正交時頻間距(orthogonal time frequency space; OTFS)調變是一種新型的多載波調變技術,適用於具有稀疏延遲和都卜勒擴散性質的雙重選擇性通道。由於OTFS系統考慮的是線性時變(linear time-varying; LTV)多路徑通道,與以往多載波調變的靜態多路徑通道之性質不盡相同。因此,在OTFS系統中的初始時間同步(initial time synchronization; ITS),需要針對LTV多路徑通道的特性進行設計和優化,以確保能準確地估計出訊號在傳送過程中的偏移,使後續通道估計與解調等工作能夠順利進行。 本論文採用離散傅立葉轉換序列的梳狀前導波形,透過計算接收訊號和本地離散頻率偏移梳狀前導波形之間的相關性,提出一套適用於離散時間偏移上的基於梳狀ITS (comb-based ITS)方法,以順序識別和選擇最大識別來估計前導幀的起始時間點,進而實現OTFS系統的ITS。Comb-based ITS包含具有寬時間採集範圍(timing acquisition range; TAR)但存在資料自干擾(data self-interference; DSI)的粗略ITS (coarse ITS; CITS),以及可調整的窄TAR且無DSI的精細ITS (fine ITS; FITS)。 透過對提出的方法進行平均偏差和均方根誤差的性能分析,並檢視在CITS和FITS系統中的模擬結果,發現其估計準確度和穩定性在多種的通道環境中皆表現出色,特別是當通道存在直視路徑與都卜勒偏移較大時,有更為顯著的優勢。
Initial time synchronization (ITS) is investigated for orthogonal time frequency space systems operating over the linear time-varying multipath channels exhibiting real-valued delay and Doppler shifts on the delay-Doppler grid. Particularly, a comb-type preamble waveform carrying discrete-Fourier-transform sequence is designed to meet the constraint of zero discrete-ambiguity-function and thereby facilitate accurate ITS. By taking the cross-ambiguity measures between the received signal and local discrete-frequency-shifted comb-type preamble signals for discrete-time shifts sequentially, the comb-based ITS approaches using sequential identification and select-the-largest identification rules are developed to estimate the start time of a received preamble frame on the leading channel path. Comb-based coarse and fine ITS systems are investigated to operate over a wide timing acquisition range (TAR) under the disturbance of data self-interference (DSI) and over an adjustable narrow DSI-free TAR, respectively. When the channel exhibits a strong leading path and wide ranges of Doppler shifts, the comb-based ITS systems are shown to outperform the conventional ITS systems acquiring linear frequency modulated preamble waveforms and hybrid data/preamble frames, significantly in estimation robustness and in estimation accuracy for sufficiently high preamble signal-to-noise power ratios.