Paper Abstract and Keywords |
Presentation |
2017-11-08 16:20
Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction Rei Nagashima, Tomoaki Ohtsuki (Keio Univ.), Wenjie Jiang, Yasushi Takatori (NTT) RCS2017-221 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiver to the transmitter, due to the enormous number of antennas. For the purpose of solving this issue, there exists the technique to compress CSI to a lower dimension matrix and decrease the amount of feedback, by principal component analysis (PCA). In the conventional method, the compression matrix to compress a channel matrix is calculated on the basis of PCA, and the compressed channel is fed back from the receiver to the base station (BS). However, it is necessary to feed back the compression matrix to compress a channel once every updating interval $T_s$ where the compression matrix accounts for a large portion of the amount of feedback. Therefore, there exists a problem that the amount of feedback increases when $T_s$ is small. In this report, to solve this problem, we propose a method to compress the channel matrix based on multi-dimensional scaling (MDS) with channel prediction. In the proposed method, we decrease the size of the CSI by mapping the channels on the multi-dimensional space and reducing the number of dimensions using MDS, thus our method does not need generating and feeding back the compression matrix. By computer simulation, we show that the proposed method achieves the same system capacity with the smaller amount of feedback compared to the conventional one based on PCA when the channel changes fast. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Massive MIMO / 5G / Channel Compression / Multi-Dimensional Scaling / Channel Prediction / Feedback Reduction / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 284, RCS2017-221, pp. 93-98, Nov. 2017. |
Paper # |
RCS2017-221 |
Date of Issue |
2017-11-01 (RCS) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Download PDF |
RCS2017-221 |
Conference Information |
Committee |
AP RCS |
Conference Date |
2017-11-08 - 2017-11-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Fukuoka University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. |
Paper Information |
Registration To |
RCS |
Conference Code |
2017-11-AP-RCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction |
Sub Title (in English) |
|
Keyword(1) |
Massive MIMO |
Keyword(2) |
5G |
Keyword(3) |
Channel Compression |
Keyword(4) |
Multi-Dimensional Scaling |
Keyword(5) |
Channel Prediction |
Keyword(6) |
Feedback Reduction |
Keyword(7) |
|
Keyword(8) |
|
1st Author's Name |
Rei Nagashima |
1st Author's Affiliation |
Keio University (Keio Univ.) |
2nd Author's Name |
Tomoaki Ohtsuki |
2nd Author's Affiliation |
Keio University (Keio Univ.) |
3rd Author's Name |
Wenjie Jiang |
3rd Author's Affiliation |
NTT Network Innovation Laboratories (NTT) |
4th Author's Name |
Yasushi Takatori |
4th Author's Affiliation |
NTT Network Innovation Laboratories (NTT) |
5th Author's Name |
|
5th Author's Affiliation |
() |
6th Author's Name |
|
6th Author's Affiliation |
() |
7th Author's Name |
|
7th Author's Affiliation |
() |
8th Author's Name |
|
8th Author's Affiliation |
() |
9th Author's Name |
|
9th Author's Affiliation |
() |
10th Author's Name |
|
10th Author's Affiliation |
() |
11th Author's Name |
|
11th Author's Affiliation |
() |
12th Author's Name |
|
12th Author's Affiliation |
() |
13th Author's Name |
|
13th Author's Affiliation |
() |
14th Author's Name |
|
14th Author's Affiliation |
() |
15th Author's Name |
|
15th Author's Affiliation |
() |
16th Author's Name |
|
16th Author's Affiliation |
() |
17th Author's Name |
|
17th Author's Affiliation |
() |
18th Author's Name |
|
18th Author's Affiliation |
() |
19th Author's Name |
|
19th Author's Affiliation |
() |
20th Author's Name |
|
20th Author's Affiliation |
() |
Speaker |
Author-1 |
Date Time |
2017-11-08 16:20:00 |
Presentation Time |
25 minutes |
Registration for |
RCS |
Paper # |
RCS2017-221 |
Volume (vol) |
vol.117 |
Number (no) |
no.284 |
Page |
pp.93-98 |
#Pages |
6 |
Date of Issue |
2017-11-01 (RCS) |
|