#
# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
"""Class for simulating Rayleigh block fading"""
import tensorflow as tf
from . import ChannelModel
[docs]class RayleighBlockFading(ChannelModel):
# pylint: disable=line-too-long
r"""RayleighBlockFading(num_rx, num_rx_ant, num_tx, num_tx_ant, dtype=tf.complex64)
Generate channel impulse responses corresponding to a Rayleigh block
fading channel model.
The channel impulse responses generated are formed of a single path with
zero delay and a normally distributed fading coefficient.
All time steps of a batch example share the same channel coefficient
(block fading).
This class can be used in conjunction with the classes that simulate the
channel response in time or frequency domain, i.e.,
:class:`~sionna.channel.OFDMChannel`,
:class:`~sionna.channel.TimeChannel`,
:class:`~sionna.channel.GenerateOFDMChannel`,
:class:`~sionna.channel.ApplyOFDMChannel`,
:class:`~sionna.channel.GenerateTimeChannel`,
:class:`~sionna.channel.ApplyTimeChannel`.
Parameters
----------
num_rx : int
Number of receivers (:math:`N_R`)
num_rx_ant : int
Number of antennas per receiver (:math:`N_{RA}`)
num_tx : int
Number of transmitters (:math:`N_T`)
num_tx_ant : int
Number of antennas per transmitter (:math:`N_{TA}`)
dtype : tf.DType
Complex datatype to use for internal processing and output.
Defaults to `tf.complex64`.
Input
-----
batch_size : int
Batch size
num_time_steps : int
Number of time steps
Output
-------
a : [batch size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths = 1, num_time_steps], tf.complex
Path coefficients
tau : [batch size, num_rx, num_tx, num_paths = 1], tf.float
Path delays [s]
"""
def __init__( self,
num_rx,
num_rx_ant,
num_tx,
num_tx_ant,
dtype=tf.complex64):
assert dtype.is_complex, "'dtype' must be complex type"
self._dtype = dtype
# We don't set these attributes as private so that the user can update
# them
self.num_tx = num_tx
self.num_tx_ant = num_tx_ant
self.num_rx = num_rx
self.num_rx_ant = num_rx_ant
def __call__(self, batch_size, num_time_steps, sampling_frequency=None):
# Delays
# Single path with zero delay
delays = tf.zeros([ batch_size,
self.num_rx,
self.num_tx,
1], # Single path
dtype=self._dtype.real_dtype)
# Fading coefficients
std = tf.cast(tf.sqrt(0.5), dtype=self._dtype.real_dtype)
h_real = tf.random.normal(shape=[ batch_size,
self.num_rx,
self.num_rx_ant,
self.num_tx,
self.num_tx_ant,
1, # One path
1], # Same response over the block
stddev=std,
dtype = self._dtype.real_dtype)
h_img = tf.random.normal(shape=[ batch_size,
self.num_rx,
self.num_rx_ant,
self.num_tx,
self.num_tx_ant,
1, # One cluster
1], # Same response over the block
stddev=std,
dtype = self._dtype.real_dtype)
h = tf.complex(h_real, h_img)
# Tile the response over the block
h = tf.tile(h, [1, 1, 1, 1, 1, 1, num_time_steps])
return h, delays