Name
Abstract | ||
|---|---|---|
AbstractAbstractIn massive multiple‐input–multiple‐output system, the accurate channel state information at the transmitter (CSIT) is required to achieve the high beamforming gain and spectral efficiency. To reduce the feedback overhead for CSIT acquisition in frequency division duplex system, a prior CSIT estimation with 1‐bit feedback per dimension is proposed to alleviate the overhead burden. Specifically, by exploiting the slow variation of channel support between the consecutive frames, the channel support in the previous frame is regarded as the prior information for CSIT estimation of current frame. Then, the received compressed channel measurements at the user side are quantized to 1 bit per measurement and only the sign information is fed back to the base station (BS). At the BS side, the CSIT recovery from the 1‐bit channel feedback information is formulated as a 1‐bit sparse complex‐value signal recovery problem with prior information. Finally, a prior support complex binary iterative hard thresholding algorithm is proposed to perform the CSIT estimation at the BS side. Simulation results demonstrate that the proposed scheme can reduce both the pilot and feedback overhead for CSIT estimation. View Figure A prior CSIT estimation with 1‐bit feedback per dimension is proposed to alleviate the overhead burden in FDD massive MIMO system. At the BS side, the CSIT recovery from the 1‐bit channel feedback information is formulated as a 1‐bit sparse complex value sig nal recovery problem with prior information. Finally, a prior support complex bin ary iterative hard thresholding algorithm is proposed to perform the CSIT estimation at the BS side. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1002/ett.3644 | Periodicals |
Field | DocType | Volume |
Control theory,Computer science,MIMO | Journal | 30 |
Issue | ISSN | Citations |
12 | 2161-3915 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruoyu Zhang | 1 | 6 | 2.79 |
| Jia Yan Zhang | 2 | 10 | 5.24 |
| Yulong Gao | 3 | 75 | 12.30 |
| Honglin Zhao | 4 | 6 | 4.82 |