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研究生:張政偉
研究生(外文):Cheng-Wei Chang
論文名稱:在感知無線電使用增強學習機制降低頻譜誤判率之研究
論文名稱(外文):The Study of Reducing False Spectrum Sensing Probability in Cognitive Radio Using Reinforcement Learning
指導教授:陳偉業陳偉業引用關係
指導教授(外文):Wei-Yeh Chen
學位類別:碩士
校院名稱:南台科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:49
中文關鍵詞:感知無線電增強學習機制誤判率
外文關鍵詞:Cognitive RadioQ-Learning
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近年來由於無線通訊技術的發展,無線網路的用戶也越來越多,頻譜的不足已成為了一個重要的問題。現今除了ISM等頻帶為非授權就能使用的頻帶外,大部分頻帶都需授權才能使用。研究發現,傳統的頻譜分配方式導致了頻譜利用率不高。為了解決頻譜利用率低的問題,感知無線電網路的發展是一項重要技術。
感知無線電是能有效率的提高頻譜利用率的智慧型通訊技術,它是以不干擾主要授權用戶傳輸為原則的跨系統資源分享網路,而快速且準確的頻譜偵測對於建立可靠的感知無線電是很重要的。因此,本篇論文旨在研究主要授權系統的頻譜利用率大小對系統效益的影響,而閒置頻譜利用率會受到頻譜偵測時的誤判率所影響。當次要使用者偵測通道時的誤判率越大,閒置頻譜利用率相對越小,反之,當誤判率越小,閒置頻譜利用率相對越大。本研究使用增強學習機制中的Q-Learning演算法之特點,藉著增強學習機制能不斷學習過去經驗的特性,來有效降低誤判率的發生,並提出修改之方法,稱為DQ-Learning。研究結果顯示,採用本論文所提出的方法可以有效的降低頻譜偵測時的誤判率並提高產出。
As the vigorous development of wireless communications technology in recent years, there are more and more users accessing the wireless network, causing the spectrum scarcity becoming a severe problem, especially when the high data throughout is required. Nowadays, only the ISM band is unlicensed, most other spectrum bands need licensed to use. The Federal Communications Commission(FCC) reports indicated that the traditional static spectrum allocation resulted in low spectrum utilization. To solve this problem, the development of cognitive radio is an important technology.
Cognitive radio is an intelligent communication technology which can enhance the efficiency of spectrum utilization by exploiting the unused resources of primary system, while not interfering with the transmission of primary users. Therefore fast and accurate spectrum sensing is very important in realizing a reliable cognitive radio. However, the error sensing of spectrum will result in low spectrum utilization. In the paper we proposed an reinforcement learning mechanism called DQ-Learning to reduce the false spectrum sensing probability. The simulation results showed that the proposed scheme can effectively reduce the false spectrum sensing probability, therefore increasing the successful channel access rate and system throughput.
摘要 ------------------------------------------------------------- II
英文摘要 --------------------------------------------------------- III
致謝 ------------------------------------------------------------- IV
目次 -------------------------------------------------------------- V
表目錄 ------------------------------------------------------------ VI
圖目錄 ----------------------------------------------------------- VII
第一章 緒論 -------------------------------------------------------- 3
1.1 前言 ----------------------------------------------------------- 3
1.2 研究動機與目的 -------------------------------------------------- 3
1.3 論文架構 ------------------------------------------------------- 5
第二章 文獻探討 ---------------------------------------------------- 6
2.1 感知無線電網路 -------------------------------------------------- 6
2.1.1 感知無線電標準 ------------------------------------------------ 8
2.1.2 系統描述及應用 ----------------------------------------------- 11
2.2 增強學習機制 --------------------------------------------------- 15
2.2.1 Q-Learning演算法 -------------------------------------------- 17
2.3 相關研究 ------------------------------------------------------ 19
第三章 研究方法 --------------------------------------------------- 21
3.1 問題概述 ------------------------------------------------------ 21
3.2使用增強學習機制降低通道誤判率 ------------------------------------ 22
第四章 模擬與效能分析 ---------------------------------------------- 28
第五章 結論與未來研究 ---------------------------------------------- 36
參考文獻 --------------------------------------------------------- 37
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