【摘要】：隨著社會(huì)信息化進(jìn)程的加快,移動(dòng)互聯(lián)網(wǎng)及大數(shù)據(jù)時(shí)代的到來,物聯(lián)網(wǎng)、云計(jì)算、大型數(shù)據(jù)中心、高清視頻及多媒體實(shí)時(shí)業(yè)務(wù)等帶寬消耗型業(yè)務(wù)量迅速增長(zhǎng),使得全球網(wǎng)絡(luò)數(shù)據(jù)流量以將近60%的速度迅速增長(zhǎng)。所需的巨大網(wǎng)絡(luò)吞吐量將對(duì)承載信息的骨干光網(wǎng)絡(luò)傳輸系統(tǒng)的要求提高到全新的高度。然而,由于單模光纖傳輸介質(zhì)中固有非線性效應(yīng)及放大器的自發(fā)輻射噪聲,使得單模光纖通信系統(tǒng)的容量越來越趨近香農(nóng)極限。為了實(shí)現(xiàn)對(duì)骨干光網(wǎng)絡(luò)的擴(kuò)容,提出基于少模光纖的模分復(fù)用技術(shù),其采用相互正交的模式作為信息傳輸載體,成倍的提高了光通信系統(tǒng)的傳輸容量。然而,由于在實(shí)際少模光纖傳輸系統(tǒng)中,存在模式隨機(jī)耦合、差分模式群時(shí)延等損傷因素,使得傳輸系統(tǒng)的接收信號(hào)串?dāng)_嚴(yán)重,為了實(shí)現(xiàn)少模光纖模分復(fù)用系統(tǒng)的長(zhǎng)距離大容量傳輸,需要在接收端采用有效的數(shù)字信號(hào)處理(DSP)算法實(shí)現(xiàn)對(duì)傳輸信號(hào)的解復(fù)用。本論文研究的主要內(nèi)容為少模光纖模分復(fù)用系統(tǒng)傳輸信號(hào)的解復(fù)用,提出采用獨(dú)立矢量分析(Independent vector analysis,IVA)算法對(duì)接收信號(hào)進(jìn)行盲源分離,并分別將IVA算法應(yīng)用于強(qiáng)度調(diào)制直接檢測(cè)的模分復(fù)用系統(tǒng)及調(diào)制格式為QPSK相干檢測(cè)的模分復(fù)用系統(tǒng)中,通過對(duì)接收信號(hào)解復(fù)用前后誤碼率的分析,衡量算法的解復(fù)用性能。本文的主要工作內(nèi)容如下:首先,以Maxwell方程組為基本理論依據(jù),對(duì)少模光纖傳輸介質(zhì)中模式傳輸特性及截止條件進(jìn)行推導(dǎo)。詳細(xì)介紹了少模光纖傳輸鏈路的損傷因素:模式隨機(jī)耦合、差分模式群時(shí)延,并分別在時(shí)域及頻域內(nèi)分析損傷因素對(duì)傳輸信號(hào)的影響。介紹了少模光纖傳輸系統(tǒng)的基本組成,并采用矩陣傳輸模型對(duì)傳輸鏈路進(jìn)行建模。其次,對(duì)獨(dú)立矢量分析算法的基本原理及流程進(jìn)行詳細(xì)介紹。IVA算法是將時(shí)域卷積混合信號(hào)變換到頻域,在頻域內(nèi)對(duì)各個(gè)頻點(diǎn)進(jìn)行瞬時(shí)分離,實(shí)現(xiàn)混合信號(hào)的去串?dāng)_,其中在頻域處理的過程,采用將同一源信號(hào)不同頻點(diǎn)的數(shù)據(jù)作為向量進(jìn)行處理,在各個(gè)頻段瞬時(shí)分離的同時(shí),解決頻段間的次序不確定性問題。通過對(duì)分離準(zhǔn)則的介紹,實(shí)現(xiàn)對(duì)IVA代價(jià)函數(shù)的簡(jiǎn)單推導(dǎo),據(jù)此能夠深刻理解IVA算法無需頻段排序的理論依據(jù),并得出分離矩陣的更新規(guī)則。此外,對(duì)IVA算法的適用條件進(jìn)行約束,可以直觀看出,模分復(fù)用系統(tǒng)中傳輸信號(hào)能夠滿足IVA算法使用的基本條件,同時(shí)介紹了IVA算法中的幅度不確定性及其解決方案。再次,依據(jù)傳輸鏈路建模理論,搭建基于強(qiáng)度調(diào)制直接檢測(cè)(IM/DD)的少模光纖模分復(fù)用仿真系統(tǒng),并采用IVA算法對(duì)IM/DD模分復(fù)用系統(tǒng)接收端混疊信號(hào)進(jìn)行解復(fù)用。通過對(duì)接收信號(hào)解復(fù)用前后波形圖眼圖及誤碼率曲線的對(duì)比,衡量IVA算法的解復(fù)用效果。仿真結(jié)果表明IVA算法無需額外頻點(diǎn)排序的情況下,具有良好的解復(fù)用性能。最后,將IVA算法應(yīng)用于調(diào)制格式為QPSK的模分復(fù)用系統(tǒng)中,探究了IVA算法分離矩陣更新公式中非線性函數(shù)的選擇對(duì)于混合QPSK信號(hào)的解復(fù)用性能的影響,通過解復(fù)用前后信號(hào)星座圖的變化及系統(tǒng)誤碼率情況,分析IVA算法的解復(fù)用性能,并與頻域獨(dú)立成分分析算法的解復(fù)用性能進(jìn)行對(duì)比。仿真結(jié)果顯示,IVA算法與頻域獨(dú)立成分分析(FD-ICA)算法的解復(fù)用性能相當(dāng)。并對(duì)IVA算法計(jì)算復(fù)雜度進(jìn)行分析。
[Abstract]:With the acceleration of social informationization, the arrival of mobile Internet and big data era, bandwidth-consuming services such as Internet of Things, cloud computing, large data centers, high-definition video and multimedia real-time services are growing rapidly, which makes the global network data flow grow rapidly at a rate of nearly 60%. However, due to the inherent nonlinear effect in single-mode optical fiber transmission medium and the spontaneous emission noise of amplifier, the capacity of single-mode optical fiber communication system is getting closer and closer to Shannon's limit. In order to realize the expansion of backbone optical network, a few-mode optical fiber is proposed. Modular division multiplexing (DMA) technology, which uses orthogonal modes as information transmission carriers, doubles the transmission capacity of optical communication systems. However, due to the existence of mode random coupling, differential mode group delay and other damage factors, the received signal of transmission system crosstalk is serious, in order to achieve less. For the long-distance and large-capacity transmission of mode-division multiplexing (MODM) systems, effective digital signal processing (DSP) algorithms should be adopted at the receiver to realize the demultiplexing of the transmitted signals. VA) Blind source separation (BSS) algorithm is used to separate the received signals, and IVA algorithm is applied to the intensity modulation direct detection mode division multiplexing system and the modulation format is QPSK coherent detection mode division multiplexing system respectively. Firstly, based on the Maxwell equations, the mode propagation characteristics and cut-off conditions in the medium of small-mode fiber transmission are deduced. The damage factors of the transmission links of small-mode fiber, such as mode random coupling, differential mode group delay, are introduced in detail, and the influence of the damage factors on the transmission signal is analyzed in time domain and frequency domain respectively. Secondly, the basic principle and flow of the independent vector analysis algorithm are introduced in detail. IVA algorithm transforms the time domain convolution mixed signal into frequency domain, and separates each frequency point instantaneously in frequency domain to realize the mixed signal. In the process of crosstalk removal, the data of different frequency points of the same source signal are processed as vectors in frequency domain, and the order uncertainties between the frequency bands are solved while the data of different frequency points of the same source signal are separated instantaneously in each frequency band. In addition, constraints on the applicable conditions of the IVA algorithm show that the transmission signal in the modular division multiplexing system can satisfy the basic conditions of the IVA algorithm. At the same time, the amplitude uncertainty in the IVA algorithm and its solution are introduced. The simulation system of mode division multiplexing (MDM) based on intensity modulation direct detection (IM/DD) is built, and the overlapped signals of the receiving end of IM/DD MDM system are demultiplexed by IVA algorithm. The results show that the IVA algorithm has good demultiplexing performance without additional frequency sorting. Finally, the IVA algorithm is applied to the modular multiplexing system with QPSK modulation format, and the influence of the selection of nonlinear functions in the updating formula of the separation matrix of the IVA algorithm on the demultiplexing performance of the mixed QPSK signal is investigated. The performance of the IVA algorithm is analyzed and compared with that of the Frequency-Domain Independent Component Analysis (FD-ICA). The simulation results show that the performance of the IVA algorithm is equivalent to that of the Frequency-Domain Independent Component Analysis (FD-ICA).
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.11

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