應用於相關性多輸入多輸出系統之通道建模、估測及前置編碼

Abstract

有別於傳統單一天線系統，多輸入多輸出(MIMO)技術由於能大幅提升通道傳輸容量，因此已被納入目前許多重要的無線通訊標準之中。藉由在傳輸端與接收端設置多根天線，我們可以透過分解通道矩陣來創造許多平行通道並用以同時傳輸多個資料流。為了充份發揮多天線系統的優勢，特別是要進行高速資料傳輸時，精準的通道狀態資訊通常是不可獲缺的。然而，隨著天線個數的增長，估測及處理龐大通道矩陣的工作顯得益發困難。在本論文中，我們提出一種簡潔有效的通道矩陣表示法，藉由此表示法我們可以減少在描述通道矩陣時所需的參數數量，同時也減輕後續信號的運算複雜度。在中至高度相關的多天線傳輸環境下，使用本文所提出的通道表示法將可大幅減少通道狀態資訊的參數個數，同時維持良好的資訊品質。

基於所提之通道描述，我們發展了一種遞迴最小平方法來估測幾種典型的多輸出入通道。所得到的通道估測值呈現一個緊緻的形式，該形式將有助於簡化許多需要利用通道矩陣估測值進行的後置信號處理程序。此外，藉由調整通道估測器中的一個模型階數，我們可以在演算法的估測準確性和計算複雜度之間取得平衡。值得一提的是，由於估測器中維度縮減特性所帶來額外的雜訊消除效果，我們將可得到比傳統最小平方估測法更優越的均方誤差表現。我們對所提的通道估測器之相關性能也作了理論分析並就不同通道環境進行數值模擬用以評估該通道估測器的效能並證明理論的正確性。

為了能充份發揮所提通道模式及估測器所帶來的好處，我們更進一步利用該模式發展新型的回饋前置編碼系統。由於在設計前置編碼器時引入前述的通道模式，我們可大幅減少回饋前置編碼系統所需的回授頻寬，並降低建構前置編碼器及後置等化器的計算複雜度。相較於傳統上使用完整即時通道資訊的前置編碼系統，我們的系統只在非常高訊號雜音比時造成極輕微的性能損失，然而其在縮減回授頻寬和簡化計算複雜度上所帶來的好處卻是相對可觀。為了進一步評估系統因為模型通道模式的誤差所帶來可能的效能損失，我們在數學上推導了數個效能上界，用以評估訊號接收的均方誤差及回授訊號的資訊品質。同樣地，我們也提供了相關的數值結果用以驗証系統效能並証實所推導的效能上界的確可以準確預測系統的效能趨勢。

Multiple-input multiple-output (MIMO) technology has been included in many industrial standards to achieve significant throughput enhancement compared with conventional single antenna systems. By using multi-element antennas at both transmit and receive sides, multiple data streams can be transmitted simultaneously through parallel spatial modes. To realize the advantages of MIMO systems, accurate channel state information (CSI) is indispensable, especially for high rate transmissions. With the increase of antenna number, the task of estimating or processing a MIMO channel matrix becomes more and more difficult. In this thesis, we propose an efficient channel representation such that the number of required parameters is reduced and the computation complexity can be lessened as well. For medially to highly correlated MIMO environments, the proposed representation can lead to significant parametric dimension reduction while maintaining good CSI quality.

Based on the proposed channel representation, we develop iterative least squared (LS) schemes to estimate several typical MIMO channels. The reduced-rank CSI representation is very useful for many post-channel-estimation operations that require processing the instantaneous channel matrices. Depending on the specified modelling order, the proposed channel estimators offer tradeoff between identification accuracy and computational complexity. Moreover, the dimension-reduction induced noise rejection effect enables the proposed model-based estimators to achieve superior mean squared error (MSE) performance over certain SNR region when compared with that of the conventional LS approach. Theoretical analysis and numerical simulations of MSE performance are provided to assess the estimators’ performance and validate the analytical predictions.

Taking advantage of the proposed compact CSI representation, we proceed to develop a model-based feedback precoded system. By incorporating our new channel representation into the precoder design, the resulting precoded system provides significant reductions on the feedback bandwidth and the computational complexity needed for constructing the precoder and equalizer matrices. Numerical results show that compared with the conventional approaches that need full knowledge of instantaneous CSI, our proposal suffers only negligible performance degradation at very high SNR region. The reductions on computing complexity and feedback channel bandwidth, nevertheless, are significant. To assess the performance of our model-based approach, we establish several bounds regarding the reception error and feedback information loss. Simulated results are compared with these analytical bounds to verify that performance trends can indeed be accurate predicted.