AI based pilot allocation scheme for network capacity maximization in massive MIMO systems = 매시브 MIMO 시스템에서 네트워크 용량을 최대화 하기 위한 인공지능 기반 파일롯 할당 스킴

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This dissertation proposes a Artificial Intelligence (AI) based pilot allocation scheme for network capacity maximization in massive multiple-input multiple-output (MIMO) systems. First, a deep multi-layer perceptron-based pilot allocation scheme (DL-PAS) is proposed for massive MIMO system that use multiple antennas for multiple users in cases of low-density users. The proposed DL-PAS improves the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users' location pattern. In this work, we design a novel supervised learning method where input features and output labels are users' locations in all cells and pilot assignments, respectively. Specifically, pretrained optimal pilot assignments with given users' locations are provided through an exhaustive search method as the training data. Then, the proposed DL-PAS provides a near-optimal pilot assignment from the produced inferred function by analyzing the training data. We implement the proposed scheme using a commercial deep multi-layer perceptron system. Simulation-based experiments show that the proposed scheme achieves almost 99.38 % theoretical upper-bound performance with low complexity, requiring only 0.597 ms computational time. In the other topic of this dissertation, we introduce a novel pilot allocation scheme for a massive multiple-input multiple-output system based on deep-convolutional-neural-network (CNN) learning (DC-PAS). This work is an extension of a prior work on the basic deep learning framework of the pilot allocation problem, the application of which to a high-density user 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, 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 a massive MIMO system and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot allocation scheme using a commercial vanilla CNN system, which takes into account shift invariant characteristics. Through simulation-based experiments, we demonstrate that the proposed scheme realizes almost a 98.00 % theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.
Advisors
Choi, Jun Kyunresearcher최준균researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[v, 89 p. :]

Keywords

Deep Learning▼aCNN▼aMLP▼apilot contamination▼apilot allocation▼amassive MIMO▼aSIR; 딥러닝▼aCNN▼aMLP▼a파일롯 오염▼a파일롯 할당▼aMassive MIMO▼aSIR

URI
http://hdl.handle.net/10203/265292
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=842199&flag=dissertation
Appears in Collection
EE-Theses_Ph.D.(박사논문)
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