A Study on MIMO Detection Based on QR Decomposition with M-algorithm

[[abstract]]在多輸入多輸出(multiple-input multiple-output，MIMO)系統中，最大相似偵測器(Maximum likelihood detection，MLD) 擁有最佳偵測效能，但其複雜度會隨著傳送天線數及訊號的調變等級呈指數增加，造成巨大的運算量，也成為硬體實現上的阻礙。基於QR分解M演算法的最大相似偵測器(The MLD based on QR decomposition with M-algorithm，QRM-MLD )是一在效能上趨近最大相似偵測器的演算法，可在複雜度與效能間取得很好的平衡。然而，傳統的QRM-MLD在高訊雜比 (Signal to Noise Ratio，SNR)環境下的計算複雜度仍相當高。 因此本論文提出一瞬時選取後選節點的QRM-MLD演算法。先計算各候選節點的可靠度，以各節點的雜訊機率密度方程式，占所有節點的雜訊機率密度方程式總和的比例，評量可靠度。再以QRM-MLD的平均模擬符元錯誤率作為可靠度標準值，瞬時選取可靠度達標準的節點。此方法可趨近MLD效能，同時降低MLD大量的運算複雜度。除此之外，我們也分析通道排序對QRM-MLD演算法的影響，並採用於我們所提出的方法中。模擬結果顯示，我們所提出的方法在4×4 4QAM (Quadrature Amplitude Modulation)和16QAM MIMO系統中，分別降低46%和71%傳統的QRM-MLD的運算量，且達到與MLD近似之錯誤率。[[abstract]]The maximum likelihood detection (MLD) with QR decomposition and M-algorithm (QRM-MLD) is a near-optimal algorithm which can achieve a good tradeoff between error rate performance and complexity. However, the conventional QRM-MLD still requires high complexity in the high SNR (Signal to Noise Ratio) region. In this thesis, we propose an instantaneously-adaptive candidate selection scheme for the QRM-MLD algorithm in multiple-input multiple-output (MIMO) detection. First, in order to evaluate the reliability of each symbol candidate, we adopt a probability metric with normalized likelihood function of each symbol candidate in each detection layer. Then, the average simulated symbol error rate is used to be the error rate threshold. By means of decreasing the number of candidates with low reliability, a large amount of the computation for the path metric is avoided. Hence, the complexity of the proposed detection scheme can be reduced. Besides, we also analyze the effect of channel column norm ordering in QRM-MLD and adopt it into the proposed scheme. Simulation results show that the proposed scheme achieves the near-optimal error rate performance with significantly lower complexity. Our scheme reduces 46% to 71% computation for 4QAM (Quadrature Amplitude Modulation) and 16QAM in 4×4 MIMO systems as compared with the conventional QRM-MLD.[[note]]碩