#
# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
"""Class for generating channel responses in the time domain"""
from sionna.channel.utils import cir_to_time_channel
[docs]class GenerateTimeChannel:
# pylint: disable=line-too-long
r"""GenerateTimeChannel(channel_model, bandwidth, num_time_samples, l_min, l_max, normalize_channel=False)
Generate channel responses in the time domain.
For each batch example, ``num_time_samples`` + ``l_max`` - ``l_min`` time steps of a
channel realization are generated by this layer.
These can be used to filter a channel input of length ``num_time_samples`` using the
:class:`~sionna.channel.ApplyTimeChannel` layer.
The channel taps :math:`\bar{h}_{b,\ell}` (``h_time``) returned by this layer
are computed assuming a sinc filter is used for pulse shaping and receive filtering.
Therefore, given a channel impulse response
:math:`(a_{m}(t), \tau_{m}), 0 \leq m \leq M-1`, generated by the ``channel_model``,
the channel taps are computed as follows:
.. math::
\bar{h}_{b, \ell}
= \sum_{m=0}^{M-1} a_{m}\left(\frac{b}{W}\right)
\text{sinc}\left( \ell - W\tau_{m} \right)
for :math:`\ell` ranging from ``l_min`` to ``l_max``, and where :math:`W` is
the ``bandwidth``.
Parameters
----------
channel_model : :class:`~sionna.channel.ChannelModel` object
An instance of a :class:`~sionna.channel.ChannelModel`, such as
:class:`~sionna.channel.RayleighBlockFading` or
:class:`~sionna.channel.tr38901.UMi`.
bandwidth : float
Bandwidth (:math:`W`) [Hz]
num_time_samples : int
Number of time samples forming the channel input (:math:`N_B`)
l_min : int
Smallest time-lag for the discrete complex baseband channel (:math:`L_{\text{min}}`)
l_max : int
Largest time-lag for the discrete complex baseband channel (:math:`L_{\text{max}}`)
normalize_channel : bool
If set to `True`, the channel is normalized over the block size
to ensure unit average energy per time step. Defaults to `False`.
Input
-----
batch_size : int
Batch size. Defaults to `None` for channel models that do not require this paranmeter.
Output
-------
h_time : [batch size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_time_samples + l_max - l_min, l_max - l_min + 1], tf.complex
Channel responses.
For each batch example, ``num_time_samples`` + ``l_max`` - ``l_min`` time steps of a
channel realization are generated by this layer.
These can be used to filter a channel input of length ``num_time_samples`` using the
:class:`~sionna.channel.ApplyTimeChannel` layer.
"""
def __init__(self, channel_model, bandwidth, num_time_samples, l_min, l_max,
normalize_channel=False):
# Callable used to sample channel input responses
self._cir_sampler = channel_model
self._l_min = l_min
self._l_max = l_max
self._l_tot = l_max - l_min + 1
self._bandwidth = bandwidth
self._num_time_steps = num_time_samples
self._normalize_channel = normalize_channel
def __call__(self, batch_size=None):
# Sample channel impulse responses
# pylint: disable=unbalanced-tuple-unpacking
h, tau = self._cir_sampler( batch_size,
self._num_time_steps + self._l_tot - 1,
self._bandwidth)
hm = cir_to_time_channel(self._bandwidth, h, tau, self._l_min,
self._l_max, self._normalize_channel)
return hm