正交時頻空間（orthogonal time frequency space, OTFS）系統是近期提出的一種二維調變技術，目的在解決高都卜勒敏感度問題。其概念是在延遲-都卜勒域（delay-Doppler domain）進行調變，將延遲-都卜勒域的訊息符元轉換為時頻域，並在時間和頻率上同時擴展使得所有訊號可同時獲得時間分集和頻率分集的效果，進而在錯誤率性能上獲得大幅度的改善，以實現最大的有效多樣性。因此，很適合用於多重路徑干擾和移動性而導致的雙選擇性通道。因此，相較於傳統的正交分頻多工（orthogonal frequency-division multiplexing, OFDM），正交時頻空間系統更適合應用在具有大都卜勒頻移的高移動性通訊場景中。文獻中兩階段偵測演算法之第一階段為在時頻域假設理想脈衝整形波形，使用一階最小均方誤差等化器求得估計符元，再透過第二階段於時延域使用迭代最大比例合成決策回授等化器改進第一階段估計符元的錯誤率。本論文針對第一階段與第二階段都提出新的方法，在第一階段等化器提出在時延域並基於矩形脈衝整形波形的狀況下使用最小均方誤差為基礎的干擾分集最小均方誤差等化器、區塊式最小均方誤差等化器兩個方法。
文獻中第二階段的最大比例合成方法推導後會等同於強制歸零(zero forcing)方法，因此我們在第二階段提出最小均方誤差合成(minimum mean square error combining)方法加以改進。結果顯示，第二階段提出來的時延域最小均方誤差迭代回授等化器可以在犧牲少許複雜度的代價下，在extended vehicular A model (EVA)、extended pedestrian A model (EPA)、extended typical urban model (ETU)這三個通道模型下皆呈現出比文獻中的方法更好的錯誤率表現。第一階段提出的干擾分集最小均方誤差等化器配合第二階段提出來的時延域最小均方誤差決策回授等化器在EVA通道模型下有最佳的錯誤率表現；跟另一個第一階段提出的區塊式最小均方誤差等化器也一同在EPA通道模型下能以更低的複雜度達到相同的錯誤率；但在ETU通道模型，仍舊是原本文獻中的低複雜度時頻域一階最小均方誤差等化器表現較優。

Orthogonal time frequency space (OTFS) is a recently proposed two-dimensional modulation technique aimed at addressing high Doppler sensitivity issues. In the literature, a two-stage detection algorithm is proposed. In the first stage, an ideal pulse shaping waveform is assumed in the time-frequency domain, using a single tap minimum mean square error (MMSE) equalizer to obtain the estimated symbols. In the second stage, an iterative maximum ratio combining decision feedback equalizer is used in the delay-time domain. This thesis proposes new methods for both stages. For the first-stage equalizer, two methods are proposed: an interference diversity MMSE equalizer and a block MMSE equalizer, both in the delay-time domain and based on rectangular pulse shaping waveforms. The maximum ratio combining method in the second stage is equivalent to the zero forcing (ZF) method. Therefore, we propose an improvement by using the minimum mean square error combining (MMSE combining) method in the second stage. Results show that the proposed second-stage iterative MMSE decision feedback equalizer achieves better error rate performance than existing methods, with slightly increased complexity, in the extended vehicular A (EVA), extended pedestrian A (EPA), and extended typical urban (ETU) channel models. The interference diversity MMSE equalizer in the first stage, combined with the second-stage delay-time domain MMSE decision feedback equalizer, achieves the best error rate performance in the EVA channel model. The block MMSE equalizer in the first stage achieves similar error rates with lower complexity in the EPA model. However, in the ETU model, the original low-complexity first-order MMSE equalizer still performs best.

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