Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Qian Chen, Shunqing Zhang, Shugong Xu, Shan Cao

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

34 Citations (Scopus)

Abstract

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

Original languageEnglish
Title of host publication2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538676462
DOIs
Publication statusPublished - Apr 2019
Externally publishedYes
Event2019 IEEE Wireless Communications and Networking Conference, WCNC 2019 - Marrakesh, Morocco
Duration: 15 Apr 201919 Apr 2019

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2019-April
ISSN (Print)1525-3511

Conference

Conference2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
Country/TerritoryMorocco
CityMarrakesh
Period15/04/1919/04/19

Keywords

  • deep learning
  • imperfect channel estimation
  • MIMO detection

Fingerprint

Dive into the research topics of 'Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach'. Together they form a unique fingerprint.

Cite this