在無線通訊系統中，訊雜比估測是相當重要的技術。許多系統應用都需要訊雜比的資訊，來達到系統效能的最佳化。本論文提出了一套適用於晶格編碼系統的創新編碼輔助式訊雜比估測法。本估測法利用平均接收訊號能量，以及從維特比解碼程序中得到的最短路徑長度，來估測訊雜比。模擬結果顯示，相較於其他過去文獻提出的無資料輔助式訊雜比估測法，特別是在低訊雜比的環境下，本估測法能達到較佳的效能。此外，天線多工亦能增進本估測法的效能。
在本論文中，我們利用本估測法，使兩種無線通訊系統內的應用達到效能的最佳化。第一種應用，是耗能感知性時空晶格編碼多輸入多輸出檢測器。我們提出一套以T理論為基礎的狀態化簡機制，降低分支長度運算的高複雜度。內嵌的訊雜比估測器提供了必要的訊雜比資訊，作為決定在狀態化簡機制中，最佳化簡下限的依據。接著，本機制動態地降低運算複雜度，並保持編碼增益減損低於0.1dB。我們採用了TSMC 1P9M CMOS技術來實現本檢測器(4對4天線維度)。當晶片時脈為83.33MHz時，功率消耗為7.94mW至8.70mW(QPSK調變)、10.02mW至10.95mW(8PSK調變)、以及12.20mW至13.10mW(16QAM調變)。當訊雜比為20dB時，可達最高耗能降低幅度為13.45%至17.62%。
另一種應用，是以接收訊號強度為基礎的高精準度移動式定位系統。各個基地台利用接收訊號強度資訊估算訊號傳送距離，接著系統利用各個估算距離進行移動裝置的定位。本論文提出的估測法，能夠將接收訊號強度中的雜訊能量消除；比起其他文獻提出的無資料輔助式訊雜比估測法，本估測法能較為精準地消除雜訊能量。各基地台的粒子濾波器，利用已消除雜訊能量的接收訊號強度，進行距離估測。我們將估測距離與各基地台位置等資訊，整理成一個凹谷最佳化問題。最後，本系統實作了子梯度法解出本問題的最佳解，得到定位結果。模擬結果顯示，當訊雜比為0dB時，比起利用未消除雜訊能量的接收訊號強度進行定位運算，本估測法能將定位誤差降至1%。因此，利用了本估測法的接收訊號強度估測器，能增進整體定位系統的精確度。當移動式裝置的速率為60km/hr時，本系統的定位誤差為30m(SUI-1通道)、以及40m(SUI-3與SUI-5通道)。

The signal-to-noise (SNR) estimation is an essential technique in wireless communication systems, and many applications require the SNR as the prior information to optimize the system performance. This thesis presents a novel code-aided (CA) SNR estimation method for the trellis-coded systems. The proposed method estimates the SNR using the average symbol energy and the minimum path metric from the Viterbi decoding procedure. The simulation results indicate that the proposed method has better estimation performance than the other non-data-aided (NDA) SNR estimation methods in the literature, especially in low
SNR conditions. Besides, the antenna diversity improves the estimation performance of the proposed method.
In this thesis, we optimize the performance of two applications in wireless communication systems by using the proposed method. The first application is the power-aware space-time trellis code (STTC) multiple-input multiple-output (MIMO) detector. We presents a state-purging mechanism based on the T-algorithm to reduce the high complexity of branch metric calculations. The embedded SNR estimator using the proposed method provides the essential
SNR information to determine the optimal threshold for the state-purging mechanism. Then, the mechanism dynamically decreases computational complexity and only degrades coding
gains by less than 0.1dB. The power-aware STTC 4 times 4 MIMO detector is fabricated using 90nm 1P9M CMOS technology, and the power consumption with the clock rate of 83.33MHz is 7.94mW to 8.70mW for the QPSK modulation, 10.02mW to 10.95mW for the 8PSK modulation and 12.20mW to 13.10mW for the 16QAM modulation. The maximum power saving ranges from 13.45% to 17.62% when the SNR is 20dB.
The other application is the high-accuracy RSS-based mobile positioning system. Each base station estimates the distance based on the RSS information, and the system collect all the estimated distances to locate the mobile station. The proposed method cancels the noise energy from the raw RSS and has better improvement on noise cancellation than the other NDA SNR estimation methods. The particle filter of each base station determines the distance based on the estimated RSS. We formulate the convex optimization problem based on the estimated distances and the positions of base stations. Finally, the system implements the sub-gradient method to solve the problem and locates the mobile station. Simulation results
depict that the proposed method decreases the errors of the raw RSS estimation to 1% when the SNR is 0dB. The RSS estimator using the proposed method derives the RSS with noise cancellation and improves the localization performance of the positioning system. When the mobile station moves at the speed of 60km/hr, the localization errors of the proposed system are 30m in the SUI-1 channel and 40m in the SUI-3 and SUI-5 channels.

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