成像通信系统性能研究与优化

发布时间：2018-04-03 20:15

本文选题：无线光通信　切入点：成像通信　出处：《中国科学技术大学》2017年博士论文

【摘要】：近年来,随着移动互联网和物联网的快速发展,移动用户的业务流量呈爆炸式增长。为了实现热点高容量、低功耗大连接和低时延高可靠的通信连接,以扩展频谱带宽的高频段传输成为下一代移动通信的关键技术。其中,以可见光通信技术为代表的光保真技术(LiFi)因其具有超大带宽、绿色高能效、无电磁干扰以及易部署等优点,成为构建具有高频谱效率与高能量效率超密集异构网络的重要选择。成像光通信是一种特殊的可见光通信方式,在实现泛在部署、超大规模光多输入多输出(MIMO)接收、多频(色)接收以及大视场角抗干扰接收等方面有着明显的优势。因此,基于成像通信的研究对构建大容量无线光通信网络意义重大。从现有关于成像通信的研究来看,大多集中于调制技术、系统同步及系统实现等方面。但从后续关于最优传输信号设计、最佳信号检测与估计以及信道编码译码等方面的研究来看,构建一个能够统一表征成像通信的通信模型,同时基于此模型进行性能分析的研究至关重要。因此,本文针对成像通信特点,构建成像通信系统模型,推导不同通信场景下的信道容量,并通过实验的办法验证所构建通信模型及容量性能理论分析的正确性。本论文的主要研究内容和贡献如下:(1)构建能够统一表征成像通信的通信模型,包括信号模型、噪声模型及统计信道模型。考虑照明约束条件,给出了由多维正交基函数集合构成的非负信号可行域、平均光功率约束可行域及峰值光功率可行域空间。建立了包括源端辐射模式、光学系统响应、像素探测器响应以及指向偏差等因素在内的统计信道模型。同时基于光电器件的物理性质及制造工艺影响,系统分析了成像接收机中的噪声分量。最后建立能够统一表征成像通信的通信模型。(2)在发射端信道信息(CSIT)已知的情况下,首先将单光源构成的理想成像单输入单输出(SISO)信道建模为混合式依赖信号的高斯噪声(M-SDGN)信道。针对M-SDGN信道,我们分析了基于熵率不等式、输入输出微分滴、容量对偶方法以及球填充理论等方法在进行M-SDGN信道容量推导时的不适用性。接下来,我们在数学上证明了使得M-SDGN信道容量可达的最优输入分布的唯一性以及离散性,并给出了满足最优输入分布的充分必要条件。最后,在给定最优输入分布充分必要的条件上,我们证明了 x= 0为最优幅度质点的结论,并结合Blahut-Arimoto迭代算法设计出低复杂度的离散输入分布搜索算法。(3)在CSIT已知的情况下,我们将多光源构成的成像MIMO信道建模为MIMO-SDGN信道。针对成像MIMO-SDGN信道,我们首先分析了和容量可达最优输入分布不再为离散输入分布。之后,我们分别通过信道矩阵求逆、QR分解以及具有直流偏置的奇异值分解(DC-SVD)分解的方法将MIMO-SDGN信道解耦为具有不同光约束及SDGN的并行光信道。之后,我们给出了该并行光信道和容量上下界的闭式表达,以及所达和容量上下界的功率分配策略。仿真结果表明,所推导的成像MIMO-SDGN信道和容量上下界在高信噪比下具有较小的间隙,体现了容量紧界的特征,并且验证了基于信道矩阵求逆及QR分解方法所求的和容量大于DC-SVD解耦办法。(4)搭建成像通信试验平台,统计分析成像通信中的噪声模型,比较系统实际所达速率与理论容量性能的性能差异。通过实验,我们验证了成像通信中的噪声为高斯白噪声,输入光信号与输出噪声方差之间呈二次函数关系,从而验证了所提M-SDGN信道模型的正确性。另外我们测试了优化设计后的成像SISO系统与成像MIMO系统所能达的传输速率,并且与相应参数下的理论容量性能进行比较。结果表明,不管是优化后的脉冲幅度调制SISO(PAM-SISO)系统还是颜色强度调制MIMO(CIM-MIMO)系统,系统所达的速率与容量界还都还存在2 bit/s/Hz～3 bit/s/Hz 左右的余量。
[Abstract]:In recent years, with the rapid development of mobile Internet, mobile user traffic exploding. In order to realize the focus of high capacity, low power consumption and low delay and high reliability communication connection connection to spread spectrum bandwidth and high frequency transmission has become the key technology of the next generation of mobile communication. Among them, the visible light communication technology as the representative of the optical fi (LiFi) because of its large bandwidth, high efficiency green, no electromagnetic interference and easy deployment has become an important choice to construct has high spectrum efficiency and high energy efficiency of ultra dense heterogeneous network. The imaging optical communication is a kind of visible light communication in a special way. The ubiquitous deployment of large scale optical multiple input multiple output (MIMO) receiver, multi frequency (color) has obvious advantages and wide field angle receiving anti jamming receiver and so on. Therefore, research on imaging communication based on the Built in high-capacity wireless optical communication network is of great significance. From the existing research on image communication, mostly focused on the modulation technology, system synchronization and system realization. But from the follow-up on the optimal transmission signal design, optimal signal detection and estimation and channel encoding decoding and so on, to build a unified communication model characterization of imaging communication, and based on the model of performance analysis. Therefore, according to the characteristics of image communication, construct the imaging model of communication system, the channel capacity is derived in different communication scenarios, and through experiments to build correct analysis of communication model and capacity theory. The main research contents of this thesis and the contributions are as follows: (1) to construct the unified communication model characterizing the imaging communication, including signal model, channel model and noise model system. Considering the lighting constraints, by multidimensional orthogonal basis function set consisting of non negative signal gives the feasible domain space, Ping Junguang power constraint feasible domain and peak optical power. The feasible region including the establishment of the source radiation pattern, optical system response, pixel detector response and pointing error and other factors, the statistical channel model. At the same time, the physical properties of photoelectric the device and the manufacturing process based on systematic analysis of the influence, the noise component imaging receiver. Finally the establishment of unified communication model can characterize imaging communication. (2) in the channel information at the transmitter (CSIT) is known, will be the first single output ideal imaging single light source composed of single input (SISO) channel modeling for Gauss mixed signal dependent noise (M-SDGN) channel. According to the M-SDGN channel, we analyze the rate of entropy inequality based on input and output differential capacity drops, dual method and ball filling Applicability of the theory method in the derivation of M-SDGN channel capacity. Next, we prove the uniqueness of the optimal input distribution of M-SDGN channel capacity is discrete and in mathematics, and gives the necessary and sufficient conditions for the optimal input distribution. Finally, the optimal input distribution is given a sufficient and necessary condition. We show that x= 0 is the optimal particle amplitude results, combined with the Blahut-Arimoto algorithm to design the discrete input distribution search algorithm with low complexity. (3) CSIT in the known circumstances, we will MIMO channel modeling, imaging light source for MIMO-SDGN channel. According to the imaging of MIMO-SDGN channel, we first analyze the capacity and up to the optimal input distribution is no longer for the discrete input distribution. After that, we were through the inverse channel matrix, QR decomposition and singular value decomposition (DC-SVD with DC bias ) decomposition method MIMO-SDGN channel decoupling for parallel optical channels with different optical constraints and SDGN. After that, we give the lower bound on the capacity of parallel optical channel and a closed expression, and the lower bound on the capacity and power allocation strategy. The simulation results show that the lower bound of channel capacity and MIMO-SDGN imaging is deduced a small gap in high SNR, reflects the characteristics of capacity tight bound, and verified the desired inverse and QR channel matrix decomposition method based on the capacity of more than DC-SVD and decoupling approach. (4) to build a test platform for imaging communication, statistical analysis of noise model imaging communication, the actual rate and capacity theory the performances of the difference system. Through the experiment, we verified the noise imaging communication in the white Gauss noise, a quadratic function relationship between the input signal and output noise variance, and verification The correctness of the proposed M-SDGN channel model. We also tested the transmission rate of the optimized SISO imaging system and the imaging system can reach MIMO, and compared with the theoretical capacity of the corresponding parameters. The results show that whether the optimized SISO pulse amplitude modulation (PAM-SISO) system and color intensity modulation MIMO (CIM-MIMO) system, speed and capacity are the world system there are 2 bit/s/Hz to about 3 bit/s/Hz margin.

【学位授予单位】：中国科学技术大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TN929.1

【相似文献】