基于壓縮感知的無線通信信號(hào)處理方法研究
發(fā)布時(shí)間：2019-03-08 20:23
【摘要】：傳統(tǒng)的信號(hào)處理方法是在Nyquist采樣定理的基礎(chǔ)上建立起來的,這就意味著為了不失真的恢復(fù)原始信號(hào),發(fā)送信號(hào)的采樣速率至少為信號(hào)帶寬的兩倍。然而,隨著無線通信技術(shù)的飛速發(fā)展,信息需求量與日俱增,信號(hào)帶寬也隨之不斷增加,這使得傳統(tǒng)信號(hào)處理方法中信號(hào)的采樣、傳輸、處理和存儲(chǔ)過程都面臨嚴(yán)峻的挑戰(zhàn)。如何在獲取足夠多信息量的同時(shí)有效降低信號(hào)采樣速率已經(jīng)成為當(dāng)前無線通信領(lǐng)域的研究熱點(diǎn)。壓縮感知(Compressed Sensing,CS)理論能夠通過低維觀測信號(hào)恢復(fù)出高維原始稀疏信號(hào),將壓縮感知理論應(yīng)用于無線通信信號(hào)處理過程中,不僅能夠顯著降低信號(hào)的采樣速率,而且可以減少信號(hào)處理過程中的信息量,提高通信系統(tǒng)性能。為此,本文對(duì)CS在無線通信領(lǐng)域中的應(yīng)用進(jìn)行了相關(guān)研究,主要內(nèi)容包括基于CS的信號(hào)重構(gòu)方法,基于CS的信道縮短方法以及基于CS的寬帶頻譜感知方法,主要?jiǎng)?chuàng)新點(diǎn)如下:1、在基于CS的信號(hào)重構(gòu)方法的研究中,為了提高稀疏信號(hào)重構(gòu)效率,提出了一種一次投影子空間追蹤算法。首先將傳統(tǒng)的子空間追蹤算法的迭代過程分解為一次相關(guān)最大化過程和兩次投影過程;然后通過減少迭代中觀測向量在支撐集上的投影過程,降低了算法的復(fù)雜度,提高了稀疏信號(hào)的重構(gòu)效率。同時(shí),分析了現(xiàn)有的衡量算法重構(gòu)性能指標(biāo)的不足,并提出一種更可靠的參考指標(biāo)。2、在基于CS的信道縮短方法的研究中,針對(duì)使用信道縮短均衡器的通信系統(tǒng)的復(fù)雜度會(huì)隨著均衡器中非零抽頭的增加而快速增大的問題,提出了一種半融合貪婪追蹤算法,并用該算法實(shí)現(xiàn)了稀疏信道縮短均衡器。首先在最小均方誤差準(zhǔn)則下,將信道縮短問題轉(zhuǎn)化為均衡器中非零抽頭數(shù)目最小化問題;然后通過稀疏度預(yù)估計(jì)過程確定了均衡器稀疏度下界;最后通過回溯重構(gòu)階段和支撐集擴(kuò)展階段確定了均衡器的非零抽頭,實(shí)現(xiàn)了稀疏信道縮短均衡器。3、在基于CS的寬帶頻譜感知方法的研究中,為了提高寬帶頻譜感知效率,設(shè)計(jì)了一種基于壓縮感知的協(xié)作頻譜感知方法。首先通過帶通濾波器組和壓縮采樣方法的組合使用,進(jìn)一步降低了寬帶信號(hào)的采樣速率;然后通過認(rèn)知用戶與融合中心的信息交互,確定了每個(gè)認(rèn)知用戶所需重構(gòu)的子頻段,該過程使得每個(gè)認(rèn)知用戶只需要重構(gòu)部分頻帶,大幅度降低了信號(hào)重構(gòu)階段的復(fù)雜度;最后通過多用戶的協(xié)作感知提高了感知方法的可靠性。
[Abstract]:The traditional signal processing method is based on the Nyquist sampling theorem, which means that in order to restore the original signal without distortion, the sampling rate of the transmitted signal is at least twice the bandwidth of the signal. However, with the rapid development of wireless communication technology, the demand for information is increasing day by day, and the signal bandwidth is also increasing, which makes the signal sampling, transmission, processing and storage procedures face severe challenges in the traditional signal processing methods. How to obtain enough information while effectively reducing the signal sampling rate has become a hot topic in the field of wireless communication. The compressed sensing (Compressed Sensing,CS) theory can recover the high-dimensional original sparse signal from the low-dimensional observation signal, and apply the compressed sensing theory to the wireless communication signal processing process, which not only can significantly reduce the sampling rate of the signal. Moreover, it can reduce the amount of information in the signal processing process and improve the performance of the communication system. In this paper, the application of CS in wireless communication is studied, including CS-based signal reconstruction method, CS-based channel shortening method and CS-based broadband spectrum sensing method. The main innovations are as follows: 1. In order to improve the efficiency of sparse signal reconstruction, a one-time shadow space tracking algorithm is proposed in the research of signal reconstruction method based on CS. Firstly, the iterative process of the traditional subspace tracking algorithm is decomposed into one-time correlation maximization process and two-time projection process. Then by reducing the projection process of the observation vector on the support set, the complexity of the algorithm is reduced and the reconstruction efficiency of sparse signal is improved. At the same time, the shortcomings of the performance index of the existing measurement algorithm reconstruction are analyzed, and a more reliable reference index is proposed. 2. In the research of channel shortening method based on CS, In order to solve the problem that the complexity of communication system using channel shortening equalizer will increase rapidly with the increase of non-zero tap in equalizer, a semi-fusion greedy tracking algorithm is proposed, and the sparse channel shortening equalizer is implemented with this algorithm. Under the minimum mean square error criterion, the channel shortening problem is transformed into the problem of minimizing the number of non-zero taps in the equalizer, and then the lower bound of the sparsity of the equalizer is determined by the sparsity pre-estimation process. Finally, the non-zero tap of the equalizer is determined by the backtracking reconstruction phase and the extension stage of the support set, and the sparse channel shortening equalizer is realized. 3. In order to improve the efficiency of broadband spectrum sensing in the research of broadband spectrum sensing method based on CS, A cooperative spectrum sensing method based on compressed sensing is designed. Firstly, a combination of band-pass filter bank and compression sampling method is used to further reduce the sampling rate of wideband signals. Then through the information interaction between the cognitive user and the fusion center, the sub-band that each cognitive user needs to be reconstructed is determined. In this process, each cognitive user only needs to reconstruct part of the frequency band, which greatly reduces the complexity of the signal reconstruction stage. Finally, the reliability of the perceptual method is improved by multi-user cooperative perception.
【學(xué)位授予單位】：寧波大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.7
本文編號(hào)：2437178
[Abstract]:The traditional signal processing method is based on the Nyquist sampling theorem, which means that in order to restore the original signal without distortion, the sampling rate of the transmitted signal is at least twice the bandwidth of the signal. However, with the rapid development of wireless communication technology, the demand for information is increasing day by day, and the signal bandwidth is also increasing, which makes the signal sampling, transmission, processing and storage procedures face severe challenges in the traditional signal processing methods. How to obtain enough information while effectively reducing the signal sampling rate has become a hot topic in the field of wireless communication. The compressed sensing (Compressed Sensing,CS) theory can recover the high-dimensional original sparse signal from the low-dimensional observation signal, and apply the compressed sensing theory to the wireless communication signal processing process, which not only can significantly reduce the sampling rate of the signal. Moreover, it can reduce the amount of information in the signal processing process and improve the performance of the communication system. In this paper, the application of CS in wireless communication is studied, including CS-based signal reconstruction method, CS-based channel shortening method and CS-based broadband spectrum sensing method. The main innovations are as follows: 1. In order to improve the efficiency of sparse signal reconstruction, a one-time shadow space tracking algorithm is proposed in the research of signal reconstruction method based on CS. Firstly, the iterative process of the traditional subspace tracking algorithm is decomposed into one-time correlation maximization process and two-time projection process. Then by reducing the projection process of the observation vector on the support set, the complexity of the algorithm is reduced and the reconstruction efficiency of sparse signal is improved. At the same time, the shortcomings of the performance index of the existing measurement algorithm reconstruction are analyzed, and a more reliable reference index is proposed. 2. In the research of channel shortening method based on CS, In order to solve the problem that the complexity of communication system using channel shortening equalizer will increase rapidly with the increase of non-zero tap in equalizer, a semi-fusion greedy tracking algorithm is proposed, and the sparse channel shortening equalizer is implemented with this algorithm. Under the minimum mean square error criterion, the channel shortening problem is transformed into the problem of minimizing the number of non-zero taps in the equalizer, and then the lower bound of the sparsity of the equalizer is determined by the sparsity pre-estimation process. Finally, the non-zero tap of the equalizer is determined by the backtracking reconstruction phase and the extension stage of the support set, and the sparse channel shortening equalizer is realized. 3. In order to improve the efficiency of broadband spectrum sensing in the research of broadband spectrum sensing method based on CS, A cooperative spectrum sensing method based on compressed sensing is designed. Firstly, a combination of band-pass filter bank and compression sampling method is used to further reduce the sampling rate of wideband signals. Then through the information interaction between the cognitive user and the fusion center, the sub-band that each cognitive user needs to be reconstructed is determined. In this process, each cognitive user only needs to reconstruct part of the frequency band, which greatly reduces the complexity of the signal reconstruction stage. Finally, the reliability of the perceptual method is improved by multi-user cooperative perception.
【學(xué)位授予單位】：寧波大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.7
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 張成;楊海蓉;韋穗;;基于隨機(jī)間距稀疏Toeplitz測量矩陣的壓縮傳感[J];自動(dòng)化學(xué)報(bào);2012年08期
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