_pattern = '38.901',
... carrier_frequency = 3.5e9)
>>> ut_array = PanelArray(num_rows_per_panel = 1,
... num_cols_per_panel = 1,
... polarization = 'single',
... polarization_type = 'V',
... antenna_pattern = 'omni',
... carrier_frequency = 3.5e9)
>>> # Instantiating UMa channel model
>>> channel_model = UMa(carrier_frequency = 3.5e9,
... o2i_model = 'low',
... ut_array = ut_array,
... bs_array = bs_array,
... direction = 'uplink')
>>> # Setting up network topology
>>> # ut_loc: UTs locations
>>> # bs_loc: BSs locations
>>> # ut_orientations: UTs array orientations
>>> # bs_orientations: BSs array orientations
>>> # in_state: Indoor/outdoor states of UTs
>>> channel_model.set_topology(ut_loc,
... bs_loc,
... ut_orientations,
... bs_orientations,
... ut_velocities,
... in_state)
>>> # Instanting the OFDM channel
>>> channel = OFDMChannel(channel_model = channel_model,
... resource_grid = rg)
where ``rg`` is an instance of :class:`~sionna.ofdm.ResourceGrid`.
Parameters
-----------
carrier_frequency : float
Carrier frequency in Hertz
o2i_model : str
Outdoor-to-indoor loss model for UTs located indoor.
Set this parameter to "low" to use the low-loss model, or to "high"
to use the high-loss model.
See section 7.4.3 of [TR38901]_ for details.
rx_array : PanelArray
Panel array used by the receivers. All receivers share the same
antenna array configuration.
tx_array : PanelArray
Panel array used by the transmitters. All transmitters share the
same antenna array configuration.
direction : str
Link direction. Either "uplink" or "downlink".
enable_pathloss : bool
If `True`, apply pathloss. Otherwise doesn't. Defaults to `True`.
enable_shadow_fading : bool
If `True`, apply shadow fading. Otherwise doesn't.
Defaults to `True`.
always_generate_lsp : bool
If `True`, new large scale parameters (LSPs) are generated for every
new generation of channel impulse responses. Otherwise, always reuse
the same LSPs, except if the topology is changed. Defaults to
`False`.
dtype : Complex tf.DType
Defines the datatype for internal calculations and the output
dtype. Defaults to `tf.complex64`.
Input
-----
num_time_steps : int
Number of time steps
sampling_frequency : float
Sampling frequency [Hz]
Output
-------
a : [batch size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths, num_time_steps], tf.complex
Path coefficients
tau : [batch size, num_rx, num_tx, num_paths], tf.float
Path delays [s]
"""
def __init__(self, carrier_frequency, o2i_model, ut_array, bs_array,
direction, enable_pathloss=True, enable_shadow_fading=True,
always_generate_lsp=False, dtype=tf.complex64):
# RMa scenario
scenario = UMaScenario(carrier_frequency, o2i_model, ut_array, bs_array,
direction, enable_pathloss, enable_shadow_fading,
dtype)
super().__init__(scenario, always_generate_lsp)