Structured Neural Network with Low Complexity for MIMO Detection

Siyu Liao, Chunhua Deng, Lingjia Liu, Bo Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Neural network has been applied into MIMO detection problem and has achieved the state-of-The-Art performance. However, it is hard to deploy these large and deep neural network models to resource constrained platforms. In this paper, we impose the circulant structure inside neural network to generate a low complexity model for MIMO detection. This method can train the circulant structured network from scratch or convert from an existing dense neural network model. Experiments show that this algorithm can achieve half the model size with negligible performance drop.

Original languageEnglish (US)
Title of host publication2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages290-295
Number of pages6
ISBN (Electronic)9781728119274
DOIs
StatePublished - Oct 2019
Event33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019 - Nanjing, China
Duration: Oct 20 2019Oct 23 2019

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Volume2019-October
ISSN (Print)1520-6130

Conference

Conference33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019
Country/TerritoryChina
CityNanjing
Period10/20/1910/23/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

Keywords

  • MIMO detection
  • circulant
  • neural network

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