基于Wishart隨機矩陣的發(fā)射天線數(shù)目估計算法研究
發(fā)布時間：2018-10-19 17:45
【摘要】：在非合作MIMO通信場景中,通信信號參數(shù)盲識別作為信號檢測和信號空時解碼解調的中間步驟,具有著十分關鍵的作用,已經在頻譜管理與監(jiān)測、軟件無線電和軍事無線通信對抗等民用和軍事領域得到了極為廣泛的應用。而準確的MIMO發(fā)射天線數(shù)目估計則是MIMO通信信號參數(shù)盲識別的關鍵問題之一,能夠為MIMO信道盲估計、MIMO空時碼盲識別和MIMO信號調制方式盲識別等其他關鍵問題的高效解決提供先驗知識,具有著十分重要的研究意義和研究價值,已經開始得到無線通信領域中眾多學者的廣泛關注和研究。本文在前人工作的基礎上對MIMO發(fā)射天線數(shù)目估計問題進行了探索和研究,具體主要工作內容包括:(1)簡略介紹了MIMO系統(tǒng)以及隨機矩陣理論等基礎理論知識;詳細闡述了AIC、MDL、PET以及IWME這四種經典MIMO發(fā)射天線數(shù)目估計算法的數(shù)學理論基礎和推導過程,同時,根據上述四種算法的計算機仿真結果,分析了算法正確估計概率與信噪比、采樣數(shù)據長度以及接收天線數(shù)目等仿真參數(shù)的關系。(2)提出了一種基于隨機矩陣理論的預測特征值上限算法(RPET),與基于經典多元統(tǒng)計理論的PET算法相比,RPET算法能夠明顯提升低信噪比和采樣數(shù)據長度較小條件下的正確估計概率,獲得略優(yōu)于IWME算法的綜合估計性能。(3)由于RPET算法利用相鄰隨機變量置信區(qū)間的比值預測噪聲特征值上限,導致該上限值偏高,算法在低信噪比條件下容易產生欠估。針對這一問題,提出了一種基于Wishart隨機矩陣特征值平方均值分布特性的假設檢驗算法(WSE)。WSE算法利用Wishart隨機矩陣特征值平方均值的分布函數(shù)和精確的噪聲功率估計值求解檢測統(tǒng)計量的判決門限。與RPET算法相比,WSE算法能夠將低信噪比和采樣數(shù)據長度較小條件下的正確估計概率再次明顯提高,同時具有更加優(yōu)異的綜合估計性能。
[Abstract]:In the non-cooperative MIMO communication scene, blind identification of communication signal parameters, as an intermediate step of signal detection and signal space-time decoding and demodulation, plays a very important role in spectrum management and monitoring. Software radio and military wireless communication countermeasures and other civil and military fields have been widely used. Accurate estimation of the number of MIMO transmit antennas is one of the key problems in blind identification of MIMO communication signal parameters. It can provide a priori knowledge for MIMO channel blind estimation, MIMO space-time code blind identification and MIMO modulation blind identification and so on. It has very important research significance and research value. It has been widely concerned and studied by many scholars in the field of wireless communication. In this paper, based on the previous work, the problem of MIMO antenna number estimation is explored and studied. The main contents are as follows: (1) the basic theoretical knowledge of MIMO system and stochastic matrix theory is briefly introduced; The mathematical theory foundation and derivation process of four classical MIMO antenna number estimation algorithms, AIC,MDL,PET and IWME, are described in detail. At the same time, according to the computer simulation results of the above four algorithms, the correct estimation probability and signal-to-noise ratio are analyzed. (2) A prediction eigenvalue upper bound algorithm based on stochastic matrix theory (RPET),) is proposed. Compared with PET algorithm based on classical multivariate statistical theory, RPET algorithm is able to show that Improve the probability of correct estimation under the condition of low signal-to-noise ratio (SNR) and small sampling data length, The synthetic estimation performance is slightly better than that of the IWME algorithm. (3) because the RPET algorithm uses the ratio of the confidence interval of adjacent random variables to predict the upper limit of the noise eigenvalue, the upper bound value is too high, and the algorithm is prone to underestimate under the condition of low signal-to-noise ratio (SNR). In response to this problem, A hypothesis checking algorithm based on the distribution of eigenvalue square mean of Wishart random matrix is proposed. (WSE). WSE algorithm uses the distribution function of square mean of eigenvalue of Wishart random matrix and accurate noise power estimate to solve the decision threshold of detection statistics. Compared with the RPET algorithm, the WSE algorithm can significantly improve the probability of correct estimation under the condition of low SNR and smaller sampling data length, and has better comprehensive estimation performance.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN827.1
本文編號：2281877
[Abstract]:In the non-cooperative MIMO communication scene, blind identification of communication signal parameters, as an intermediate step of signal detection and signal space-time decoding and demodulation, plays a very important role in spectrum management and monitoring. Software radio and military wireless communication countermeasures and other civil and military fields have been widely used. Accurate estimation of the number of MIMO transmit antennas is one of the key problems in blind identification of MIMO communication signal parameters. It can provide a priori knowledge for MIMO channel blind estimation, MIMO space-time code blind identification and MIMO modulation blind identification and so on. It has very important research significance and research value. It has been widely concerned and studied by many scholars in the field of wireless communication. In this paper, based on the previous work, the problem of MIMO antenna number estimation is explored and studied. The main contents are as follows: (1) the basic theoretical knowledge of MIMO system and stochastic matrix theory is briefly introduced; The mathematical theory foundation and derivation process of four classical MIMO antenna number estimation algorithms, AIC,MDL,PET and IWME, are described in detail. At the same time, according to the computer simulation results of the above four algorithms, the correct estimation probability and signal-to-noise ratio are analyzed. (2) A prediction eigenvalue upper bound algorithm based on stochastic matrix theory (RPET),) is proposed. Compared with PET algorithm based on classical multivariate statistical theory, RPET algorithm is able to show that Improve the probability of correct estimation under the condition of low signal-to-noise ratio (SNR) and small sampling data length, The synthetic estimation performance is slightly better than that of the IWME algorithm. (3) because the RPET algorithm uses the ratio of the confidence interval of adjacent random variables to predict the upper limit of the noise eigenvalue, the upper bound value is too high, and the algorithm is prone to underestimate under the condition of low signal-to-noise ratio (SNR). In response to this problem, A hypothesis checking algorithm based on the distribution of eigenvalue square mean of Wishart random matrix is proposed. (WSE). WSE algorithm uses the distribution function of square mean of eigenvalue of Wishart random matrix and accurate noise power estimate to solve the decision threshold of detection statistics. Compared with the RPET algorithm, the WSE algorithm can significantly improve the probability of correct estimation under the condition of low SNR and smaller sampling data length, and has better comprehensive estimation performance.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN827.1
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