- An annotated 5G SigMF recording
For quite some time I’ve been thinking about generating SigMF annotations in some of the Jupyter notebooks I have about signal analysis, such as those for LTE and 5G NR. The idea is that the information about the frequencies and timestamps of the packets, as well as their type and other metadata, is already obtained in the notebook, so it is not too difficult to generate SigMF annotations with this information. The main intention is educational: the annotated SigMF file provides a visual guide that helps to understand the signal structure, and it also serves as a summary of what kind of signal detection and analysis is done in the Jupyter notebook. The code also serves as an example of how to generate annotations.
Another benefit of this idea is that it serves as a good test case for applications that display SigMF annotations. It shows what kinds of limitations the current tools have, and can also motivate new features. I’ve been toying with this idea since a while ago, but never wrote a blog post about it before. A year ago I sent a pull request to Inspectrum to be able to display annotation comments as tooltips when the mouse hovers above the annotation. While doing some tests with one LTE recording I realized that a feature like this was necessary to display any kind of detailed information about a packet. Back then, Inspectrum was the only application that was reasonably good at displaying SigMF annotations in a waterfall. Later, IQEngine has appeared as another good tool to display SigMF annotations (and also add them manually).
I have now updated the Jupyter notebook that I used to process a 5G NR downlink recording made by Benjamin Menkuec. This is much better to show an example of what I have in mind compared to the LTE recordings I was playing with before. The recording is quite short (so it is small), and I already have code to detect all the “packets”, although I have not been able to identify what kind of signals some of them are.
- ssdv-fec: an erasure FEC for SSDV implemented in Rust
Back in May I proposed an erasure FEC scheme for SSDV. The SSDV protocol is used in amateur radio to transmit JPEG files split in packets, in such a way that losing some packets only cases the loss of pieces of the image, instead of a completely corrupted file. My erasure FEC augments the usual SSDV packets with additional FEC packets. Any set of \(k\) received packets is sufficient to recover the full image, where \(k\) is the number of packets in the original image. An almost limitless amount of distinct FEC packets can be generated on the fly as required.
I have now written a Rust implementation of this erasure FEC scheme, which I have called ssdv-fec. This implementation has small microcontrollers in mind. It is
no_std(it doesn’t use the Rust standard library nor libc), does not perform any dynamic memory allocations, and works in-place as much as possible to reduce the memory footprint. As an example use case of this implementation, it is bundled as a static library with a C-like API for ARM Cortex-M4 microcontrollers. This might be used in the AMSAT-DL ERMINAZ PocketQube mission, and it is suitable for other small satellites. There is also a simple CLI application to perform encoding and decoding on a PC. - Psyche telemetry
In my previous post I spoke about the recording of the telemetry signal from the Psyche spacecraft that I made just a few hours after launch with the Allen Telescope Array. In that post I analysed the physical aspects of the signal and the modulation and coding, but left the analysis of the telemetry frames for another post. That is the topic of this post. It will be a rather long and in-depth look at the telemetry, since I have managed to make sense of much of the structure of the data and there are several protocol layers to cover.
As a reminder from the previous post, the recording was around four hours long. During most of the first three hours, the spacecraft was slowly rotating around one of its axes, so the signal was only visible when the low-gain antenna pointed towards Earth. It was transmitting a low-rate 2 kbps signal. At some point it stopped rotating and switched to a higher rate 61.1 kbps signal. We will see important changes in the telemetry when this switch happens. Even though the high-rate signal represents only one quarter of the recording by duration, due to its 30x higher data rate, it represents most of the received telemetry by size.
- Receiving the Psyche launch
On Friday, the Psyche mission launched on a Falcon Heavy from Cape Canaveral. This mission will study the metal-rich asteroid of the same name, 16 Psyche. For more details about this mission you can refer to the talk that Lindy Elkins-Tanton, the mission principal investigator, gave a month ago at GRCon23.
The launch trajectory was such that the spacecraft could be observed from the Allen Telescope Array shortly after launch. The launch was at 14:19 UTC. Spacecraft separation was at 15:21 UTC. The spacecraft then rose above the ATA 16.8 degree elevation mask in the western sky at 15:53 UTC. However, the signal was so strong that it could be received even when the spacecraft was a couple degrees below the elevation mask, so I confirmed the presence of the signal and started recording a couple minutes earlier. At this moment, the spacecraft was at a distance of 18450 km. The spacecraft continued to rise in the sky, achieving a maximum elevation of 32.9 degrees at 16:53 UTC, and setting below the elevation mask on the west at 19:22 UTC. At this moment the spacecraft was 103800 km away. The signal could still be received for a few minutes afterwards, but eventually became very weak and I stopped recording.
Since the recording started only 30 minutes after spacecraft separation, we get to see some of the events that happen very early on in the mission. Most of the observations of deep space launches that I have done with the ATA have started several hours after launch. This Twitter thread by Lindy Elkins-Tanton gives some insight about the first steps following spacecraft separation, and I will be referring to it to explain what we see in the recording.
I intend to publish the recordings in Zenodo as usual, but the platform has been upgraded recently and is showing the following message “Oct 14 12:03 UTC: We are working to resolve issues reported by users.” So far I have been unable to upload large files, but I will keep retrying and update this post when I manage.
Update 2023-10-19: Zenodo have now solved their problems and I have been able to upload the recordings. They are published in the following datasets:
- BSRC REU GNU Radio tutorial recordings
Since 2021 I have been collaborating with the Berkeley SETI Research Center Breakthrough Listen Summer Undergraduate Research Experience program by giving some GNU Radio tutorials. This year, the tutorials have been recorded and they are now available in the BSRC Tech YouTube channel (actually they have been there since the end of August, but I only realized just now).
These tutorials are intended as an introduction to GNU Radio and SDR in general, focusing on topics and techniques that are related or applicable to SETI and radio astronomy. They don’t assume much previous background, so they can also be useful for GNU Radio beginners outside of SETI. Although each tutorial builds up on concepts introduced in previous tutorials, their topics are reasonably independent, so if you have some background in SDR you can watch them in any order.
All the GNU Radio flowgraphs and other materials that I used are available in the daniestevez/reu-2023 Github repository. Below is a short summary of each of the tutorials.
10ghz artemis1 astronomy astrophotography ATA ccsds ce5 contests digital modes doppler dslwp dsp eshail2 fec freedv frequency gmat gnss gnuradio gomx hermeslite hf jt kits lilacsat limesdr linrad lte microwaves mods noise ofdm orbital dynamics outernet polarization radioastronomy radiosonde rf amplifiers satellites sdr signal generators tianwen vhf & uhf vlbi voyager