在巨量多輸入多輸出（massive multiple-input multiple-output, M-MIMO）蜂巢式網路中，由於時頻資源分配有限且通道同調時間受限，因此可用的正交領航序列(pilot sequence，PC)數量也是有限的。這導致在相鄰細胞中重複使用PC，當其他細胞的用戶傳輸PC時，會對目標細胞基站的通道估計造成干擾，這種干擾被稱為領航序列污染（pilot contamination, PC）。頻率和PC的重複使用將導致通道估計性能下降，因此PC被認為是多細胞M-MIMO系統的性能限制因素。本論文旨在研究在PC環境下，引入一種更適用於實際環境的卡爾曼濾波器（Kalman filter, KF）並結合資料輔助（data-aided）通道估計方法來抵抗PC的策略，經由模擬結果可得本文所提出的方法能夠有效降低在在PC環境下通道估計的均方誤差(mean square error，MSE)。;In massive multiple-input multiple-output (M-MIMO) cellular networks, the availability of orthogonal pilot sequences (PS) is limited due to constraints in time-frequency resource allocation and channel coherence time. This leads to the reuse of PS among adjacent cells, causing interference in channel estimation at the target base station when PS transmissions from users in other cells occur, known as pilot contamination (PC). The reuse of frequency and PS results in degraded channel estimation performance, making PC a critical limiting factor in multi-cell M-MIMO systems. This paper aims to investigate a strategy to mitigate PC by introducing a Kalman Filter (KF) tailored for practical environments and integrating it with data-aided channel estimation methods. Simulation results demonstrate that the proposed approach effectively reduces the Mean Square Error (MSE) of channel estimation in PC environments.
