發(fā)布時(shí)間：2018-03-17 14:02

  本文選題：時(shí)域延續(xù)性　切入點(diǎn)：馬爾可夫轉(zhuǎn)移特性　出處：《華僑大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：認(rèn)知無線電是一種提高授權(quán)頻段頻譜利用率的新興技術(shù)。頻譜檢測(cè)是其關(guān)鍵任務(wù)之一。本文分析主用戶的行為特性,并研究基于該特性的頻譜檢測(cè)技術(shù)。本文首先介紹了認(rèn)知無線電的背景、概念和關(guān)鍵技術(shù),闡述了頻譜檢測(cè)技術(shù)。接著,對(duì)現(xiàn)有的幾種檢測(cè)技術(shù)進(jìn)行分析和比較。其中,能量檢測(cè)法因?yàn)榻Y(jié)構(gòu)簡(jiǎn)單、計(jì)算量小,得到廣泛使用。其次,本文分析了主用戶的時(shí)域延續(xù)性,介紹了兩時(shí)隙時(shí)域延續(xù)性模型,并研究基于該模型的快速能量檢測(cè)算法。該算法將前兩次能量檢測(cè)的檢測(cè)結(jié)果當(dāng)作主用戶在前兩個(gè)時(shí)隙的實(shí)際狀態(tài),然后基于時(shí)域延續(xù)性預(yù)判主用戶的當(dāng)前狀態(tài),從而減少檢測(cè)次數(shù),達(dá)到快速檢測(cè)的目的。其中,狀態(tài)預(yù)判可基于OR準(zhǔn)則或者AND準(zhǔn)則進(jìn)行,分別對(duì)應(yīng)OTPED算法和ATPED算法。本文在先驗(yàn)等概的條件下推導(dǎo)了這兩種算法的檢測(cè)精度,分析了它們的檢測(cè)次數(shù)。和傳統(tǒng)算法相比,上述算法可以在檢測(cè)精度近似的條件下減少17~20%的檢測(cè)次數(shù)。再次,本文介紹了主用戶的馬爾可夫轉(zhuǎn)移特性,并基于該特性預(yù)測(cè)主用戶的當(dāng)前狀態(tài),從而動(dòng)態(tài)調(diào)整判決門限,提高頻譜檢測(cè)精度。首先假定主用戶前一時(shí)隙的實(shí)際狀態(tài)已知,在恒虛警條件下,推導(dǎo)出檢測(cè)概率的理論上界。然后,為了解決主用戶實(shí)際狀態(tài)未知的問題,利用檢測(cè)結(jié)果近似實(shí)際狀態(tài),提出了馬爾可夫恒虛警能量檢測(cè)(MCFED)算法和改進(jìn)的MCFED(IMCFED)算法。MCFED算法在高信噪比情況下具有很高的檢測(cè)概率,但在低信噪比區(qū)域性能較差。IMCFED則始終優(yōu)于傳統(tǒng)的頻譜檢測(cè)算法。最后,為了兼顧虛警概率和漏檢概率的影響,本文研究利用主用戶馬爾可夫轉(zhuǎn)移特性減少貝葉斯代價(jià)。首先提出了馬爾可夫貝葉斯能量檢測(cè)(MBED)算法。其思想是將前一時(shí)隙的檢測(cè)結(jié)果看作主用戶的實(shí)際狀態(tài),并根據(jù)檢測(cè)結(jié)果選擇當(dāng)前時(shí)隙的判決門限。該算法計(jì)算量小,信噪比較高時(shí)性能較好。為了克服MBED算法在低信噪比條件下檢測(cè)精度較差的問題,本文又提出了改進(jìn)的MBED(IMBED)算法。該算法根據(jù)預(yù)測(cè)概率對(duì)判決門限進(jìn)行動(dòng)態(tài)調(diào)整,能夠進(jìn)一步減少貝葉斯代價(jià),且適用的信噪比區(qū)域更廣。
[Abstract]:Cognitive radio is a new technology to improve spectrum efficiency of authorized frequency band. Spectrum detection is one of its key Ren Wuzhi. Firstly, this paper introduces the background, concept and key technology of cognitive radio, and expounds the spectrum detection technology. Then, it analyzes and compares several existing detection techniques. The energy detection method is widely used because of its simple structure and low computational cost. Secondly, the time-domain continuity of the primary user is analyzed, and the time-domain continuity model of two time slots is introduced. The fast energy detection algorithm based on this model is studied, which regards the first two energy detection results as the actual state of the primary user in the first two time slots, and then prejudges the current state of the primary user based on the continuity of time domain. In order to reduce the number of times of detection and achieve the purpose of fast detection, the state prediction can be based on OR criterion or AND criterion, corresponding to OTPED algorithm and ATPED algorithm respectively. In this paper, the detection accuracy of these two algorithms is deduced under the condition of priori probability. The detection times of these algorithms are analyzed. Compared with the traditional algorithms, these algorithms can reduce the detection times by 17% or 20% under the condition of approximate detection accuracy. Thirdly, this paper introduces the Markov transfer characteristics of the primary users. Based on this characteristic, the current state of the primary user is predicted, and the decision threshold is dynamically adjusted to improve the accuracy of the spectrum detection. Firstly, the actual state of the previous slot of the primary user is assumed to be known, and under the condition of constant false alarm, The theoretical upper bound of detection probability is derived. Then, in order to solve the problem that the actual state of the primary user is unknown, the detection result is used to approximate the actual state. Markov constant false alarm energy detection (MCFED) and modified MCFED.MCFED have high detection probability in the case of high signal-to-noise ratio (SNR), but the performance of IMCFED in low signal-to-noise ratio (SNR) region is lower than that of the traditional spectrum detection algorithm. Finally, the MCFED algorithm is always superior to the traditional spectrum detection algorithm. In order to balance the influence of false alarm probability and missed detection probability, In this paper, we study how to reduce the Bayesian cost by using the Markov transfer characteristic of the primary user. Firstly, we propose the Markov Bayesian energy detection (MBED) algorithm. The idea is that the detection results of the previous time slot are regarded as the actual state of the host user. According to the detection results, the decision threshold of the current time slot is selected. The algorithm has less computation and better performance when the signal-to-noise ratio is high. In order to overcome the problem of poor detection accuracy of MBED algorithm under low SNR, In this paper, an improved MBED-IMBED-based algorithm is proposed, which dynamically adjusts the decision threshold according to the prediction probability, which can further reduce the Bayesian cost and has a wider range of applicable signal-to-noise ratio (SNR).
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN925

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