以能量偵測演算法偵測隨機到達和離開之主要使用者訊號

Abstract

本論文針對頻譜感知中的能量偵測技術，考慮當主要使用者訊號隨機到達和離開的情況，推導精確的偵測機率，並提出以貝氏原則的能量偵測法，藉以提升偵測效能。另一部分，考慮當主要使用者訊號在感知時槽中較晚時間點到達的情況，提出一個具權重之能量偵測演算法，並推導精確的偵測機率。模擬結果可以證實相較傳統的能量偵測法，本論文提出的方法能大幅提升能量偵測法的效能，模擬結果也驗證模擬值與本論文推導的理論值相符。

Energy detection (ED) is a popular spectrum sensing technique in cognitive radios. In this thesis, we consider the effect of random arrival and departure of primary signals and derive the exact detection probability for ED. We also propose a Bayesian based ED to improve the performance. We then consider the scenario with late arrival of primary signals, and propose a weighted ED to enhance the detection performance. Simulation results are used to validate our theoretical study and to illustrate the performance of the proposed schemes.