於分頻雙工大規模多天線系統中基於區塊稀疏特性之壓縮下行通道狀態資訊還原

Abstract

於本研究中，吾人探討於分頻雙工大規模多天線系統中下行通道資訊估計問題。吾人首先分析此環境下之通道矩陣於角度維度中具區塊稀疏特性，並利用實際衰落環境中用戶間享共同散射路徑之特性，提出一基於壓縮感知(compressive sensing; CS)之二階段式演算法，其不僅可萃取出共同及個別路徑資訊，更經由權重設計而充分利用此資訊以估計通道。此外，吾人基於區塊有限等距性質(block restricted isometry property)提出此演算法之效能保證與不同權重下之估計誤差上界，基於此結果吾人更進一步提出此演算法效能優於不採用權重設計者效能之充份條件。最終，電腦模擬結果顯示吾人提出之通道估計演算法相較於現有的貪婪式演算法，能較精確地估計通道。

This thesis proposes a new compressive sensing (CS) based downlink channel state information (CSI) estimation scheme for FDD massive MIMO systems. The proposed approach, which is a two-stage weighted block -minimization algorithm, exploits the block sparse nature of the angular-domain representation of the MIMO channel matrices, as well as the existence of common scattering paths in the realistic propagation environment. Analytic performance guarantees of the proposed method are specified in terms of the block restricted isometry property of the sensing matrix. Specifically, the -norm reconstruction error upper bounds of the proposed approach under various weighting schemes are derived. Our analytic results leads to further propositions on the condition under which the proposed algorithm outperforms the un-weighted counterpart. Computer simulations show that our method achieves better estimation accuracy as compared to an existing greedy-based solution.