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研究生:謝馨儀
研究生(外文):Hsing-yi Hsieh
論文名稱:應用於感知無線電之序列式高階統計量頻譜偵測
論文名稱(外文):Higher-Order Statistics Based Sequential Spectrum Sensing for Cognitive Radio
指導教授:古孟霖林嘉慶林嘉慶引用關係
指導教授(外文):Meng-Lin KuJia-Chin Lin
學位類別:碩士
校院名稱:國立中央大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:56
中文關鍵詞:序列式機率比高階統計量頻譜偵測感知無線電
外文關鍵詞:cognitive radiospectrum sensingcumulantsequential probability ratio test
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隨著行動與無線通訊技術與日俱進的快速發展,應用於不同範圍的無線通訊標準
也如雨後春筍萌生。然而隨著數位電視的崛起,類比電視大部份的頻帶使用率並
不高。
綠能科技的提出,使得感知無線電成為下一世代有效增進頻譜效能的通訊技術。
頻譜偵測為感知無線電的其中一個重要議題,感知使用者偵測主要使用者是否有
傳輸資料後,擷取並用來傳輸資料。藉由感知無線電和合作式通信兩種技術來達
成高可靠、高品質及高效率之通訊服務,來克服傳輸通道衰落並提升機會傳輸量;
兩者的結合將會對未來的無線行動通信系統帶來重大影響。目前的文獻大多利用
能量檢測器當作頻譜偵測的方法,但在低訊雜比時,辨識率並不高。因此本研究
基於高階統計量為基礎設計一套檢測器,可有效消除高斯白雜訊在低訊雜比的影
響;此外,高階統計量所需的複雜度較高,因此我們利用序列式檢測法降低偵測
時間。為了抵抗遮蔽效應,我們利用合作式通訊增加偵測可靠度。本演算法的優點不僅能應用於大多通訊系統,也能有效抵抗雜訊及衰落通道。
In cognitive radio, spectrum sensing is a key enabling functionality to identify the
vacant spectrum which is not occupied by primary systems. With good sensing capability,
secondary users can effectively recycling the spectrum resource without
disturbing active primary users. Energy detectors are commonly used and relatively
simple spectrum sensing techniques. However, for low signal-to-noise ratio
(SNR) regimes, the performance of energy detectors degrades dramatically as the
signal and noise could be mixed together after the operation of energy calculation.
In addition, the outputs of the energy detectors are often assumed as Gaussian
distribution, which is not necessarily guaranteed in realistic cases. In this paper,
a high-order statistics (HOS) based sequential test detector is investigated for
sensing spectrum, particularly for low-SNR applications. We resort to high-order
statistics, in terms of cumulant statistics, for overwhelming the Gaussian noise effect
and improving the spectrum sensing reliability. Based on these cumulants, a
binary hypothesis testing problem is formulated and a low-complexity sequential
probability ratio test (SPRT) is developed for efficiently detecting underutilized
spectrum. Our numerical results show that the proposed detector outperforms
more than 10dB detection probability than the conventional energy detectors.
1 Overview of Cognitive Radio 1
1.1 Evolution of Cognitive Radio . . . . . . . . . . . . . . . . . . . . 1
1.2 Organization of CR . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Spectrum Sensing Techniques . . . . . . . . . . . . . . . . . . . 6
1.3.1 Blind Sensing . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Signal Specific Sensing . . . . . . . . . . . . . . . . . . . 9
1.3.3 Cooperative Sensing . . . . . . . . . . . . . . . . . . . . 9
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Detection Problem 12
2.1 Important pdf . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.1 Gaussian . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.2 Chi-square . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Bayes Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Neyman-Pearson Theorem . . . . . . . . . . . . . . . . . . . . . 16
2.4 Test Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.1 Fixed Sample Size Test . . . . . . . . . . . . . . . . . . . 18
2.4.2 Variable Sample Size Test . . . . . . . . . . . . . . . . . 19
3 Higher-Order Statistics 24
3.1 Higher-order Statistical Analysis . . . . . . . . . . . . . . . . . . 24
3.1.1 Time-Frequency Representations . . . . . . . . . . . . . 25
3.2 Definition of Moment . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.1 Properties of MGF . . . . . . . . . . . . . . . . . . . . . 26
3.3 Definition of CGF . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.1 Properties of CGF . . . . . . . . . . . . . . . . . . . . . 28
3.3.2 Sample Estimate of Cumulant . . . . . . . . . . . . . . . 29
3.3.3 Properties of Cumulant . . . . . . . . . . . . . . . . . . . 30
3.3.4 Some Important Cumulants . . . . . . . . . . . . . . . . 30
4 Algorithm 32
4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2.1 Trade-off of Complexity and Performance . . . . . . . . . 38
4.3 Cooperative Sensing . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Simulation Results 41
5.1 Energy Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.2 HOS+FSS Detector . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.3 HSS Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
6 Conclusion 52
Bibliography 52
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