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. As a sparsifying basis, prior work has applied compressive sensing (CS) techniques with the two-dimensional discrete cosine transform (2D-DCT) and the instantaneous Karhunen-Loeve transform (KLT). 2D-DCT fails, however, to reflect the spatial correlation and channel conditions. Instantaneous KLT requires perfect CSI, which means it is not feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel and a new reconstruction algorithm for CS. We also suggest that dimensionality reduction is more proper to compress, and compare performance with the conventional method. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single user) and point-to-multi-point (multiuser) scenarios.
|Title of host publication||2014 IEEE Globecom Workshops, GC Wkshps 2014|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2014 Mar 18|
|Event||2014 IEEE Globecom Workshops, GC Wkshps 2014 - Austin, United States|
Duration: 2014 Dec 8 → 2014 Dec 12
|Name||2014 IEEE Globecom Workshops, GC Wkshps 2014|
|Other||2014 IEEE Globecom Workshops, GC Wkshps 2014|
|Period||14/12/8 → 14/12/12|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications