eedesignhouse / spectrogram

80MHz bandwidth with LimeSDR-Mini and GQRX

80MHz bandwidth with LimeSDR-Mini and GQRX

LimeSDR-Mini diagram:

Note: DC-removal is based on Linear-phase DC Removal Filter (Dual-MA 1024 taps)

Install

Works on Linux PC distributions (Ubuntu, Manjaro ...) or ARM boards (Rasperry Pi 3/4, Jetson Nano ...). First install docker:

curl -fsSL https://get.docker.com | sh

You must then add your user to the docker group:

sudo groupadd docker
sudo usermod -aG docker $USER

Log out and back in (or restart the computer) so that your group membership is re-evaluated.

Finally you need to install the 'spectrogram' script:

pip3 install spectrogram

Ubuntu note: You may need to first install pip3 with sudo apt install python3-pip.

Rasbian/Ubuntu note: Script is installed to ~/.local/bin/spectrogram, which is not on PATH by default. You need to execute it by using full path!

Usage

Invoking spectrogram does following:

1. If needed, programs the LimeSDR-Mini with FPGA accelerator ( restore with spectogram --fpga_restore)
2. Starts the local 'SoapySDR-Remote' server
3. Starts GQRX - this connects to the SoapySDR server and displays the spectrogram

Warning: You should cool your LimeSDR-Mini, especially the FPGA. It takes 2.5 minutes for FPGA temperature to rise from 30C to 80C, after which you risk damage!

Works on RaspberryPi:

Notes: Current draw was around 1.25A@5V. 5’ TFT-Display created some noise in the spectrogram - this was not a problem with HDMI display. OTOH HDMI supports higher resolution, which may cap the CPU if GQRX window is too big (updating the waterfall is expensive).

Remote usage

Pair your LimeSDR-Mini with RaspberryPi and execute spectrogram --server_only - this sets up a SoapySDR-Remote server. Next, on the monitoring device, execute spectrogram - this scans for remote devices and opens GQRX if one is found. Network bandwidth will be around 1 MB/s.

MISC

Using without GQRX

See demo notebook

Accuracy vs floating-point model

This is a fixed-point accelerator, accuracy against the floating-point model has been verified.

Reproduce

How is 512 point FFT comparable to 131k FFT??

It's about how many samples are averaged e.g. the 131k FFT averages 131k samples - same can be achieved with 512 point FFT and averaging 256 results - 512*256 = 131k.

Reproduce

In general this is a trade-off - hardware complexity is reduced, but you will lose ~3dB dynamic range.

Cooling solutions

No cooling

Took 5 minutes to go from cold to critical FPGA temperature.

You will risk damaging your board!

Heat-sink on FPGA

Temperature is stable at ~65C after 10 minutes.

Heat-sink everything

Temperature is stable at ~54C after 20 minutes.