【摘要】：隨著移動互聯(lián)網(wǎng)與智能移動終端的高速發(fā)展,頻譜供需矛盾日漸突出,成為了制約無線通信技術(shù)發(fā)展的新瓶頸。然而,根據(jù)FCC報告指出,當前的平均頻譜利用率僅為15%～85%,頻譜資源浪費嚴重。認知無線電技術(shù)通過頻譜感知利用頻譜空洞進行通信,提高頻譜利用率,成為了解決頻譜資源短缺的關(guān)鍵技術(shù)之一。頻譜感知是認知無線電技術(shù)實現(xiàn)的前提,因此,如何實現(xiàn)頻譜狀態(tài)的快速感知已成為認知無線電研究的重點問題。本文首先以減小檢測時延、提高系統(tǒng)吞吐率為目標,在認知無線電中引入隨機線性網(wǎng)絡(luò)編碼,對基于隨機線性網(wǎng)絡(luò)編碼的快速頻譜檢測算法進行了研究。其次,為了減小檢測時延的影響、降低虛警概率,本文利用隱馬爾可夫模型對主用戶信道進行建模,深入研究了基于隨機線性網(wǎng)絡(luò)編碼的頻譜預(yù)測算法。本文的主要工作和創(chuàng)新點總結(jié)如下：1、針對傳統(tǒng)認知無線電網(wǎng)絡(luò)中頻譜狀態(tài)轉(zhuǎn)換頻繁和頻譜檢測時延過長的問題,提出基于隨機線性網(wǎng)絡(luò)編碼的累積和能量檢測算法(RLNC-CUSUM)。該算法在主用戶信道中引入隨機線性網(wǎng)絡(luò)編碼,利用隨機線性網(wǎng)絡(luò)編碼對頻譜狀態(tài)的整形作用,使頻譜狀態(tài)轉(zhuǎn)換稀疏、頻譜結(jié)構(gòu)更規(guī)律化,進而減小頻譜檢測時延、提高系統(tǒng)吞吐率。針對傳統(tǒng)的累積和能量檢測算法(CUSUM)抗衰落性能差的問題,本文對五種衰落信道進行建模分析,并比較RLNC-CUSUM算法在不同衰落信道下的檢測性能,進而驗證該算法良好的抗衰落能力。實驗結(jié)果表明,在一定的虛警概率下,該算法能夠有效減小檢測時延,提高吞吐率及抗衰落能力,能夠更好地適應(yīng)復(fù)雜的衰落信道環(huán)境。2、基于在認知無線電網(wǎng)絡(luò)中引入隨機線性網(wǎng)絡(luò)編碼帶來的頻譜預(yù)測性和隱馬爾可夫模型對頻譜的預(yù)測作用,提出基于隨機線性網(wǎng)絡(luò)編碼的退避頻譜預(yù)測算法(Back-off-SP)。該算法利用隱馬爾可夫模型對主用戶信道進行建模分析,引入退避預(yù)測方法,并對傳統(tǒng)的基于隱馬爾可夫模型的頻譜預(yù)測算法進行改進,以提高頻譜感知性能。通過實驗仿真驗證,該算法能夠有效提高頻譜預(yù)測概率,降低虛警概率,進而降低次用戶對主用戶的干擾,提高次用戶吞吐率。
[Abstract]:With the rapid development of mobile Internet and intelligent mobile terminal, the contradiction of spectrum supply and demand is becoming more and more prominent, which has become a new bottleneck restricting the development of wireless communication technology. However, according to the FCC report, the current average spectrum efficiency is only 15% 85%, and the waste of spectrum resources is serious. Cognitive radio technology is one of the key technologies to solve the shortage of spectrum resources. Spectrum sensing is the premise of cognitive radio technology. Therefore, how to realize the fast sensing of spectrum state has become an important issue in cognitive radio research. In order to reduce detection delay and improve system throughput, this paper introduces stochastic linear network coding into cognitive radio, and studies a fast spectrum detection algorithm based on stochastic linear network coding. Secondly, in order to reduce the influence of detection delay and the probability of false alarm, this paper uses hidden Markov model to model the primary user channel, and deeply studies the spectrum prediction algorithm based on stochastic linear network coding. The main work and innovations of this paper are summarized as follows: 1. Aiming at the problems of frequent spectrum state transition and long spectrum detection delay in traditional cognitive radio networks, a cumulative and energy detection algorithm based on stochastic linear network coding (RLNC-CUSUM) is proposed. In this algorithm, random linear network coding is introduced into primary user channel, and the shaping effect of random linear network coding on spectrum state is used to make spectrum state conversion sparse, spectrum structure more regular, and spectrum detection delay reduced. Improve system throughput. Aiming at the problem of poor anti-fading performance of traditional cumulative and energy detection algorithm (CUSUM), five fading channels are modeled and analyzed in this paper, and the detection performance of RLNC-CUSUM algorithm in different fading channels is compared to verify the good anti-fading performance of the algorithm. The experimental results show that the proposed algorithm can effectively reduce the detection delay and improve the throughput and anti-fading ability under a certain false alarm probability. It can better adapt to the complex fading channel environment. It is based on the prediction of spectrum caused by the introduction of stochastic linear network coding in cognitive radio networks and the effect of hidden Markov model on spectrum prediction. A Backoff Spectrum Prediction algorithm (Back-off-SP) based on stochastic linear network coding is proposed. The algorithm uses hidden Markov model to model and analyze the primary user channel, introduces Backoff prediction method, and improves the traditional spectrum prediction algorithm based on hidden Markov model to improve spectrum sensing performance. The experimental results show that the proposed algorithm can effectively improve the spectrum prediction probability, reduce the false alarm probability, and then reduce the secondary user's interference to the primary user and improve the secondary user's throughput.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN925

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