Sionna

Ray Tracing

- New notebook: [Tutorial on Coverage Maps](https://nvlabs.github.io/sionna/examples/Sionna_Ray_Tracing_Coverage_Map.html).
It computes coverage maps for different precoding vectors, visualizes metrics like path gain, received signal strength (RSS), and signal-to-interference-plus-noise ratio (SINR), estimates user-to-transmitter associations, and samples user positions for the generation of channel impulse responses
- [Scene](https://nvlabs.github.io/sionna/api/rt.html#sionna.rt.Scene) extended with [bandwidth](https://nvlabs.github.io/sionna/api/rt.html#sionna.rt.Scene.bandwidth) and [temperature](https://nvlabs.github.io/sionna/api/rt.html#sionna.rt.Scene.temperature) properties
- [Transmitter](https://nvlabs.github.io/sionna/api/rt.html?highlight=transmitter#sionna.rt.Transmitter) extended with a transmit power property in [Watt]( https://nvlabs.github.io/sionna/api/rt.html?highlight=transmitter#sionna.rt.Transmitter.power) and [dBm]( https://nvlabs.github.io/sionna/api/rt.html?highlight=transmitter#sionna.rt.Transmitter.power_dbm)
- [CoverageMap](
https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap) extended to compute [SINR maps](https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.sinr) and [RSS maps](https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.rss), in addition to the already existing [path gain maps](https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.path_gain)
- [CoverageMap]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap) extended to [compute]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.cell_to_tx) and [visualize]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.show_association) cell-to-transmitter association
- [CoverageMap]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap) extended to [compute and visualize the CDF]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.cdf) of path gain, RSS, or SINR
- [show()](https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.show) property of [CoverageMap](https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap) extended to visualize SINR and RSS maps in addition to the existing path gain
maps

- Adds feature to [Scene.coverage_map]( https://nvlabs.github.io/sionna/api/rt.html#sionna.rt.Scene.coverage_map) to execute multiple runs of shoot-and-bounce to compute coverage maps. This allows computing coverage maps using large number of rays despite memory limitation

- **Breaking Change**: `CoverageMap.as_tensor()` replaced by [path_gain]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.path_gain), [rss]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.rss), and [sinr]( https://nvlabs.github.io/sionna/api/rt.html?highlight=coveragemap#sionna.rt.CoverageMap.sinr) properties

Config

- Pseudo-random generators can be configured from the [config](https://nvlabs.github.io/sionna/api/config.html) module to ensure reproducible results, for [Python](https://nvlabs.github.io/sionna/api/config.html#sionna.Config.py_rng), [Numpy](https://nvlabs.github.io/sionna/api/config.html#sionna.Config.np_rng) and [TensorFlow](https://nvlabs.github.io/sionna/api/config.html#sionna.Config.tf_rng)

- New [config.seed property](https://nvlabs.github.io/sionna/api/config.html#sionna.Config.seed) to configure all Python, Numpy and TensorFlow pseudo-random generator seeds at once

OFDM

- Adds feature to prepend cyclic prefixes to OFDM symbols of different lengths (see [OFDMModulator](https://nvlabs.github.io/sionna/api/ofdm.html?highlight=ofdmmodulator#sionna.ofdm.OFDMModulator) and the related PR [465](https://github.com/NVlabs/sionna/pull/465))

Precoding

- Adds feature to produce Discrete Fourier Transform (DFT) grid of beams for [uniform linear](https://nvlabs.github.io/sionna/api/mimo.html#sionna.mimo.grid_of_beams_dft_ula) and [rectangular](https://nvlabs.github.io/sionna/api/mimo.html#sionna.mimo.grid_of_beams_dft) MIMO antenna arrays

Fixes

- Addresses the significant increase in memory footprint for path and coverage map computations introduced in version 0.18

- Fixes the skewness of the UTs position distribution toward the center of the sector generated via [drop_uts_in_sector](https://nvlabs.github.io/sionna/api/channel.wireless.html?highlight=drop_uts_in_sector#sionna.channel.drop_uts_in_sector), ensuring a uniform distribution across the sector

- Fixes ray leakage in coverage map computation (564)

- Fixes the application of precoding vector in coverage map computation in the presence of multiple transmitters

- Fixes object IDs issues in ray tracer: Scene objects now always have contiguous indices starting from 0

- Fixes missing scenes in Docker (597)

Ray tracing

- Adds support for reconfigurable intelligent surfaces (RIS). Exact paths as well as coverage maps can be computed.
- New [“Tutorial on Reconfigurable Intelligent Surfaces”](https://nvlabs.github.io/sionna/examples/Sionna_Ray_Tracing_RIS.html) notebook
- New section in the EM Primer on [Reconfigurable Intelligent Surfaces](https://nvlabs.github.io/sionna/em_primer.html#reconfigurable-intelligent-surfaces-ris)

Misc

- Fixes scaling of the antenna positions of planar arrays with the frequency (470, 400)
- Fixes support of non-CUDA GPUs in Sionna RT (464)
- Fixes an issue accessing the positions of SceneObjects (449)
- Fixes deprecated matplotlib function used in coverage maps (444)
- Fixes typos in the [“5G NR PUSCH Tutorial Notebook”]( https://nvlabs.github.io/sionna/examples/5G_NR_PUSCH.html)
- Fixes a bug in the detection of wedges on CPU (347, contribution by AinurZiga)
- Fixes crash of mobility notebook in Google Colab (422)
- Updates to the Makefile and Dockerfile (309)

Ray tracing

- Every scene object has a now a velocity vector which is used to compute per-path Doppler shifts. These are used by `Paths.apply_doppler()`to compute time evolution of channel impulse responses.
- Every scene object has now  a `position` and `orientation` property that can be modified after a scene is loaded.
- A new tutorial notebook “Mobility in Sionna RT” demonstrates various ways how the new features can be used to simulate mobility.

Misc

- Fixes typos in the 5G NR PUSCH Tutorial (384)
- Fixes typos in the Neural Receiver Tutorial (406, contribution by pablosreyero)
- Fixes typos in Tutorial Notebooks 1 and 2 (355, contribution by LateNightIceCream)
- Fixes a bug in the `Demapper` for very high SNR (327, contribution by japm48)
- Fixes an issue in `sample_positions()` for coverage maps (376)
- Removes warnings related to casting from `tf.float` to `tf.complex` (348)
- Fixes a bug in `theta_phi_from_unit_vec()` so that gradients are always well defined

Ray tracing

- New feature that allows defining a plane beyond which everything in the preview becomes invisible. This is useful, e.g., to look into buildings
- For the computation of coverage maps, not providing a combining vector now results in summing the energy received by all antennas
- Improved accuracy of the implementation of the Fibonacci lattice

Misc

- All evaluation statistics are now passed to the `callback` of `sim_ber` (PR286, contribution by nbecker)
- Fixes issues related to compatibility with versions 2.14 and 2.15 of TensorFlow

Ray tracing

- Fixes issue related to slow GPU computation in `compute_paths` (283)
- Fixes a formatting issue in the Diffraction notebook (279)

Ray tracing

- Splits path tracing and EM field computation through the addition of two methods to `Scene`: `trace_paths()` to trace the paths and `compute_fields()` to compute the corresponding EM fields
- `Scene.compute_fields()` can now be executed in graph mode
- Adds a feature to use a callable object to compute radio material properties from the intersection points between rays and the scene and the ids of the intersected objects
- Adds a feature to use a callable object to compute the scattering patterns
- Adds a feature to define custom colors for radio devices that are used for rendering and preview
- Enables the instantiation of radio materials without loading a scene (PR 216, contribution by fklement)
- Fixes a bug with diffraction that was creating `NaN`s in some scenarios (201)
- Fixes a bug that occurred when reversing link directions using `Paths.reverse_direction`
- ***(Breaking change)*** Delay normalization is now done across all receive and transmit antennas of each transmitter-receiver pair
- `RadioMaterial` properties are not defined anymore as `tf.Variable` but can now be set freely by the user to be either a `tf.Variable` or a `tf.Tensor`
- ***(Breaking change)*** Removes the flags `trainable_relative_permittivity`, `trainable_conductivity`, `trainable_scattering_coefficient`, and `trainable_xpd_coefficient` from `RadioMaterial`
- `Transmitter` and `Receiver` position and orientation are not defined anymore as `tf.Variable` but can be set freely by the user to be either a `tf.Variable` or a `tf.Tensor`
- ***(Breaking change)*** Removes the flags `trainable_position` and `trainable_orientation` from `Transmitter` and  `Receiver`
- Fixes a bug with paths that were wrongly flagged as LoS in some multi-link scenarios
- Adds a flag to `Scene.compute_paths()` and `Scene.compute_fields()` to disable the addition of random phases to scattered paths
- Fixes the link to the `simple_reflector` Blender file in API documentation
- Speeds-up `Scene.preview()` by switching to `uint8`-typed textures for coverage maps
- Better document the difference between edges and wedges (PR 214, contribution by jeertmans)
- Fixes 231
- Fixes 222
- Fixes 217

FEC

- Fixes a bug in `make_systematic()` (PR 236, contribution by daniel-x)
- Fixes XLA issue in CN update of `LDPCBPDecoder` (255)

MISC

- Adds multi-gpu support via `tf.distribute` to `sim_ber()`
- Adds `target_ber`, `target_bler` and `callback`  to `sim_ber()` (PR 257, contribution by nbecker)
- Update requirements: TensorFlow 2.10 – 2.13, Python 3.9 – 3.11 and Ubuntu 22.04 recommended