在本論文中, 我們考慮在上鏈多使用者多輸入多輸出系統中, 為每個使用
者建置波束權重與通訊解調機制,進而提昇系統整體效能。傳統的處理方式先
在傳送端調整出適當波束權重, 再考慮接收端收到的解調機設計。這樣的處理
過程並非結合傳送與接收端的最佳設計方式, 故無法得到系統最佳化之效益。
因此, 結合式傳送端波束權重與接收端解調器之結合式最佳化設計不易以傳
統數學方式求解, 卻能有效提昇系統整體效能的重要課題。
為此, 我們引入基因演算法處理此系統結合之非線性最佳化問題, 這是因
為基因演算法已廣泛且有效地應用在許多非線性問題, 如: 圖形辨識、非線性
控制、無線通訊等。由模擬結果顯示, 基於基因演算法廣域式搜尋方式的優點
配合本文所提出的最佳化結合式設計方式, 確實能較傳統作法得到更佳的效
能。

In this thesis, We precent a joint scheme which combine the transmits beamforming
and multiuser symbol detection in uplink MIMO(Multi-Input Multi-Iutput)
systems. The joint decision statistic, however, is highly nonlinear and the conventional
linear schemes are not applicable. To alleviate the computation overhead
the we employ the genetic algorithm (GA) to solve the nonlinear optimization
involved. Due to the robustness of the GA, the joint decision statisic can be efficiently
solved and near optimum results can be obtained. Conducted simulation
show the new approach provides superior performance compaed with previous works.

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