Compressed channel feedback for correlated massive MIMO

Min Soo Sim, Jeonghun Park, Chan Byoung Chae, Robert W. Heath

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)


Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.

Original languageEnglish
Article number000012
Pages (from-to)95-104
Number of pages10
JournalJournal of Communications and Networks
Issue number1
Publication statusPublished - 2016 Feb

Bibliographical note

Publisher Copyright:
© 2016 KICS.

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications


Dive into the research topics of 'Compressed channel feedback for correlated massive MIMO'. Together they form a unique fingerprint.

Cite this