Release v0.19.1

Signed-off-by: The Sionna Team <[email protected]> Merged-by: Guillermo Marcus <[email protected]> Co-authored-by: Jakob Hoydis <[email protected]> Co-authored-by: Fayçal Ait-Aoudia <[email protected]> Co-authored-by: Sebastian Cammerer <[email protected]> Co-authored-by: Lorenzo Maggi <[email protected]> Co-authored-by: Guillermo Marcus <[email protected]> Co-authored-by: Merlin Nimier-David <[email protected]>
NVlabs · Nov 29, 2024 · 63519e7 · 63519e7
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diff --git a/README.md b/README.md
@@ -38,7 +38,7 @@ On macOS, you need to install [tensorflow-macos](https://github.com/apple/tensor
 ```
     >>> import sionna
     >>> print(sionna.__version__)
-    0.19.0
+    0.19.1
 ```
 
 3.) Once Sionna is installed, you can run the [Sionna "Hello, World!" example](https://nvlabs.github.io/sionna/examples/Hello_World.html), have a look at the [quick start guide](https://nvlabs.github.io/sionna/quickstart.html), or at the [tutorials](https://nvlabs.github.io/sionna/tutorials.html).
@@ -97,7 +97,7 @@ We recommend to do this within a [virtual environment](https://docs.python.org/3
 ```
     >>> import sionna
     >>> print(sionna.__version__)
-    0.19.0
+    0.19.1
 ```
 
 ## License and Citation

diff --git a/doc/source/installation.rst b/doc/source/installation.rst
@@ -39,7 +39,7 @@ e.g., using `conda <https://docs.conda.io>`_. On macOS, you need to install `ten
 
     >>> import sionna
     >>> print(sionna.__version__)
-    0.19.0
+    0.19.1
 
 3.) Once Sionna is installed, you can run the `Sionna "Hello, World!" example <https://nvlabs.github.io/sionna/examples/Hello_World.html>`_, have a look at the `quick start guide <https://nvlabs.github.io/sionna/quickstart.html>`_, or at the `tutorials <https://nvlabs.github.io/sionna/tutorials.html>`_.
 
@@ -111,4 +111,4 @@ e.g., using `conda <https://docs.conda.io>`_.
 
     >>> import sionna
     >>> print(sionna.__version__)
-    0.19.0
+    0.19.1
diff --git a/doc/source/made_with_sionna.rst b/doc/source/made_with_sionna.rst
@@ -6,6 +6,15 @@ We love to see how Sionna is used by other researchers! For this reason, you fin
 
 If you want your paper/project and code be listed here, please send an email to `[email protected] <mailto:[email protected]>`_ with links to the paper (e.g., `arXiv <https://arxiv.org>`_) and code repository (e.g., `GitHub <https://github.com>`_).
 
+.. made-with-sionna::
+    :title: Advancing Spectrum Anomaly Detection through Digital Twins
+    :authors: Anton Schösser, Friedrich Burmeister, Philipp Schulz, Mohd Danish Khursheed, Sinuo Ma, Gerhard Fettweis
+    :year: 2024
+    :version: 0.15.1
+    :link_arxiv: https://www.techrxiv.org/users/775914/articles/883996-advancing-spectrum-anomaly-detection-through-digital-twins
+    :link_github: https://github.com/akdd11/advancing-spectrum-anomaly-detection
+    :abstract: 6th generation (6G) cellular networks are expected to enable various safety-critical use cases, e.g., in the industrial domain, which require flawless operation of the network. Thus, resilience is one of the key requirements for 6G. A particularly critical point is that 6G, as any other wireless technology, is based on the open radio medium, making it susceptible to interference. Especially intentional interference, i.e., jamming, can severely degrade the network operability. Therefore, a new approach for detecting anomalies in the radio spectrum using a digital twin (DT) of the radio environment is presented in this work. This allows the integration of contextual awareness in the anomaly detection process and is thereby superior to state-of-the-art methods  for spectrum anomaly detection. We propose a suitable system architecture and discuss the tasks of machine learning (ML) therein, particularly for reducing the computational complexity and to detect anomalies in an unsupervised manner. The feasibility of the approach is demonstrated by ray tracing simulations. The results indicate a strong detection capability in case of an accurate DT and thereby illustrate the potential of DTs to enhance monitoring of  wireless networks in the future.
+
 .. made-with-sionna::
     :title: Physically Consistent RIS: From Reradiation Mode Optimization to Practical Realization
     :authors: Javad Shabanpour, Constantin Simovski, Giovanni Geraci

diff --git a/examples/Autoencoder.ipynb b/examples/Autoencoder.ipynb
@@ -85,21 +85,6 @@
     "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
     "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
     "\n",
-    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
-    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
-    "import tensorflow as tf\n",
-    "gpus = tf.config.list_physical_devices('GPU')\n",
-    "if gpus:\n",
-    "    try:\n",
-    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
-    "    except RuntimeError as e:\n",
-    "        print(e)\n",
-    "# Avoid warnings from TensorFlow\n",
-    "tf.get_logger().setLevel('ERROR')\n",
-    "\n",
-    "from tensorflow.keras import Model\n",
-    "from tensorflow.keras.layers import Layer, Dense\n",
-    "\n",
     "# Import Sionna\n",
     "try:\n",
     "    import sionna\n",

diff --git a/examples/Weighted_BP_Algorithm.ipynb b/examples/Weighted_BP_Algorithm.ipynb
@@ -77,18 +77,6 @@
     "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
     "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
     "\n",
-    "import tensorflow as tf\n",
-    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
-    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
-    "gpus = tf.config.list_physical_devices('GPU')\n",
-    "if gpus:\n",
-    "    try:\n",
-    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
-    "    except RuntimeError as e:\n",
-    "        print(e)\n",
-    "# Avoid warnings from TensorFlow\n",
-    "tf.get_logger().setLevel('ERROR')\n",
-    "\n",
     "# Import Sionna\n",
     "try:\n",
     "    import sionna\n",

diff --git a/requirements.txt b/requirements.txt
@@ -3,7 +3,7 @@ numpy
 scipy >=1.6.0
 matplotlib >= 3.5.3
 importlib_resources
-mitsuba >= 3.2.0
+mitsuba >= 3.2.0, <3.6.0
 pythreejs >= 2.4.2
 ipywidgets >= 8.0.4
 ipydatawidgets == 4.3.2

diff --git a/setup.cfg b/setup.cfg
@@ -27,10 +27,10 @@ python_requires = >=3.8
 install_requires =
     tensorflow >=2.13.0, <2.16.0
     numpy
-    matplotlib
+    matplotlib >= 3.5.3
     scipy >=1.6.0
     importlib_resources
-    mitsuba >= 3.2.0
+    mitsuba >= 3.2.0, <3.6.0
     pythreejs >= 2.4.2
     ipywidgets >= 8.0.4
     ipydatawidgets == 4.3.2

diff --git a/sionna/__init__.py b/sionna/__init__.py
@@ -5,7 +5,7 @@
 """This is the Sionna library.
 """
 
-__version__ = '0.19.0'
+__version__ = '0.19.1'
 
 from .config import config
 from .constants import *

diff --git a/sionna/ofdm/pilot_pattern.py b/sionna/ofdm/pilot_pattern.py
@@ -335,7 +335,7 @@ def __init__(self,
 
         # Compute the length of a pilot sequence
         num_pilots = num_pilot_symbols*num_effective_subcarriers/num_seq
-        assert num_pilots%1==0, \
+        assert (num_pilots/num_pilot_symbols)%1==0, \
             """`num_effective_subcarriers` must be an integer multiple of
             `num_tx`*`num_streams_per_tx`."""
 

diff --git a/sionna/rt/coverage_map.py b/sionna/rt/coverage_map.py
@@ -506,19 +506,10 @@ def show(self,
             If set to `True`, then the position of the RIS are shown.
             Defaults to `False`.
 
-        show_association : bool
-            If set to `True`, then the cell-to-transmitter association
-            is shown ina second figure (based on the selected metric).
-            Defaults to `False`.
-
         Output
         ------
         : :class:`~matplotlib.pyplot.Figure`
             Figure showing the coverage map
-
-        : :class:`~matplotlib.pyplot.Figure`
-            Figure showing the cell-to-transmitter association
-            Only returned if ``show_association`` is `True`.
         """
 
         if metric not in ["path_gain", "rss", "sinr"]:

diff --git a/sionna/rt/scene.py b/sionna/rt/scene.py
@@ -583,10 +583,6 @@ def trace_paths(self, max_depth=3, method="fibonacci", num_samples=int(1e6),
             If set to `True`, then the paths involving RIS are computed.
             Defaults to `True`.
 
-        ris : bool
-            If set to `True`, then the paths involving RIS are computed.
-            Defaults to `True`.
-
         scat_keep_prob : float
             Probability with which to keep scattered paths.
             This is helpful to reduce the number of scattered paths computed,
@@ -618,9 +614,6 @@ def trace_paths(self, max_depth=3, method="fibonacci", num_samples=int(1e6),
         ris_paths : :class:`~sionna.rt.Paths`
             Computed paths involving RIS
 
-        ris_paths : :class:`~sionna.rt.Paths`
-            Computed paths involving RIS
-
         spec_paths_tmp : :class:`~sionna.rt.PathsTmpData`
             Additional data required to compute the EM fields of the specular
             paths
@@ -637,9 +630,6 @@ def trace_paths(self, max_depth=3, method="fibonacci", num_samples=int(1e6),
             Additional data required to compute the EM fields of the paths
             involving RIS
 
-        ris_paths_tmp : :class:`~sionna.rt.PathsTmpData`
-            Additional data required to compute the EM fields of the paths
-            involving RIS
         """
 
         if scat_keep_prob < 0. or scat_keep_prob > 1.: