本文選題：超寬帶信道估計 + 壓縮感知��； 參考：《長沙理工大學》2014年碩士論文

【摘要】：超寬帶是擁有許多優(yōu)點的通信方式,加之近幾年出現的壓縮感知理論,為超寬帶的發(fā)展提供了契機,壓縮感知技術可以應用在對超寬帶的信道估計上,這有助于提高超寬帶通信系統的通信質量和性能,人們對此廣泛關注。本文將該技術應用于超寬帶信道估計中,具有積極的貢獻和意義。本文以壓縮感知理論為基礎,并利用超寬帶信道的稀疏特性,對信道參數進行估計,完成理論分析和實驗數據仿真,實現了超寬帶信道準確的估計。該研究對構造更高效、實用的算法具有突出的研究指導與現實意義。主要工作如下：1、利用混沌序列良好的隨機性提出了一種基于Logistic混沌序列的超寬帶信道估計方法,對貝葉斯壓縮感知進行了數學建模。理論分析和計算機仿真結果表明,在相同的實驗條件下,本文方法相比傳統的重構算法,具有更好的抗噪聲性能和重構精度,并與其它類型的測量矩陣進行比較,在低信噪比和測量次數的數值分析結果證明本算法是可行有效的,且相對于隨機矩陣,本文矩陣更易于實現,信道估計值更加穩(wěn)定。2、利用快速RVM來優(yōu)化分布式貝葉斯算法,解決傳統單任務貝葉斯壓縮感知算法在多用戶超寬帶系統中的不足。本文通過建立基于快速RⅧ的分布式貝葉斯壓縮感知模型,利用多用戶信號間的統計相關性,對接收信號進行聯合重構仿真結果表明,該算法能有效的減少多用戶超寬帶信道估計的測量次數。此外,本文對測量矩陣在基于壓縮感知的超寬帶信道估計系統模型中的性能分析,為今后的研究者們提供了新的思考方向。
[Abstract]:Ultra-wideband (UWB) is a communication mode with many advantages. In addition, the theory of compressed sensing, which has emerged in recent years, provides an opportunity for the development of UWB. Compression sensing technology can be applied to the channel estimation of UWB. This is helpful to improve the communication quality and performance of UWB communication system. This paper applies this technique to UWB channel estimation, which has positive contribution and significance. Based on the theory of compression sensing and using the sparse characteristic of UWB channel, the channel parameters are estimated, and the theoretical analysis and experimental data simulation are completed to realize the accurate estimation of UWB channel. This research has the outstanding research instruction and the realistic significance to the construction more efficient, the practical algorithm. The main work is as follows: 1. By using the good randomness of chaotic sequences, a novel channel estimation method based on Logistic chaotic sequences is proposed, and the mathematical model of Bayesian compression sensing is presented. Theoretical analysis and computer simulation results show that, under the same experimental conditions, the proposed method has better anti-noise performance and better reconstruction accuracy than the traditional reconstruction algorithm, and is compared with other types of measurement matrix. The numerical results of low SNR and measurement times show that the proposed algorithm is feasible and effective. Compared with the random matrix, the proposed algorithm is easier to implement and the channel estimation value is more stable. The fast RVM is used to optimize the distributed Bayesian algorithm. To solve the problem of traditional single-task Bayesian compression sensing algorithm in multi-user UWB systems. In this paper, a distributed Bayesian compression perception model based on fast R 鈪，

本文編號：1900005