以基於能量偵測法則的頻譜偵測演算法偵測抵達時間未知的主要使用者訊號

Abstract

在下世代的無線感知系統中，次要使用者 (Secondary user) 進行頻譜偵測 (Spectrum sensing) 時會遇到與主要使用者 (Primary user) 時間不同步的情形。因此，本論文假設主要使用者存取頻帶的時間為一均勻分布的隨機變數，進而分析能量偵測器 (Energy detector) 在此環境設定下的效能。其中，本論文推導出準確偵測機率 (Detection probability) 的公式並藉由電腦模擬驗證之。為了進一步提升系統偵測效能，本論文提出一個基於貝氏 (Bayesian) 原則的偵測演算法。此外，當主要使用者存取頻帶的時間被視為一個不變的未知數，本論文則提出另一個以廣義概似比例檢定(Generalized likelihood ratio test, GLRT) 為基礎的偵測法則，藉以改善系統偵測效能。電腦模擬的結果證實本論文所提出的兩種偵測演算法皆能有效提升系統偵測效能。

Spectrum sensing in next-generation wireless cognitive systems, such as overlay femtocell net-works, is typically subject to timing misalignment between the primary transmitter and the secondary receiver. In this thesis we investigate the performance of the energy detector (ED) when the arrival time of the primary signal is modeled as a uniform random variable over the observation interval. The exact formula for the detection probability is derived and corroborated via numerical simulation. To further improve the detection performance, we propose a robust ED based on the Bayesian principle. In addition, when the primary signal arrival time is unknown but fixed, we propose another detection rule based on the generalized-likelihood ratio test (GLRT) to improve the detection performance. Computer simulation confirms the effectiveness of the Bayesian based and the GLRT based solution when compared with the traditional ED.