Decoding MOVE-II

MOVE-II is a cubesat from Technical University of Munich that was launched in December 2018. It transmits telemetry in the 145 MHz amateur satellite band using a protocol that uses CCSDS LDPC codewords. Back in the day, there was a GNU Radio out-of-tree module developed by the satellite team to decode this satellite. Given the additional effort required to support LDPC decoding for just this satellite and since there was already a GNU Radio decoder available, I never added a decoder for MOVE-II to gr-satellites.

Fast forward 5 years, and MOVE-II is still active, but apparently its GNU Radio out-of-tree module has bit rotten. The Gitlab repository where this was hosted (I believe it was a self-hosted Gitlab) has disappeared, and while it was originally developed for GNU Radio 3.7, it was never ported to newer GNU Radio versions. Some days ago, some amateurs including Scott Chapman K4KDR and Bob Mattaliano N6RFM started doing some experiments to try to get a decoder for MOVE-II working.

Seeing this, I decided to revisit the situation and try to add a decoder for MOVE-II to gr-satellites. Since this satellite was launched, I have been dealing with CCSDS LDPC for the Artemis Orion, made my own LDPC decoder, and participated in fixing the GNU Radio in-tree LDPC decoder. Therefore, most of the heavy lifting seemed to be already done.

I have now added an example decoder flowgraph for MOVE-II to gr-satellites. Here I describe the details of this example, and why it is only an example instead of a fully supported decoder as the ones that exist for other satellites.

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.

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.

DVB-S2 GRCon23 CTF challenge

This year I submitted a challenge track based around a DVB-S2 signal to the GRCon CTF (see this post for the challenges I sent in 2022). The challenge description was the following.

I was scanning some Astra satellites and found this interesting signal. I tried to receive it with my MiniTiouner, but it didn’t work great. Maybe you can do better?

Note: This challenge has multiple parts, which can be solved in any order. The flag format is flag{...}.

A single SigMF recording with a DVB-S2 signal sampled at 2 Msps at a carrier frequency of 11.723 GHz was given for the 10 flags of the track. The description and frequency were just some irrelevant fake backstory to introduce the challenge, although some people tried to look online for transponders on Astra satellites at this frequency.

The challenge was inspired by last year’s NTSC challenge by Clayton Smith (argilo), which I described in my last year’s post. This DVB-S2 challenge was intended as a challenge with many flags that can be solved in any order and that try to showcase many different places where one might sensibly put a flag in a DVB-S2 signal (by sensibly I mean a place that is actually intended to transmit some information; surely it is also possible to inject flags into headers, padding, and the likes, but I didn’t want to do that). In this sense, the DVB-S2 challenge was a spiritual successor to the NTSC challenge, using a digital and modern TV signal.

Another source of motivation was one of last year’s Dune challenges by muad’dib, which used a DVB-S2 Blockstream Satellite signal. In that case demodulating the signal was mostly straightforward, and the fun began once you had the transport stream. In my challenge I wanted to have participants deal with the structure of a DVB-S2 signal and maybe need to pull out the ETSI documentation as a help.

I wanted to have flags of progressively increasing difficulty, keeping some of the flags relatively easy and accessible to most people (in any case it’s a DVB-S2 signal, so at least you need to know which software tools can be used it to decode it and take some time to set them up correctly). This makes a lot of sense for a challenge with many flags, and in hindsight most of the challenges I sent last year were quite difficult, so I wanted to have several easier flags this time. I think I managed well in this respect. The challenge had 10 flags, numbered more or less in order of increasing difficulty. Flags #1 through #8 were solved by between 7 and 11 teams, except for flag #4, which was somewhat more difficult and only got 6 solves. Flags #9 and #10 were significantly more difficult than the rest. Vlad, from the Caliola Engineering LLC team, was the only person that managed to get flag #10, using a couple of hints I gave, and no one solved flag #9.

When I started designing the challenge, I knew I wanted to use most of the DVB-S2 signal bandwidth to send a regular transport stream video. There are plenty of ways of putting flags in video (a steady image, single frames, audio tracks, subtitles…), so this would be close to the way that people normally deal with DVB-S2, and give opportunities for many easier flags. I also wanted to put in some GSE with IP packets to show that DVB-S2 can also be used to transmit network traffic instead of or in addition to video. Finally, I wanted to use some of the more fancy features of DVB-S2 such as adaptive modulation and coding for the harder flags.

To ensure that the DVB-S2 signal that I prepared was not too hard to decode (since I was putting more things in addition to a regular transport stream), I kept constantly testing my signal during the design. I mostly used a Minitiouner, since perhaps some people would use hardware decoders. I heard that some people did last year’s NTSC challenge by playing back the signal into their hotel TVs with an SDR, and for DVB-S2 maybe the same would be possible. Hardware decoders tend to be more picky and less flexible. I also tested gr-dvbs2rx and leandvb to have an idea of how the challenge could be solved with the software tools that were more likely to be used.

What I found, as I will explain below in more detail, is that the initial way in which I intended to construct the signal (which was actually the proper way) was unfeasible for the challenge, because the Minitiouner would completely choke with it. I also found important limitations with the software decoders. Basically they would do some weird things when the signal was not 100% a regular transport stream video, because other cases were never considered or tested too thoroughly. Still, the problems didn’t get too much in the way of getting the video with the easier flags, and I found clever ways of getting around the limitations of gr-dvbs2rx and leandvb to get the more difficult flags, so I decided that this was appropriate for the challenge.

In the rest of this post I explain how I put together the signal, the design choices I made, and sketch some possible ways to solve it.

ldpc-toolbox gets LDPC decoding

Recently I have implemented an FPGA LDPC decoder for a commercial project. The belief propagation LDPC decoder algorithm admits many different approximations in the arithmetic, and other tricks that can be used to trade off between decoding sensitivity (BER versus Eb/N0 performance) and computational complexity. To help me benchmark the different belief propagation algorithms, I have extended my ldpc-toolbox project to implement many different LDPC decoding algorithms and perform BER simulations.

ldpc-toolbox is a Rust library and command line tool for the design of LDPC codes. I initially created this project when I was trying to design a suitable LDPC code for a narrowband 32APSK modem to be used over the QO-100 amateur GEO transponder. The tool so far supported some classical pseudorandom constructions of LDPC codes, computed Tanner graph girths, and could construct the alists for all the DVB-S2 and CCSDS LDPC codes. Extending this tool to support LDPC encoding, decoding and BER simulation is a natural step.

Decoding Euclid

Euclid is an ESA near-infrared space telescope that was launched to the Sun-Earth Lagrange L2 point on July 1, using a Falcon 9 from Cape Canaveral. The spacecraft uses K-band to transmit science data, and X-band with a downlink frequency of 8455 MHz for TT&C. On July 2 at 07:00 UTC, 16 hours after launch and with the spacecraft at a distance of 167000 km from Earth, I recorded the X-band telemetry signal using antennas 1a and 1f from the Allen Telescope Array. The recording lasted approximately 4 hours and 30 minutes, until the spacecraft set.

Even though the telemetry signal fits in about 300 kHz, I recorded at 4.096 Msps, since I wanted to see if there were ranging signals at some point. Since the IQ recordings for two antennas at 4.096 Msps 16-bit are quite large, I have done some data reduction before publishing to Zenodo. I have decided to publish only the data for antenna 1a, since the SNR in a single antenna is already very good, so there is no point in using the data for the second antenna unless someone wants to do interferometry. Since looking in the waterfall I saw no signals outside of the central 2 MHz, I decimated to 2.048 Msps 8-bit. Also, I synthesized the signal polarization from the two linear polarizations. To fit within Zenodo’s constraints for a 50 GB dataset, I split the recording in two parts of 32 GiB each.

The recording is published in these two datasets:

In this post I will look at the signal modulation and coding and present GNU Radio decoders. I will look at the contents of the telemetry frames in a future post.

Decoding JUICE

JUICE, the Jupiter Icy Moons Explorer, is ESA’s first mission to Jupiter. It will arrive to Jupiter in 2031, and study Ganymede, Callisto and Europa until 2035. The spacecraft was launched on an Ariane 5 from Kourou on April 14. On April 15, between 05:30 and 08:30 UTC, I recorded JUICE’s X-band telemetry signal at 8436 MHz using two of the 6.1 m dishes from the Allen Telescope Array. The spacecraft was at a distance between 227000 and 261000 km.

The recording I made used 16-bit IQ at 6.144 Msps. Since there are 4 channels (2 antennas and 2 linear polarizations), the total data size is huge (966 GiB). To publish the data to Zenodo, I have combined the two linear polarizations of each antenna to form the spacecraft’s circular polarization, and downsampled to 8-bit IQ at 2.048 Msps. This reduces the data for each antenna to 41 GiB. The sample rate is still enough to contain the main lobes of the telemetry modulation. As we will see below, some ranging signals are too wide for this sample rate, so perhaps I’ll also publish some shorter excerpts at the higher sample rate.

The downsampled IQ recordings are in the following Zenodo datasets:

In this post I will look at the signal modulation and coding, and some of its radiometric properties. I’ll show how to decode the telemetry frames with GNU Radio. The analysis of the decoded telemetry frames will be done in a future post.

More details about Orion uncoded telemetry

In a previous post I analysed the residual carrier telemetry of the Artemis I Orion capsule using some recordings done by CAMRAS with the 25 m radio telescope at Dwingeloo observatory. I noticed that, in contrast to some recordings that I had done early after launch with the Allen Telescope Array, in those recordings the telemetry was uncoded instead of using LDPC. I related that finding to some tweets from Richard Stephenson about the project switching frequenctly between residual carrier and OQPSK, and between uncoded and LDPC.

I wanted to study the situation in more detail, for example to see what combinations of residual carrier / OQPSK and uncoded / LDPC were possible. Since CAMRAS hasn’t made available on their web server all the recordings they did, due to disk space constraints, I asked them to publish a few additional recordings that seemed interesting to this end. This is a short post with my findings about those new recordings.

Decoding Lunar Flashlight

Lunar Flashlight is a 6U NASA cubesat whose mission is to detect the presence of water ice on permanently shadowed regions of the lunar south pole. It was launched on December 11 2022 together with Hakuto-R M1 (to which I dedicated my previous post). It travels using a low-energy transfer to lunar orbit, so it will arrive to the Moon in a few months.

The day after the launch, AMSAT-DL made an IQ recording of the X-band beacon of Lunar Flashlight at 8457.27 MHz with the 20 metre antenna at Bochum observatory. The recording was done on 2022-12-12 17:08:54 UTC and lasts 3 minutes 2 seconds. In this post I will analyse the recording.

Decoding Hakuto-R M1

Hakuto-R Mission 1 is a private lunar mission led by the Japanese company ispace. It consists of a lander, which carries the Emirates Lunar Mission rover Rashid and JAXA/Tomy’s SORA-Q toy-like robot. It was launched on a Falcon 9 from Cape Canaveral together with the cubesat Lunar Flashlight on 11 December 2022, and will attempt to land on the Moon approximately 4.5 months after launch.

AMSAT-DL made some recordings of Lunar Flashlight and Hakuto-R M1 in the days following the launch using the 20 meter antenna at Bochum observatory. Here I will look at two recordings of the X-band telemetry signal of Hakuto-R M1 at 8492.5 MHz done on 2022-12-11 at 22:48:43 (121 seconds at 1.25 Msps IQ) and 23:23:08 UTC (54 seconds at 5 Msps IQ).