深度學習在多用戶大規模輸入輸出系統中預編碼設計上之研究

Research on Precoding Design in Multi-user Large-scale Input-Output System by Deep Learning

林榆澂(Yu-Cheng Lin)

指導教授 ： 李光啟

淡江大學/工學院/電機工程學系碩士班/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00399

摘要

隨著5G標準化，多用戶大規模多輸入多輸出系統（MU M-MIMO system）的設計為一項關鍵技術，其中迫零預編碼器為常見的簡單預編碼方式，在高信噪比情境下，可將多用戶大規模多輸入多輸出系統y=nHFx+n改寫為y=x+n，使得y容易解碼為x。一般而言，迫零預編碼器的主要困難在於赫米特威沙特矩陣HH^H的逆運算，但藉由Chebyshev多項式加速方法，可利用HH^H的特徵值直接推估x，降低運算複雜度。
已知近來研究中，多有以深度學習途徑求解矩陣特徵值之研究，惟其往往對矩陣尺寸與特性有諸多限制，雖取得良好成果，但也缺代應用性。本篇研究試圖以並聯的神經網路架構，增進深度學習方法在矩陣特徵值問題的適用性，並以條件篩選方式增進資料的特徵。

關鍵字

深度學習 ； 人工神經網路

並列摘要

With the standardization of 5G, the design of a multi-user large-scale multi-input multi-output system (MU M-MIMO system) has became a key technology. And the zero-forcing precoder is a simple precoding method. In high signal-to-noise ratio, the received signal y is decoded into the transmitted signal x easily. Generally speaking, the main difficulty of the zero-forcing precoder is the inverse operation of the Hermitian Wishart matrix, HH^H. However, by the Chebyshev polynomial acceleration method, the eigenvalues of HH^Hcan be used to directly estimate x, reducing the computational complexity.

In recent studies, there are many studies on solving matrix eigenvalues by deep learning. However, those studies often have many restrictions on the size and property of the matrix. Although good performances have been achieved, those studies are also lacking in applicability. This study attempts to improve the applicability of deep learning methods to matrix eigenvalue problems with a parallel neural network architecture and use conditional sorting to improve the features of the data.

並列關鍵字

Deep Learning ； Artificial Neural Network

參考文獻

參考文獻

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