Monthly Downloads: 11
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License

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A Software Defined Radio library written in Haskell

See the blog post.


  • Write software defined radio applications in Haskell
  • Signal processing blocks can be chained together using the Pipes library
  • Zero copy design
  • Signal processing functions are implemented in both Haskell and C:
    • Optimised C implementations of signal processing functions that utilise SIMD instructions
    • Performance of Haskell signal processing functions within a factor of 2 of C (without SIMD) thanks to the vector library, stream fusion and ghc's LLVM backend
  • Can filter, decimate and resample
  • Helper functions for FIR filter design using window functions and plotting of the frequency response
  • FFTs using FFTW
  • Line and waterfall plots using OpenGL
  • FM demodulation
  • PulseAudio sound sink
  • rtl-sdr and BladeRF based radio sources/sinks supported and other sources are easily added
  • Extensive benchmark and test suites of signal processing functions

See sdr-apps for a collection of simple apps built on the library, sdr-demo for a demo application and bladerf-sdr-apps to get started with the BladeRF.


A chunk of the FM broadcast spectrum. Captured with an RTLSDR device and drawn as a waterfall using the Plot module.


Getting Started


This library will only build and run on 64 bit x86 Linux systems.

You can install it from Hackage:

cabal install sdr

Or, you can build it with cabal sandboxes:

cabal sandbox init
git clone https://github.com/adamwalker/dynamic-graph
git clone https://github.com/adamwalker/haskell-fftw-simple
git clone https://github.com/adamwalker/sdr
cabal sandbox add-source dynamic-graph haskell-fftw-simple sdr
cabal install sdr

If you want to use the BladeRF, you will also need bladerf-pipes and hlibBladeRF.

Example Applications

A collection of simple apps can be found here. These include an FM radio receiver, an OpenGL waterfall plotter and an AM radio receiver that can be used to listen to Airband.

Clone and build:

git clone https://github.com/adamwalker/sdr-apps  
cabal sandbox add-source sdr-apps
cabal install sdr-apps

To run the FM receiver:

.cabal-sandbox/bin/fm -f <your favourite station, e.g. 90.2M>  

To run the waterfall plot:

.cabal-sandbox/bin/waterfall -f <center frequency, e.g. 90.2M> -r <sample rate, e.g. 1280M>

To run the AM receiver:

.cabal-sandbox/bin/am -f <center frequency, e.g. 124.4M> 


Documentation is available on Hackage.

An FM receiver:

import           Control.Monad.Trans.Either
import           Data.Vector.Generic        as VG 
import           Pipes
import qualified Pipes.Prelude              as P

import SDR.Filter 
import SDR.RTLSDRStream
import SDR.Util
import SDR.Demod
import SDR.Pulse
import SDR.CPUID

--The filter coefficients are stored in another module
import Coeffs

samples    = 8192
frequency  = 105700000

main = eitherT putStrLn return $ do

    info <- lift getCPUInfo

    str  <- sdrStream (defaultRTLSDRParams frequency 1280000) 1 (fromIntegral samples * 2)

    lift $ do

        sink <- pulseAudioSink

        deci <- fastDecimatorC info 8 coeffsRFDecim 
        resp <- fastResamplerR info 3 10 coeffsAudioResampler
        filt <- fastFilterSymR info coeffsAudioFilter

        runEffect $   str
                  >-> P.map (interleavedIQUnsignedByteToFloatFast info)
                  >-> firDecimator deci samples 
                  >-> fmDemod
                  >-> firResampler resp samples 
                  >-> firFilter filt samples
                  >-> P.map (VG.map (* 0.2)) 
                  >-> sink


I started this project to learn about signal processing. I still have no idea what I'm doing.

Only tested on Arch Linux.

If you actually use this library for anything, let me know: [email protected]