博碩士論文 92523005 完整後設資料紀錄

DC 欄位 語言
DC.contributor通訊工程學系zh_TW
DC.creator李彥賢zh_TW
DC.creatorYen-Hsien Leeen_US
dc.date.accessioned2005-7-7T07:39:07Z
dc.date.available2005-7-7T07:39:07Z
dc.date.issued2005
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=92523005
dc.contributor.department通訊工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract在高效率資料傳輸系統中,多輸入多輸出(MIMO)正交分頻多工系統(OFDM)是一個有效的資料傳輸技術。在這樣的系統,我們需要通道參數的資訊,以便解決資料多樣性連結、同調偵測和編解碼等問題。本論文中,我們使用調適性遞迴最小平方演算法(RLS)來估測通道,並且改進演算法的複雜度以增加使用上效能,我們還使用估測出的通道參數資訊來解調經由多餘循環檢查(CRC)編碼的資料,並用回授決策的方式更新通道參數。此外,我們對所提出的演算法做數學上的效能分析,並從中獲得在不同督普勒頻率(Doppler frequency)下的最佳的遺忘參數(forgetting factor),最後的模擬結果也驗證了所提出的方法的有較好的平均平方誤差(MSE)和位元錯誤率(BER)。zh_TW
dc.description.abstractMultiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is an effective technique for highly efficient data transmission. Channel parameters are required for diversity combing, coherent detection, and decoding. In this thesis, we propose an adaptive algorithm (RLS) for channel estimation in MIMO OFDM system and reduce its complexity to improve performance compared with the scheme in [1]. We employ the information of estimated channel parameters and demodulated data with CRC in the previous transmit block to update the channel information in a decision feedback fashion. Besides, we also obtain the optimum forgetting factor corresponding to different Doppler frequencies by analyzing the performance, and present the efficacy of the proposed scheme by simulation results.en_US
DC.subject決策回授zh_TW
DC.subject調適性通道估測zh_TW
DC.subject遞迴最小平方演算法zh_TW
DC.subject多輸入多輸出正交分頻多工系統zh_TW
DC.subjectadaptive channel estimaitonen_US
DC.subjectRLSen_US
DC.subjectdecision feedbacken_US
DC.subjectMIMO OFDMen_US
DC.title多天線正交分頻多工系統應用決策回授之調適性簡易通道估測zh_TW
dc.language.isozh-TWzh-TW
DC.titleAdaptive Simplified Channel Estimation for Multiple Antenna OFDM Systems with Decision Feedbacken_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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