頻譜感測是感知無線電網路中非常重要的一個功能。透過頻譜感測，使得網路中的次要使用者可以去感測主要使用者是否有在使用頻帶。一般而言，次要使用者並沒有被授權使用該頻帶，但在感知無線網路中，當主要使用者沒有在使用該頻段時，我們允許使用該頻帶以提升頻帶的使用率。一般而言，頻譜感測被要求在沒有主要使用者的資訊下也可以運作，許多研究提出了頻譜感測的方法，各有其利與弊。能量感測器可以在非常小運算量達到很好的效果，然而，前提是要知道環境中雜訊的能量，否則對它的偵測率會有很大的影響。最大最小特徵值方法雖然不用知道雜訊的能量，但是運算量卻較為龐大。
最大最小特徵值方法主要是利用收到訊號的協方差矩陣中最大以及最小特徵值的比例來做偵測，偵測率的好壞與主要使用者端傳出訊號的相關性有很大的關係。因此我們認為運用瑞雷衰弱通道的相關特性對頻譜感測可以有很大的幫助。不僅模擬結果顯示利用相關特性的方法可以表現得比能量感測器以及最大最小特徵值方法來得好。我們同時也從數學上進行了推導，對整體偵測率的分析有相當大的幫助。

Spectrum sensing is a key function in the cognitive radio networks. It allows cognitive users to check the existence of the primary user in the frequency radio and use the channel if the channel is vacant to achieve high spectrum usage efficiency. Blind spectrum sensing is widely discussed because it does not need prior knowledge of the primary user. Many researchers have proposed different blind sensing algorithms and each has its advantages and disadvantage. For example, the energy detector performs well with low complexity but has the problem of noise uncertainty, and the maximum-to-minimum eigenvalue method does not need to know the noise power but with high complexity.
Inspired by the maximum-to-minimum eigenvalue method, we proposed a correlation-based method exploiting the channel correlation under Rayleigh fading channel. Simulation shows the improvement of the correlation-based method toward energy detector and the maximum-minimum eigenvalue method. Analytical expressions are also derived.

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