Robust MIMO Detection using Hypernetworks with Learned Regularizers

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra

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

1 Scopus citations


Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Recently, there has been a growing interest to get reasonably close to the optimal solution using neural networks while keeping the computational complexity in check. However, existing work based on deep learning shows that it is difficult to design a generic network that works well for a variety of channels. In this work, we propose a method that tries to strike a balance between symbol error rate (SER) performance and generality of channels. Our method is based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel. We propose a general framework by regularizing the training of the hypernetwork with some pre-trained instances of the channel-specific method. Through numerical experiments, we show that our proposed method yields high performance for a set of prespecified channel realizations while generalizing well to all channels drawn from a specific distribution.

Original languageEnglish (US)
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797091
StatePublished - 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: Aug 29 2022Sep 2 2022

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference30th European Signal Processing Conference, EUSIPCO 2022


  • deep learning
  • hypernetwork
  • MIMO detection

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Robust MIMO Detection using Hypernetworks with Learned Regularizers'. Together they form a unique fingerprint.

Cite this