本文關(guān)鍵詞： 能量檢測算法 噪聲功率不確定性 主用戶狀態(tài)改變 最小采樣時間 數(shù)據(jù)碰撞概率　出處：《重慶郵電大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著無線通信業(yè)務(wù)和技術(shù)的快速增長，對無線頻譜的需求也日益增大，當前，無線頻譜的匱乏是阻礙無線通信發(fā)展的瓶頸之一。認知無線電（CR）通過伺機地接入授權(quán)的空閑頻譜以提高頻譜利用率，對緩解無線頻譜資源的緊缺具有重要意義。由于實現(xiàn)簡單和復雜度低的優(yōu)點，能量檢測算法已經(jīng)在空閑頻譜檢測中被廣泛應(yīng)用，但其檢測性能特別容易受到噪聲功率不確定性（NPU）、檢測期間用戶狀態(tài)改變和低信噪比（SNR）因素的影響。本文在這方面進行深入探討，主要研究內(nèi)容和成果如下： 針對能量檢測算法受到NPU的嚴重影響，本文給出了一種新的復雜度較低的NPU區(qū)間估計算法，并且從理論上分析了估計的噪聲功率對能量檢測算法信噪比墻（SNR WALL）惡化影響，得出了SNR WALL惡化性定理。進一步，基于門限修正提出了一種改進的能量檢測算法以消除SNR WALL惡化。仿真結(jié)果表明，本文算法能較為精確地估計NPU區(qū)間，并且驗證了SNR WALL惡化性定理的正確性；同時，改進的能量檢測算法性能要優(yōu)于RSA算法，降低了SNR WALL惡化，提高了檢測的魯棒性。 本文將能量檢測算法和經(jīng)典分布式認知MAC（DC-MAC）進行結(jié)合，通過跨層協(xié)作的方式分析得出了多個次用戶（SU）同時檢測同一空閑頻譜造成檢測結(jié)果不可靠。為在低SNR場景達到目標檢測概率，推導出了能量檢測算法的最小采樣時間（MST）�；贛ST，提出了一種改進的DC-MAC（ODC-MAC）協(xié)議。ODC-MAC整合了物理層頻譜感知策略和MAC層數(shù)據(jù)傳輸，通過跨層協(xié)作提高SU的數(shù)據(jù)傳輸可靠性。仿真表明，本文理論分析和實際仿真結(jié)果吻合；同時，相對于DC-MAC，ODC-MAC可以提高數(shù)據(jù)傳輸可靠性和吞吐量。 針對檢測期間主用戶（PU）的活動狀態(tài)變化和低SNR造成能量檢測算法檢測性能嚴重下降的現(xiàn)象，本文給出了一種加權(quán)（weight-p）能量檢測算法。為減少實現(xiàn)復雜性和節(jié)約需要的功耗，weight-p能量檢測算法的最優(yōu)權(quán)值建模成MST的優(yōu)化問題，分析得出了最優(yōu)權(quán)值和次優(yōu)權(quán)值。仿真表明，在PU狀態(tài)改變和低SNR的場景下，本文提出的weight-p能量檢測算法可以提高SU的檢測性能和降低虛警概率，，并且在獲得相同檢測性能的前提下可以壓縮檢測時間。 由于用戶（PU或者SU）隨機到達對頻譜檢測的性能有很大影響，為解決這個問題，本文提出了一種反饋疊加能量檢測方案。通過將檢測周期后半部分采樣點的瞬時能量值累加到檢測周期前半部分采樣點的瞬時能量上，在不延長檢測時間的基礎(chǔ)之上，提高整個檢測周期的能量統(tǒng)計值。仿真表明，反饋疊加能量檢測方案的檢測性能要優(yōu)于現(xiàn)有文獻的檢測方法，并且可以降低用戶之間數(shù)據(jù)發(fā)生碰撞的概率，從而提高SU的吞吐量。
[Abstract]:With the rapid growth of wireless communication services and technologies, the demand for wireless spectrum is also increasing. The lack of wireless spectrum is one of the bottlenecks to the development of wireless communication. It is of great significance to alleviate the shortage of wireless spectrum resources. Because of the advantages of simple implementation and low complexity, the energy detection algorithm has been widely used in idle spectrum detection. However, its detection performance is especially vulnerable to the influence of noise power uncertainty, user state change and low signal-to-noise ratio (SNR) factors during detection. In this paper, the main research contents and results are as follows:. In view of the serious influence of NPU on the energy detection algorithm, a new low complexity NPU interval estimation algorithm is presented in this paper, and the influence of the estimated noise power on the SNR wall deterioration of the energy detection algorithm is analyzed theoretically. The SNR WALL deterioration theorem is obtained. Furthermore, an improved energy detection algorithm based on threshold correction is proposed to eliminate the SNR WALL deterioration. The simulation results show that the proposed algorithm can estimate the NPU interval more accurately. At the same time, the performance of the improved energy detection algorithm is better than that of the RSA algorithm, which reduces the deterioration of SNR WALL and improves the robustness of the detection. In this paper, the energy detection algorithm is combined with the classical distributed cognitive MACU DC-MAC. By analyzing the way of cross-layer collaboration, it is concluded that multiple secondary users simultaneously detect the same idle spectrum, which results in unreliable detection results. In order to achieve target detection probability in low SNR scene, The minimum sampling time of the energy detection algorithm is derived. Based on MSTs, an improved DC-MAC ODC-MACMAC protocol. ODC-MAC integrates the spectrum sensing strategy of physical layer and the data transmission of MAC layer. The reliability of data transmission of Su is improved by cross-layer cooperation. The theoretical analysis is in agreement with the actual simulation results, and the reliability and throughput of data transmission can be improved compared with DC-MAC ODC-MAC. In view of the change of the active state of the primary user during the detection and the phenomenon that the detection performance of the energy detection algorithm is seriously reduced due to the low SNR, In this paper, a weighted weight-p energy detection algorithm is presented. In order to reduce the complexity and save the power consumption of weight-p energy detection algorithm, the optimal weight value is modeled as an optimization problem of MST, and the optimal weight value and the suboptimal weight value are obtained. In the scenario of pu state change and low SNR, the proposed weight-p energy detection algorithm can improve the detection performance of Su and reduce the false alarm probability, and the detection time can be compressed on the premise of the same detection performance. In order to solve this problem, the random arrival of users pu or SUU has a great impact on the performance of spectrum detection. In this paper, a feedback superposition energy detection scheme is proposed. By adding the instantaneous energy value of the second half of the detection cycle to the instantaneous energy of the first half of the detection cycle, the detection time is not extended. The simulation results show that the detection performance of the feedback superposition energy detection scheme is better than that of the existing literature, and the probability of data collision between users can be reduced. Thus, the throughput of Su is improved.
【學位授予單位】：重慶郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN925

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