Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9285
標題: 應用於多輸入多輸出正交分頻多工系統預編碼與訊號偵測之快速矩陣分解法設計與晶片實現
Designs and Chip Implementations of Fast Matrix Decomposition Schemes for Precoding and Signal Detection in MIMO OFDM Systems
作者: 陳韋達
Chen, Wei-Da
關鍵字: 複數QR分解
Complex-valued QR Decompostion
訊號偵測
預編碼
複數奇異值分解
幾何平均分解
多輸入多輸出正交分頻多工
座標旋轉運算器
Signal Detection
Precoding
Complex-valued Singular Values Decomposition
Geometric Mean Decomposition
MIMO OFDM
CORDIC
出版社: 電機工程學系所
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摘要: 在多輸入多輸出的系統,為了基頻訊號處理,經常要承受大量的矩陣運算,這些運算將會成為即時系統實現之重大阻礙。預設碼與訊號偵測是兩個最高運算密集度的模組。在這一篇論文當中,我們著手研究許多不同類型的矩陣分解,這一些經常使用在多輸入多輸出之訊號處理。在第二章,我們首先回顧多輸入多輸出預設碼與訊號偵測分解方式之應用。特別選擇QR分解和幾何平均分解法,由於它們可以分別應用在基於多輸入多輸出QR-BALST訊號偵測以及預編碼上。 在QR分解部分,有兩個版本的設計分別呈現在第三與第四章。第一個是高吞吐量、完全平行的複數QR分解設計且只使用實數吉文氏旋轉(Givens rotation)。低運算複雜度與各種不同分解的計算方式也一併呈現。藉由精心設計硬體排程,一個4 × 4 複數QR分解只需要花費8個時脈週期。遵循電子電機工程師學會(IEEE) 802.11n的標準,發展出2 × 2和4 × 4之QR分解晶片設計。在TSMC 0.18um製程下,所實現的結果,顯示出這兩個設計都有每秒計算15百萬複數QR分解之能力。第二個複數QR分解是第一版本QR分解具有最小均方誤差(MMSE)加強之特色。藉由使用額外訊號處理之摺疊(folding)技巧,使得所建議的設計在計算一個4 × 4複數最小均方誤差QR分解只需要花費四個時脈週期且已發展硬體在TSMC 0.18um製程下和兩種不同FPGA平台(Xilinx and Altera)裡。 在幾何平均分解的部分,將開發兩個有效率的計算方式,分別在第五與第六章。有別於傳統基於幾何平均分解演算法的奇異值分解。這兩個版本都是使用雙對角線矩陣視為計算幾何平均分解之前處理,而取代原來的奇異值分解。它們具有低複雜度和不需要置換運算之特色且預算碼與訊號偵測之間的硬體可以共用。第一個幾何平均分解運算的方式,是採用漸進的方式,每次的運算都是從一個2×2 sub-matrix開始,最後可得到幾何平均分解的結果;而第二個方式是採用分治法(divide-and-conquer)計算策略。運算複雜度分析顯示出,相較於傳統的方式,計算效率至少優於30%。在第七章,將處理硬體實現。建議的GMD演算法將會映射到完全平行化以及高管線化的架構上,且此架構運算一個4×4複數矩陣的GMD只需要四個時脈週期。它也具支援兩個運算模組之整合架構,另如:針對訊號偵測的QR分解和預設碼的幾何平均分解。在TSMC 90nm CMOS製程下的晶片實現,最大時脈頻率可達到170MHz且此設計每秒可以計算42.5M個幾何平均分解或是QR分解的運算。最後在第八章,將敘述結論與這篇論文的未來應用。
Multiple Input Multiple Output (MIMO) systems often impose tremendous computing overheads in the form of matrix operations to the base band signal processing. This becomes a formidable barrier in real time system implementation. In particular, precoding and signal detection are the two most computation-intensive modules. In this dissertation, we start with an investigation on various matrix decomposition schemes commonly used in MIMO signal processing. The applications of these decomposition schemes on MIMO signal detection and precoding are first reviewed in chapter 2. In particular, QR decomposition and geometric mean decomposition are chosen specifically for the applications in QR-blast based MIMO signal detection and MIMO signal pre-coding, respectively. In the QR decomposition part, two versions of the design are presented in chapter 3 and chapter 4, respectively. The first one indicates a high throughput, fully parallel Complex-valued QR Decomposition (CQRD) design using real-valued Givens rotations only. The simplicity in computing complexity against various decomposition schemes is shown. Via a carefully plotted scheduling, one CQRD computation can be completed in 8 clock cycles. Sized 2 � 2 and 4 � 4 chip designs largely following the IEEE 802.11n standard are developed. The implementation results in TSMC 0.18 um process technology show that both designs are capable of performing 15M CQRDs per second. The second CQRF design features a minimum mean square error (MMSE) enhancement of the first one. By applying an additional DSP folding technique, the design takes only four clock cycles to perform a 4x4 complex-valued MMSE-QR decomposition. The ASIC fabrication in a TSMC 0.18µm process technology and the FPGA implementations in two types of FPGA devices (Xilinx and Altera) are developed. In the GMD part, two versions of the efficient computing scheme are developed in chapter 5 and chapter 6. Unlike conventional SVD based GMD algorithms, both schemes use matrix bi-diagonalization rather than SVD as the pre-processing step. They also feature lower computing complexities, permutation-free operations, and hardware sharing between the pre-coding and the signal detection modules. The first version of the GMD computing scheme adopts a progressive approach and obtains the GMD result incrementally starting from a 2�2 sub-matrix. The second version of the GMD scheme adopts a divide-and-conquer computing strategy. Computing complexity analyses indicate at least 30% more computing efficiency than other SVD based GMD computing schemes. In chapter 7, the hardware implementation is addressed. The scheme is mapped to a fully parallel and deeply pipelined architecture where one GMD computation of a 4�4 complex-valued matrix can be accomplished every 4 clock cycles. It also features a joint design supporting two computing modes, i.e. QRD for signal decoding and GMD for precoding. Chip implementation in TSMC 90nm CMOS technology shows that, with a maximum clock frequency up to 170MHz, the design can perform 42.5M GMD or QRD computations per second. Finally, in chapter 8, the conclusion and the future work of this dissertation are drawn.
URI: http://hdl.handle.net/11455/9285
其他識別: U0005-3006201316182200
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