無線感測器網路下之結合通道估計與感測器容錯碼的設計

Abstract

無線感測器網路常設置在惡劣的環境中，因此感測器有可能會出現無預警的故障而導致接收端(fusion center)對環境判斷的準確度不穩定。針對此種情況，最直接的方式就是感測器先傳送訓練碼(training sequences)讓接收端去偵測是否有感測器發生故障，但此動作會增加感測器所消耗的能量，而導致由電池來供應電源的感測器運作壽命降低。

在此論文中，我們運用編碼的技巧來使無線感測器網路能同時擁有通道估計與感測器容錯的能力，我們將運用模擬退火法(simulation annealing)來尋找達到最小的錯誤率聯集上限(union bound)的最佳非線性碼。之後，我們採用量化器來對抗通道嚴重衰減(deep fading)的情況。模擬結果顯示，我們的方法可以在不考慮感測器故障、通道估計和等化的情況下，即可於效能上媲美於有傳送訓練碼的系統。

Wireless sensor networks are usually deployed in a harsh environment. Hence, the performance of the final decision at the fusion center is not stable because some of the sensors may be unexpectedly faulty. A straightforward technique to exclude untrustworth observations from faulty sensors at the fusion center is to detect them based on the pre-sent training sequences. However, the transmission of training sequences consumes additional energy, and shortens the life cycle of the battery-supported sensors.

In this thesis, we proposed to combine channel estimation and sensor fault protection in wireless sensor networks in terms of the coding technique. Simulation annealing is employed to search the best non-linear code that minimizes the upper error bound. Quantization is later added in order to eliminate the sudden performance degradation due to deep fading. Our results show that the proposed combined scheme can compete with the the conventional training-sequence-based fusion in performance but without the effort of faulty-sensor detection, channel estimation and equalization.