P25 vocoder FEC

Following a discussion with Carlos Cabezas EB4FBZ over on the Spanish telegram group Radiofrikis about using Codec2 with DMR, I set out to study the error correction used in DMR, since it quickly caught my eye as something rather interesting. As some may know, I’m not a fan of using DMR for Amateur Radio, so I don’t know much about its technical details. On the other hand, Carlos knows a lot about DMR, so I’ve learned much with this discussion.

In principle, DMR is codec agnostic, but all the existing implementations use a 2450bps AMBE codec. The details of the encoding and FEC are taken directly from the P25 Half Rate Vocoder specification, which encodes a 2450bps MBE stream as a 3600bps stream. Here I look at some interesting details regarding the FEC in this specification.

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Improved signal processing for LilacSat-2 VLBI

Last week I published my results about the LilacSat-2 VLBI experiment. There, I mentioned that there were some things I still wanted to do, such as studying the biases in the calculations or trying to improve the signal processing. Since then, I have continued working on this and I have tried out some ideas I had. These have given good results. For instance, I have been able to reduce the delta-range measurement noise from around 700m to 300m. Here I present the improvements I have made. Reading the previous post before this one is highly recommended. The calculations of this post were performed in this Jupyter notebook.

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Amateur VLBI experiment with LilacSat-2

On 23 February, Wei Mingchuan BG2BHC published on Twitter the first Amateur VLBI experiment. This consisted of a GPS-synchronized recording of signals from LilacSat-2 using USRPs in groundstations at Harbin and Chongqing, which are about 2500km apart. Wei has made a Github repository containing the recording (in MATLAB file format) and some signal processing in MATLAB. I have done some signal processing of my own with the recording and published my results in a Jupyter notebook. Here I describe some general aspects about VLBI and its use in Amateur radio, and some specific details of the signal processing I have done.

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Testing LDPC code erasure decoding performance

In my previous post I talked about the RFC5170 LDPC codes used in Outernet. There I explained in some detail the pseudorandom construction of the LDPC codes and the simple erasure decoding algorithm used both in free-outernet and in the official closed-source receiver.

The Outernet LDPC codes follow what I call the “identity scheme”. This is different from the staircase and triangle schemes introduced in the RFC. The identity scheme already appeared in the literature, but it did not make it into the RFC. See, for instance, the report by Roca and Neumann Design, Evaluation and Comparison of Four Large Block FEC Codecs, LDPC, LDGM, LDGM Staircase and LDGM Triangle, plus a Reed-Solomon Small Block FEC Codec, especially Section 2, where it is called “LDGM”.

I also commented that erasure decoding for an LDPC code (or any other linear code) amounts to solving a linear system. This can be done using any algebraic method, such as Gaussian elimination. However, the simple decoding algorithm used in Outernet is as follows: try to find an equation with only one unknown, solve for that unknown, and repeat until the system is solved. Clearly this algorithm can fail even if the system can be solved (see my previous post for some examples and formal results). I will refer to this algorithm as iterative decoding, as it is done in the RFC.

With these two things in mind, I wondered about the performance of the LDPC codes used in Outernet and the iterative decoding algorithm. I’ve done some simulations and here I present my results.

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Studying IONEX files

Many Amateur radio operators are familiar with the effects of the ionosphere at HF frequencies. However, the effects of the ionosphere are also noticeable at much higher frequencies. In particular, at L band, which is used by most satellite navigation systems. Thus, GNSS receivers can be used to measure ionospheric parameters. These measurements are usually distributed as TEC maps in IONEX files.

Here I describe some basic ionospheric physics, how a GNSS receiver can measure the ionosphere, and give some Python code to study TEC maps in IONEX files. Then I use TEC maps to study the CODAR ionospheric observations I did in December last year.

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Mystery 9k6 BPSK satellite

On January 28th, Tetsu JA0CAW reported on Twitter his reception of an unknown satellite. The time of reception was 2018-01-28 12:15 UTC and the frequency was around 435.525MHz. The time and frequency coincided with a PicSat pass over JA0CAW’s station in Japan. He provided an IQ recording of the signal. So far, the satellite that originated the signal has not been identified. Several people have tried to listen to this satellite again, but I haven’t seen any other reports. Doppler identification has not been attempted and it is perhaps unfeasible with the few packets in JA0CAW’s recording.

I have looked at the recording to try to identify the satellite. The modulation is easily seen to be BPSK at 9600baud. The signal presents a lot of fading, so demodulation without bit errors is difficult. There seems to be a scrambler in use. I’ve tried descrambling with G3RUH and CCSDS without any luck. I’ve also failed to identify a preamble or frame sync marker.

To look at the packets in more detail, I’ve resorted to do demodulation as postprocessing in a Jupyter Python notebook. The resulting notebook is here. It is written with detailed comments, so it can be of interest to anyone who wants to learn these techniques.

The only interesting piece of information that I’ve been able to extract from my analysis is that the bits in the packets present strong self-correlations at lags of 1920 bits (and multiples). This is 240 bytes, but I have no clue of what to make of this.

As always, I would be grateful if anyone can provide any additional information about this unknown satellite.

A first look at TY-2

TY-2 is a 6U Chinese cubesat that was launched on January 19th in a CZ-11 rocket from Jiuquan, together with several other small satellites, including TY-6. According to the IARU Satcoord, TY-2 and TY-6 transmit 9k6 GMSK telemetry in the 70cm Amateur satellite band (435.350MHz for TY-2 and 436.100MHz for TY-6).

Several Amateurs such as K4KDR and PD0OXW have tried to decode the packets from TY-2 and TY-6 without success. I have taken a look to an IQ recording of TY-2 that Scott K4KDR has sent me and at least I’ve managed to do something (though not much) with it. Here I describe my findings.

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A CODAR advent

Over the last few days, I have been recording CODAR on 4463kHz to produce images of the ionosphere. I started on Friday 15th and the plan was to leave the recording running until Christmas Day, thus producing some kind of “CODAR advent” images. Unfortunately, there seems to be a problem when the receiver runs for several days that results in the sudden loss of the CODAR signal. This problem can be seen at the bottom of the image below. Thus, I have finished the recording on the morning of the 24th. The equipment and software used is the same that I detailed in a previous post.

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Using CODAR for ionospheric sounding

CODAR is an HF radar used to measure surface ocean currents in coastal areas. Usually, it consists of a chirp which repeats every second. The chirp rate is usually on the order of 10kHz/s, and the signal is gated in small pulses so that the CODAR receiver can listen between pulses. The gating frequency can be on the order of 1kHz.

CODAR can be received by skywave many kilometers inland. Being a chirped signal, it is easy to extract the multipath information from the received signal. In this way, one can see the signal bouncing off the different layers of the ionosphere, and magnificent pictures showing the changes in the ionosphere (especially at dawn and dusk) can be obtained. For instance, see these images by Pieter Ibelings N4IP, or the image at the top of this post, which contains 48 hours worth of CODAR data.

Here I describe my approach to receiving CODAR. It uses GNU Radio for most of the signal processing, and Python with NumPy, SciPy and Matplotlib for plotting.

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