#
# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
"""Layer for implementing the channel in the time domain"""
import tensorflow as tf
from . import GenerateTimeChannel, ApplyTimeChannel
from .utils import time_lag_discrete_time_channel
[docs]class TimeChannel(tf.keras.layers.Layer):
# pylint: disable=line-too-long
r"""TimeChannel(channel_model, bandwidth, num_time_samples, maximum_delay_spread=3e-6, l_min=None, l_max=None, normalize_channel=False, add_awgn=True, return_channel=False, dtype=tf.complex64, **kwargs)
Generate channel responses and apply them to channel inputs in the time domain.
This class inherits from the Keras `Layer` class and can be used as layer
in a Keras model.
The channel output consists of ``num_time_samples`` + ``l_max`` - ``l_min``
time samples, as it is the result of filtering the channel input of length
``num_time_samples`` with the time-variant channel filter of length
``l_max`` - ``l_min`` + 1. In the case of a single-input single-output link and given a sequence of channel
inputs :math:`x_0,\cdots,x_{N_B}`, where :math:`N_B` is ``num_time_samples``, this
layer outputs
.. math::
y_b = \sum_{\ell = L_{\text{min}}}^{L_{\text{max}}} x_{b-\ell} \bar{h}_{b,\ell} + w_b
where :math:`L_{\text{min}}` corresponds ``l_min``, :math:`L_{\text{max}}` to ``l_max``, :math:`w_b` to
the additive noise, and :math:`\bar{h}_{b,\ell}` to the
:math:`\ell^{th}` tap of the :math:`b^{th}` channel sample.
This layer outputs :math:`y_b` for :math:`b` ranging from :math:`L_{\text{min}}` to
:math:`N_B + L_{\text{max}} - 1`, and :math:`x_{b}` is set to 0 for :math:`b < 0` or :math:`b \geq N_B`.
The channel taps :math:`\bar{h}_{b,\ell}` 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``.
For multiple-input multiple-output (MIMO) links, the channel output is computed for each antenna of each receiver and by summing over all the antennas of all transmitters.
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`)
maximum_delay_spread : float
Maximum delay spread [s].
Used to compute the default value of ``l_max`` if ``l_max`` is set to
`None`. If a value is given for ``l_max``, this parameter is not used.
It defaults to 3us, which was found
to be large enough to include most significant paths with all channel
models included in Sionna assuming a nominal delay spread of 100ns.
l_min : int
Smallest time-lag for the discrete complex baseband channel (:math:`L_{\text{min}}`).
If set to `None`, defaults to the value given by :func:`time_lag_discrete_time_channel`.
l_max : int
Largest time-lag for the discrete complex baseband channel (:math:`L_{\text{max}}`).
If set to `None`, it is computed from ``bandwidth`` and ``maximum_delay_spread``
using :func:`time_lag_discrete_time_channel`. If it is not set to `None`,
then the parameter ``maximum_delay_spread`` is not used.
add_awgn : bool
If set to `False`, no white Gaussian noise is added.
Defaults to `True`.
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`.
return_channel : bool
If set to `True`, the channel response is returned in addition to the
channel output. Defaults to `False`.
dtype : tf.DType
Complex datatype to use for internal processing and output.
Defaults to `tf.complex64`.
Input
-----
(x, no) or x:
Tuple or Tensor:
x : [batch size, num_tx, num_tx_ant, num_time_samples], tf.complex
Channel inputs
no : Scalar or Tensor, tf.float
Scalar or tensor whose shape can be broadcast to the shape of the
channel outputs: [batch size, num_rx, num_rx_ant, num_time_samples].
Only required if ``add_awgn`` is set to `True`.
The noise power ``no`` is per complex dimension. If ``no`` is a scalar,
noise of the same variance will be added to the outputs.
If ``no`` is a tensor, it must have a shape that can be broadcast to
the shape of the channel outputs. This allows, e.g., adding noise of
different variance to each example in a batch. If ``no`` has a lower
rank than the channel outputs, then ``no`` will be broadcast to the
shape of the channel outputs by adding dummy dimensions after the last
axis.
Output
-------
y : [batch size, num_rx, num_rx_ant, num_time_samples + l_max - l_min], tf.complex
Channel outputs
The channel output consists of ``num_time_samples`` + ``l_max`` - ``l_min``
time samples, as it is the result of filtering the channel input of length
``num_time_samples`` with the time-variant channel filter of length
``l_max`` - ``l_min`` + 1.
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
(Optional) Channel responses. Returned only if ``return_channel``
is set to `True`.
For each batch example, ``num_time_samples`` + ``l_max`` - ``l_min`` time
steps of the channel realizations are generated to filter the channel input.
"""
def __init__(self, channel_model, bandwidth, num_time_samples,
maximum_delay_spread=3e-6, l_min=None, l_max=None,
normalize_channel=False, add_awgn=True, return_channel=False,
dtype=tf.complex64, **kwargs):
super().__init__(trainable=False, dtype=dtype, **kwargs)
# Setting l_min and l_max to default values if not given by the user
l_min_default, l_max_default = time_lag_discrete_time_channel(bandwidth,
maximum_delay_spread)
if l_min is None:
l_min = l_min_default
if l_max is None:
l_max = l_max_default
self._cir_sampler = channel_model
self._bandwidth = bandwidth
self._num_time_steps = num_time_samples
self._l_min = l_min
self._l_max = l_max
self._l_tot = l_max-l_min+1
self._normalize_channel = normalize_channel
self._add_awgn = add_awgn
self._return_channel = return_channel
def build(self, input_shape): #pylint: disable=unused-argument
self._generate_channel = GenerateTimeChannel(self._cir_sampler,
self._bandwidth,
self._num_time_steps,
self._l_min,
self._l_max,
self._normalize_channel)
self._apply_channel = ApplyTimeChannel( self._num_time_steps,
self._l_tot,
self._add_awgn,
tf.as_dtype(self.dtype))
def call(self, inputs):
if self._add_awgn:
x, no = inputs
else:
x = inputs
h_time = self._generate_channel(tf.shape(x)[0])
if self._add_awgn:
y = self._apply_channel([x, h_time, no])
else:
y = self._apply_channel([x, h_time])
if self._return_channel:
return y, h_time
else:
return y