電力線通訊 (power line communication, PLC) 系統透過現有的電力網路進行資訊傳輸，具有低成本部署的優勢，在智慧電網 (smart grid) 發展中扮演關鍵角色。然而，PLC 系統的性能易受多路徑衰減 (multipath fading) 與隨機脈衝雜訊 (impulsive noise, IN) 的影響，對訊號偵測與數據傳輸的準確性構成嚴重挑戰。因此，強健訊號偵測和IN抑制技術是確保電力線通訊系統可靠性的關鍵。
本論文針對PLC系統的訊號偵測與脈衝雜訊抑制兩大關鍵技術進行設計、分析與性能比較。首先，目前針對 IN 影響環境下前導碼 (preamble) 偵測的研究相對有限，本論文評估了兩種常見的訊號偵測方法：能量偵測及自相關偵測。研究結果顯示，在電力線通道環境中，相較於自相關偵測，能量偵測具有更短平均正確偵測時間及較低的複雜度。因此，能量偵測比自相關偵測更適用於電力線通道中的前導碼偵測處理，此結果顯著差異於傳統認知。
在系統性能方面，基於「千兆位元家庭網路」 (gigabit home networking, G.hn) PLC 系統標準規範並結合低密度奇偶校驗碼 (low-density parity-check code, LDPC)，本論文評估不同脈衝雜訊抑制及資料檢測設計的系統效能，其中包括空白 (blanking) 與 Mengi-Häring迭代方法，並探討其技術應用的延伸。此外，本論文分別採用二元假說 (binary hypothesis testing) 和內曼-皮爾森 (Neyman-Pearson test) 兩種檢定方法來計算上述脈衝雜訊抑制設計的門檻值重要參數。最後，本論文提出一種整合訊號偵測及通道編碼的結構化 (combined structure) 設計，其中 LDPC 解碼的似然值 (likelihood) 計算是根據脈衝雜訊抑制與等化器 (equalizer) 處理過程中訊號功率與雜訊功率的變化進行自適應調整。
綜合而言，本論文不單分析比較 PLC 系統中的前導碼偵測方法，還探討不同的脈衝雜訊抑制技術。藉由理論分析與模擬驗證，所得結果可提供未來智慧電網下PLC 系統設計的重要參考。

Power line communication (PLC) systems utilize existing power grids for data transmission, providing the advantage of low deployment costs and playing a crucial role in the development of smart grids. However, multipath fading and random impulsive noise (IN) heavily impact the performance of PLC systems, both serious challenges to accurate signal detection and data transmission. Thus, robust signal detection and IN suppression techniques are key to ensuring the reliability of power line communication systems.
This dissertation designs, analyzes, and compares the performance of two key technologies: signal detection and impulsive noise suppression in PLC systems. First, due to the relatively limited research on preamble detection performance in environments affected by IN, this dissertation evaluates two common signal detection methods: energy detection and autocorrelation detection. The research results show that, compared with autocorrelation detection, energy detection has a shorter mean time to correct detection and is less complex in power line channels. Hence, energy detection is more suitable for preamble detection processing in power line channels than autocorrelation detection, which is significantly different from conventional expectations.
In terms of system performance, this dissertation is based on the PLC system defined by the Gigabit Home Networking (G.hn) standard and incorporates low-density parity-check (LDPC) codes. It evaluates various impulsive noise suppression and data detection schemes, including blanking and Mengi-Häring iteration schemes, and explores their potential extensions for practical implementation. Moreover, this dissertation uses two statistical testing methods, the binary hypothesis test and the Neyman–Pearson test, to calculate threshold values for important parameters of the impulsive noise suppression design mentioned above. Finally, this dissertation proposes a combined structure design that integrates signal detection and channel coding, in which the likelihood values are computed in LDPC decoding and are adaptively adjusted according to the variations in signal and noise power resulting from the IN mitigation and equalization processes.
In summary, this dissertation not only analyzes and compares the preamble detection methods, but also explores different impulsive noise suppression techniques in PLC systems. Through theoretical analysis and simulation verification, the results obtained can provide an important reference for the design of PLC systems in future smart grids.

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