在先進的MIMO-OFDM系統中，速度需求巨幅攀升，以往tone-by-tone的QR分解處理方式，在QR分解部分耗費相當多的計算，此高運算複雜度特性造成了實作上的瓶頸。為了解決上述傳統作法之高運算量問題，內插式QR分解演算法(interpolation-based QR decomposition, IQRD)已證實能改良傳統作法，降低大量複雜度。因此，此篇論文主要研究內插式QR分解演算法並提出修正的方法已改良運算的複雜度。在利用Q和R矩陣進行解碼的演算法中，由於矩陣R是一上三角矩陣，矩陣解碼順序為由下而上，而第一個進行解碼的信號(symbol)會迭代回方程式中進行後面的信號解碼，因此越早進行解碼的信號，其準確性影響錯誤延遲的情形，對解碼正確率的影響亦越大。在提出的修正方法中，利用上述特性，在進行內插程序時，將相對應於第一個進行解碼信號的最後一列R矩陣之數值使用足夠的pilot數以減少內插錯誤，而其餘的信號則選擇較少的pilot數進行內插以減少運算複雜度且選取的方式為最接近欲內插資料的pilot信號群，透過此種方式可以同時減少運算複雜度和維持解碼準確性。
此外，本篇研究的另一主題是將內插式QR分解應用於結合晶格演算法(LR)的MIMO-OFDM系統。LR已被提出應用於MIMO系統接收端以大幅降低解碼錯誤率，但OFDM系統中的每一個子載波(sub-carrier)皆需要一次LR運算，因此在本論文中提出利用內插式QR分解減少LR在系統中的運算量，並附上模擬的結果、運算量和硬體分析，此外，採用群組(group)架構，在硬體設計上可以根據需求作彈性的調整。從模擬和分析的結果顯示，結合內插式QR分解有效且大幅降低系統複雜度。

1 Introduction
1.1 Background
1.2 Research Motivation
1.3 Organization of This Thesis
2 System Model
2.1 MIMO-OFDM System Model
2.2 QR-based MIMO Detection
2.3 Channel Estimation
2.4 3GPP-LTE System
3 Interpolation-based QR Decomposition Research
3.1 QR Decomposition Algorithm
3.1.1 Gram-Schmidt Process
3.1.2 Householder Reflection
3.1.3 Givens Rotation Method
3.2 Laurent Polynomials and Interpolation
3.2.1 Laurent Polynomial Definition
3.2.2 Laurent Polynomial Interpolation
3.2.3 Laurent Polynomial Mapping Function
3.2.4 Exact and Inexact Interpolation
3.3 Interpolation-based QRD Algorithm
3.3.1 Single Step Interpolation-based QRD Processing
3.3.2 Multiple Steps Interpolation-based QRD Processing
3.4 Modified Interpolation-based QRD Algorithm
4 Lattice Reduction-Aided MIMO-OFDM Detection
4.1 Lattice Reduction Algorithm
4.2 Lattice Reduction-Aided MIMO-OFDM Architectures
4.3 Proposed Lattice Reduction-Aided MIMO-OFDM Detector with IQRD
5 Complexity Analysis and Simulation Results
5.1 Simulated Setting
5.2 Simulation of Modified Interpolation-based QR Decomposition
5.2.1 Exact Interpolation Case
5.2.2 Inexact Interpolation Case
5.3 Simulation of Proposed Lattice Reduction-Aided MIMO-OFDM Detector
6 Conclusion

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