A Low Overhead Feedback Scheme of Channel Covariance Matrix for Massive MIMO Systems

Youngrok Jang, Dongheon Lee, Sooyong Choi

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

Abstract

In this paper, we propose a feedback scheme of a channel covariance matrix with low overhead for massive multiple-input multiple-output systems. The proposed scheme decomposes the channel covariance matrix into the phase and amplitude parts. For the phase part, the element-wise uniform scalar quantization is performed. For the amplitude part, the following feedback information is generated: a bitmap which denotes a sign of difference between adjacent elements, the first value, an increment, and a decrement. To calculate the elements of the amplitude part, starting from the first value, when a bitmap is 1, the increment is added, otherwise the decrement is added. Simulation results show that the feedback overhead of the proposed scheme can be significantly reduced from 1.92% to 48.33% while the performance of mean square error can be maintained with that of the conventional scheme.

Original languageEnglish
Title of host publicationICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages714-717
Number of pages4
ISBN (Print)9781538646465
DOIs
Publication statusPublished - 2018 Aug 14
Event10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic
Duration: 2018 Jul 32018 Jul 6

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2018-July
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Other

Other10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
Country/TerritoryCzech Republic
CityPrague
Period18/7/318/7/6

Bibliographical note

Funding Information:
This work was partially supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00181, Development on the core technologies of transmission, modulation and coding with low-power and low-complexity for massive connectivity in the IoT environment) and by the Graduate School of YONSEI University Research Scholarship Grants in 2017.

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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