高階調變多輸入多輸出正交分頻多工系統中基於機率資料聯結演算法之資料檢測方法

Abstract

今天我們的資訊社會的特點是變得越來越需要移動性以及無障礙性。同時，對於高傳輸率的需求也大大的增加。多輸入多輸出正交分頻多工(MIMO-OFDM)空間多工系統是一種可行的技術，它可以用來改進無線通訊訊號的品質以及傳輸的速率。在MIMO-OFDM空間多工系統的資料檢測中，球狀解碼是一種被廣泛使用的次佳解檢測器。它擁有非常好的效能，但是它的複雜度會隨著通道和訊雜比而改變。在這篇論文中，我們證明了SSIC演算法與PDA演算法是等效的。此外，我們提出了兩種檢測器，GPDA-MCPDA 與 GPDA-SD，用來降低球狀解碼在接近最佳解的MIMO-OFDM空間多工系統下使用高階QAM調變(16QAM/ 64QAM)的複雜度。模擬結果顯示，這兩種檢測器都可以達到接近最佳解的效能而且它們的優勢在於擁有比球狀解碼還低的複雜度，特別是在低訊雜比的區域。

Our information society today is marked by an increasing need for mobility and accessibility. At the same time, the demand for ever higher data transfer rates is also increasing. A MIMO-OFDM spatial multiplexing system is a proven technology that can improve signal quality and data rate for wireless communications. Sphere Decoding (SD) algorithm is a popular suboptimal data detection method in a MIMO-OFDM spatial multiplexing system. It although performs very well but suffers from the fact that its complexity is a random variable depending on channel and signal to noise ratio (SNR). In this thesis, we establish the equivalence of the SSIC algorithm and the PDA algorithm. Furthermore, we proposed two detectors, GPDA-MCPDA and GPDA-SD, to reduce the complexity of the sphere decoding algorithm for near-optimal detection in a MIMO-OFDM spatial multiplexing system with higher order QAM constellations (16QAM/64QAM). Simulation results demonstrate that both detectors can achieve near-optimal performance with lower complexity as compare with the sphere decoding algorithm, especially at the low SNR regions.