GSoCIdeas

Note- also check out Grant Ideas for additional ideas that are more suited towards grant money than GSoC.

Summer of Code 2024: Project ideas list

This is the list of project ideas for the summer of code 2024 within GNU Radio.
Remember that these are ideas and are merely meant as an inspiration for you to write your own proposal.

Students who do not find a fit among these projects are encouraged to engage with us and suggest new ones. The GNU Radio discussion mailing list is the best place to contact all of us. Please do not contact us off-list for the sake of discussing the summer of code, unless you're contacting a mentor listed here to get feedback on a proposal.

Reviewing the Google GSoC FAQ page for a broader understanding of project, mentor, and student responsibilities is recommended.

If you need a USRP or other radio hardware to complete the project, we will be able to arrange something.

Please add ideas to this list (you may cannibalize old ideas, of course!).

Guidelines for good projects (when suggesting projects, please consider these):

• Clearly defined scope, with a main target that can be done in 3 months
• Clear benefits for the GNU Radio project
• Not specific to a certain hardware. No specific embedded devices, either, please.
• Both OOTs and in-tree improvements are welcome

Graphical interoperability between CyberEther and GNU Radio

The CyberEther project comes with some neat graphical sinks that would be great to have access to in GNU Radio. This project entails creating a new CyberEther GUI workflow much like the gr-bokehgui project, such that users can create flowgraphs with CyberEther sinks. This would allow the user to visualize GNU Radio data streams in one of the high-performance CyberEther plots (lineplot, waterfall, spectrogram, etc).

Prerequisites

• Knowledge of C++ and some Python
• Familiarity with graphical APIs (OpenGL, Vulkan, Metal)
• Basic Qt understanding

Outcome

• OOT module with CyberEther sinks
• Support for both GNU Radio main branch and 3.10?

Project length

Long (350 hours)

Difficulty

Medium

Mentor(s)

Luigi Cruz, Håkon Vågsether

GPU Accelerated Signal Processing Blocks

GPUs offer incredible capability for accelerating a number of signal processing routines when the calculations can be done in parallel. Also, GNU Radio 3.10 brought in a "custom buffers" feature which provides support generally for accelerator devices by allowing blocks to have direct access to device memory, finally making accelerator processing feasible through a flowgraph (see FOSDEM 2022 Presentation.

One piece that is missing for GNU Radio is a library of blocks that accelerate common DSP routines. There are several interesting libraries of GPU accelerated signal processing - primarily using CUDA because of its accessible programming paradigm and the ubiquity of NVIDIA hardware:

• Matx
• cuSignal (Python signal processing)
• CUSP

Integration of any of this functionality, along with additional kernels for signal processing would need to be predicated on using gr-cuda custom buffers, and expanding this module as needed

This project can be broken into several subprojects:

• Create gr-matx OOT
  • Add Matx Custom Buffer Type (after gr-cuda)
  • Create blocks wrapping Matx operations
• Expand gr-cuda
  • Additional custom buffer types - pinned, unified
  • Create python custom buffers allowing zero copy into python blocks
• Create gr-cuSignal
  • Wrap cuSignal functionality (dependent on python zero copy)
• Replicate existing GR blocks as CUDA accelerated (things not in cuSignal or Matx)
  • Target for extensions to Matx, cuSignal, or CUSP (within our control)
  • FIR Filters
  • Polyphase Resampler
  • Signal Source
  • Moving Average
  • Polyphase Clock Sync
  • Stream Operators
  • ...

Prerequisites

• Knowledge of C++ and Python.
• Familiarity with CUDA programming

Outcome

Depends on chosen subprojects (see above).

Project length

350 hours

Difficulty

Medium

Mentor(s)

Josh Morman

GRC and GR 4.0

Development of GR 4.0 is progressing quickly. In the current runtime prototype a plugin architecture is used to properly register blocks with the runtime. This allows a more dynamic construction of flowgraphs and introspection into the blocks. But this means the current way of assembling a flowgraph by generating a Python or C++ file needs updates.

The idea is to port and change necessary parts of GRC (Qt development version) to use the block registry in the new GNU Radio runtime https://github.com/fair-acc/graph-prototype/ and assemble some of the example flowgraphs defined in GRC files and make them run. The design for this is not finalized and therefore you will have freedom to propose your ideas.

Prerequisites

• Good Knowledge of C++ and Python
• Experience with inter-language bindings (not necessarily C++ & Python) is useful
• Basic Qt understanding

Outcome

• Prototype integration of GRC with the new plugin architecture of GR 4.0

Project length

Long (350 hours)

Difficulty

Challenging

Mentor(s)

Andrej Rode, Josh Morman

GRC: Standalone application and pluggable workflows

GNU Radio Companion (GRC) has become useful outside of just GNU Radio, and several projects have forked and maintained their own versions. Even within GRC, there are different workflows (QT GUI, C++, Bokeh-gui) with different options in the path to render a working flowgraph see GREP 0025. In its most basic form, GRC does the following:

• User sets high level options (type of flowgraph)
• User draws flowgraph graphically with blocks and connections
• Flowgraph uses templates (Mako) to render to a python script

The goal of this project is to pull GRC out of the GNU Radio codebase and make the workflow modular. There should be a high level selection of the workflow that defines the options block. In our current usage these workflows could be:

• Python QT GUI
• C++ QT GUI
• Python No GUI
• C++ No GUI
• Bokeh GUI

The workflow should map to a set of templates that are used to render the output script. The definition of the workflow options and the associated templates should be defined in some pluggable manner (files dropped into a directory that GRC sees at runtime), so that "out of tree" workflows can be added easily - because we don't know all the use cases of GRC.

Steps

• Move GRC as a separate repository (while maintaining git history)
• Remove dependence of GRC on gnuradio
• Modularized options block
• Modularized templates
• Allow templating with jinja as well
• If time allows:
  • Modularize gr-modtool templates as well per GREP 0026
  • Support multiple domains' workflows.

Prerequisites

• Knowledge of Python.

Outcome

• GRC as a GNU Radio-independent application
• Support for additional workflows in GRC
• Depends on chosen subprojects (see above).

Project length

350 hours

Difficulty

Medium

Mentor(s)

Josh Morman, Håkon Vågsether, Sebastian Koslowski, ?? Someone else that is a GRC Wizard

GRC: Build-in sub flowgraphs

GNU Radio has hierarchical blocks as a way to build reuseable sub flowgraphs. These hier_blocks can be designed in GRC, however, they have to be compiled to code and GRC bindings, before they can be used in other GRC files. While this is great for reuseablity across flowgraphs, it is quite cumbersome when the main use is to structure a single (larger) flowgraph. The goal of this project is to ease this use-case by embedding sub flowgraphs directly in the main GRC file. Instead of creating bindings and code and then parsing them back again, this process shall be done in-place to allow quickly editing sub flowgraphs on-the-fly.

Prerequisites

• GRC is written in Python which is (almost) all you need to know for this project.

Outcome

• A vastly improved workflow for structuring flowgraphs

Project length

175 hours

Difficulty

Easy

Mentor(s)

Håkon Vågsether

Revitalize in-tree and out-of-tree (OOT) modules

A lot has changed since version 3.7, and GNU Radio has made great technical strides the last few years. However, some OOT modules haven't been updated to support the latest versions of GNU Radio, and these modules currently require the user to install an older version of the framework. This is unfortunate, and lowers the useability of GNU Radio as a whole. Some of these modules have been superseded by others, but might still have some blocks or flowgraphs that are useful, and these could be updated and moved in-tree. Some in-tree modules are also in need of attention, like gr-wavelet, which does not have any examples.

Prerequisites

• Knowledge of C++, Python and DSP.

Outcome

• More example code, tests and flowgraphs for various in-tree modules
• Porting various OOT modules to support recent versions of GNU Radio
• Possibly blocks/flowgraphs from old OOT modules moved in-tree

Project length

Small (90 hours) - Medium (175 hours)

Difficulty

Easy - Medium

Mentor(s)

Håkon Vågsether, ?

Forward Error Correction in GNU Radio

Over the years many different forward error correction (FEC) methods ( e.g. Polar Encoder/Decoder, LDPC Encoder/Decoder) have been added to GNU Radio. In other open-source projects (e.g. Aff3ct, more modern methods and possibly more performant methods have been implemented.

The goal of this project is to update and possibly overhaul the FEC implementations within GNU Radio. Since there are quite some methods in the wild, we need to coordinate on which methods and other libraries should be included in the comparison. Same goes for the already available methods for error coding in GNU Radio.

Prerequisites

• Knowledge of C++, Python and DSP.
• Interest in information theory and error coding
• No fear of reading & comparing other implementations

Outcome

• Updated & polished FEC experience in GNU Radio
• Addition of more performant and updated methods for error coding
• Deletion of possibly redundant and inperformant methods

Project length

Medium (175 hours) - Long (350 hours)

Difficulty

Medium - Hard

Mentor(s)

Andrej Rode, ?

CI for maintenance branches and select OOT modules

It would be useful to have nightly builds for GNU Radio's maintenance branches (3.8, 3.9, 3.10) and some select OOTs.

Prerequisites

• Experience with Docker?
• ?

Outcome

• Automated PPAs, Snaps, Flatpak apps

Project length

175 hours

Difficulty

Easy

Mentor(s)

Håkon Vågsether, ?

Old Ideas

Feel free to browse old ideas from previous years for inspiration.