#
# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0#
"""Class for generating channel frequency responses"""
from sionna.phy.block import Object
from sionna.phy.channel.utils import subcarrier_frequencies, cir_to_ofdm_channel
[docs]
class GenerateOFDMChannel(Object):
# pylint: disable=line-too-long
r"""
Generates channel frequency responses
The channel impulse response is constant over the duration of an OFDM symbol.
Given a channel impulse response
:math:`(a_{m}(t), \tau_{m}), 0 \leq m \leq M-1`, generated by the ``channel_model``,
the channel frequency response for the :math:`s^{th}` OFDM symbol and
:math:`n^{th}` subcarrier is computed as follows:
.. math::
\widehat{h}_{s, n} = \sum_{m=0}^{M-1} a_{m}(s) e^{-j2\pi n \Delta_f \tau_{m}}
where :math:`\Delta_f` is the subcarrier spacing, and :math:`s` is used as time
step to indicate that the channel impulse response can change from one OFDM symbol to the
next in the event of mobility, even if it is assumed static over the duration
of an OFDM symbol.
Parameters
----------
channel_model : :class:`~sionna.phy.channel.ChannelModel`
Channel model to be used.
resource_grid : :class:`~sionna.phy.ofdm.ResourceGrid`
Resource grid
normalize_channel : `bool`, (default `False`)
If set to `True`, the channel is normalized over the resource grid
to ensure unit average energy per resource element.
precision : `None` (default) | "single" | "double"
Precision used for internal calculations and outputs.
If set to `None`,
:attr:`~sionna.phy.config.Config.precision` is used.
Input
-----
batch_size : `None` (default) | `int`
Batch size. Defaults to `None` for channel models that do not require this parameter.
Output
-------
h_freq : [batch size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_ofdm_symbols, num_subcarriers], `tf.complex`
Channel frequency responses
"""
def __init__(self, channel_model, resource_grid, normalize_channel=False,
precision=None, **kwargs):
super().__init__(precision=precision, **kwargs)
# Callable used to sample channel input responses
self._cir_sampler = channel_model
# We need those in call()
self._num_ofdm_symbols = resource_grid.num_ofdm_symbols
self._subcarrier_spacing = resource_grid.subcarrier_spacing
self._num_subcarriers = resource_grid.fft_size
self._normalize_channel = normalize_channel
self._sampling_frequency = 1./resource_grid.ofdm_symbol_duration
# Frequencies of the subcarriers
self._frequencies = subcarrier_frequencies(self._num_subcarriers,
self._subcarrier_spacing,
self.precision)
def __call__(self, batch_size=None):
# Sample channel impulse responses
h, tau = self._cir_sampler( batch_size,
self._num_ofdm_symbols,
self._sampling_frequency)
h_freq = cir_to_ofdm_channel(self._frequencies, h, tau,
self._normalize_channel)
return h_freq