Quantisation effects in PDMM: A first study for synchronous distributed averaging

Daan H.M. Schellekens, Thomas Sherson, Richard Heusdens

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with energy, computational power, and bandwidth limitations imposed by such networks, place tight constraints on the transmission capacities of the individual nodes. In this paper we investigate the effects of subtractive dithered uniform quantisation in PDMM for the synchronous distributed averaging problem. This is done by deriving expressions for the mean squared error (MSE) that include quantisation noise. Also, the required data rate for quantised PDMM is considered. It was found that for practical applications quantisation in PDMM can be applied with a fixed-rate quantiser, such that significant data rate reduction can be achieved, without compromising the rate of convergence.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-1-5090-4117-6
Publication statusPublished - 2017
EventICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States
Duration: 5 Mar 20179 Mar 2017
Conference number: 42


ConferenceICASSP 2017
Abbreviated titleICASSP
CountryUnited States
CityNew Orleans, LA
Internet address


  • PDMM
  • quantisation
  • subtractive dithering


Dive into the research topics of 'Quantisation effects in PDMM: A first study for synchronous distributed averaging'. Together they form a unique fingerprint.

Cite this