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Spectrogram (80MHz bandwidth) accelerator for LimeSDR

Project description

# Spectrogram (80MHz bandwidth) with LimeSDR and GQRX

![Bluetooth GIF]( “Demo”)

[LimeSDR-Mini]( diagram:

![Block diagram]( “Diagram”)

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

## Install

Install the helper script to bootstrap the Docker images (Linux PC/ARM architectures):

`pip install spectrogram`

_**Rasbian note:** Use `pip3`. Executable is installed to `/home/pi/.local/bin/spectrogram`, which is not on PATH by default._

## 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

_**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:

![Pi setup]( “lime_mini_screen”)

_**Notes:** Current draw was around 1.25A@5V. 5’ TFT-Display created some noise in the spectrogram, this was fixed by using HDMI display. Later allowed much higher resolution, which caps the CPU when the spectrogram is running full-screen._

### 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.

_**Note:** [LimeNet-Micro]( is ideal for remote applications - it has LimeSDR, RaspberryPi and power-over-ethernet on single board. Work in progress ([#9](

## MISC ### Accuracy vs floating-point model

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

![fix vs float accuracy](


### 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.

![132k FFT vs 512 + averaging]( [Reproduce](

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

### Cooling solutions

#### No cooling

![No cooling](

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

You will risk damaging your board!

#### Heat-sink on FPGA

![FPGA sinked](

Temperature is stable at ~65C after 10 minutes.

#### Heat-sink everything

![Massive sink](

Temperature is stable at ~54C after 20 minutes.

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