TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Deep CNN based Pilot Allocation Scheme in Massive MIMO systems |
| 영문제목(English Title) |
Deep CNN based Pilot Allocation Scheme in Massive MIMO systems |
| 저자(Author) |
Kwihoon Kim
Joohyung Lee
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| 원문수록처(Citation) |
VOL 14 NO. 10 PP. 4214 ~ 4230 (2020. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users’ locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.
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| 키워드(Keyword) |
Deep Learning
CNN
MLP
pilot contamination
pilot assignment
massive MIMO
SIR
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| 파일첨부 |
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