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研究生:黃琳証
研究生(外文):Huang, Lin-Zheng
論文名稱:適用於多輸入多輸出正交分頻多工系統之內差式QR分解處理器
論文名稱(外文):Interpolation-based QR Decomposition Processor for MIMO-OFDM system
指導教授:黃元豪黃元豪引用關係
指導教授(外文):Huang, Yuan-Hao
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:102
中文關鍵詞:QR分解內插多輸入多輸出分頻多工多輸入多輸出解碼器群序多候選人制
外文關鍵詞:QR decompositioninterpolationMIMOOFDMMIMO detectorGroup orderMultiple candidates selectionpreprocess
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在先進的MIMO-OFDM系統中速度需求巨幅攀升,以往tone-by-tone 的QR分解構成高運算複雜度,形成了實作上的瓶頸。為了解決此問題,內差式QR分解演算法已證實能降低大量複雜度。本論文提出 one step function 降低內插式QR分解演算法的複雜度,並且加入可規模性讓演算法適用於不同大小的通道矩陣。此外,藉由導入群序(Group order)概念進入內插式QR演算法,群序型內插式QR演算法被提出以提升偵測效能。根據我們所知,提出的內插式QR分解演算法是截至目前為止複雜度最低的QR分解演算法。內插式QR分解的可更改設定之硬體架構被提出,並且選用多候選人制QR-SIC 與之配合。多候選人制QR-SIC擁有可規模化運算複雜度還有彈性效能。內插式QR分解和多候選人制QR-SIC的整合可以實現一套低複雜度且高效能的MIMO detector於各種的MIMO-OFDM系統。因此,多候選人制QR-SIC的迭代式硬體架構被提出,縮短其電路延遲使其運作在更高頻的速度上,並顯示在低維度調變方式時,其有等同於ML的表現。結合上述兩者,提出的QR-based MIMO 架構可支援4x4. 4x2 和2x2通道矩陣,以及QPSK, 16-QAM 和 64-QAM 的調變方式。根據 90nm UMC COMS製程的初步合成結果,提出之內插式QR分解的速度高達31.25 MQRD/s,可以支援3GPP-LTE Rel. 8的硬體規格中下傳鏈路的最高速度。
The throughput requirement of advanced MIMO-OFDM systems increases tremendously recently. The complexity of tone-by-tone QR decomposition becomes the bottleneck of implementation. The interpolation-based QR decomposition(IQRD) algorithm has been proven to reduce the complexity. In the thesis, a one step function is proposed to reduce more complexity of IQRD, and the scalability for different-size channel matrices is attractive to implementation. Moreover, a group-ordering interpolation-based QR decomposition (GO-IQRD) is presented by introducing group-ordering concept into IQRD to increase detection performance. To our best knowledge, the proposed QR decomposition has the lowest complexity among QR decomposition algorithms. A configurable architecture of IQRD without group-order is proposed. Besides, QR-based successive interference cancellation (SIC)
with multiple-candidate selection (MCS-QR-SIC) which has scalable complexity and flexible performance is chosen to cooperate with IQRD. These flexible algorithms are integrated together to be a MIMO detector which could be realized in many MIMO-OFDM systems. The iterative architecture of MCS-QR-SIC is presented. The shorten critical path enables it to work on higher frequency, and it shows the proposed hardware could achieve ML detection performance by controller in low-order modulation. Hence, a QR-based MIMO architecture which supports 4 x 4, 4 x 2, and 2 x 2 channel matrices, with QPSK, 16-QAM, and 64-QAM modulations in the specified bandwidth in the 3GPP-LTE standard is presented. The synthesis result of proposed interpolation-based QR decomposition with UMC 90nm 1P9M CMOS technology shows the corresponding throughput is up to 31.25 MQRD/s, so it could meet the requirement of the highest downlink data rate in hardware categories for 3GPP-LTE Rel. 8.

1 Introduction 1
1.1MIMO Technology..............................1
1.2 Contribution..................................3
1.3 Thesis Organization..............................5
2 MIMO-OFDM System and MIMO Detection 7
2.1 System Model.................................7
2.1.1MIMO-OFDM System Model....................7
2.1.2 3GPP-LTE Release8 Standard...................9
2.2 Review of MIMO Detection.........................12
2.2.1 Linear Detection...........................13
2.2.2 Successive Interference Cancellation (SIC) Detection.........15
2.2.3 Maximum-Likelihood Detection...................18
2.2.4 Sphere Detection and K-best Detection ...............20
2.3 Review of QR preprocessing.........................22
2.3.1 Gram-Schmidt Algorithm......................22
2.3.2 Householder Reflection........................24
2.3.3 Givens Rotation............................25
2.3.4 Interpolation-based QR Decomposition (IQRD)...........28
3 Proposed QR Preprocessing and MIMO Detection Algorithms 33
3.1QR Preprocessing...............................34
3.1.1 Modified Interpolation-based QR Decomposition (MIQRD)....34
3.1.2 Group-Ordering Modified Interpolation-based QR Decomposition (GO-MIQRD).............................39
3.2 Multiple-Candidate-Selection QRSIC....................42
4 Architecture Design of MIQRD and MCS-QRSIC 47
4.1 Architecture Overview............................47
4.2 Architecture Design and Fixed Point Simulation..............53
4.2.1 MIQRD................................53
4.2.2 MCS-QR-SIC.............................67
4.3 Implementation Result............................79
5 Implementation and Verification of MIMO Detector 85
5.1 Pre-simulation.................................86
5.2 Synthesis Result................................86
6 Conclusion 93
6.1 Conclusion...................................93
6.2 Future Work..................................94


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