无线通信系统压缩采样定时同步机制研究

Accurate timing synchronization is the key supporting technology of wireless communication systems. Most of existing timing methods are based on the Nyquist Sampling Theory, which results in low frequency efficiency. Especially for systems with multi-path time delay or with distributed antennas or of Ultra-wideband, the performance of timing is not good. The emerging theory of Compressed Sensing (CS) is a revolutionary breakthrough for digital signal processing, which provides a new inspiration for solving the existing problems of timing synchronization. According to the CS theory, the sparse signal can be reconstructed with high resolution even by a sampling frequency much smaller than that of Nyquist Sampling. In this project, the compressed sampling method for the sparse wireless channel and synchronization signal are studied to improve the spectral efficiency. Based on CS, the signal model and performance model are proposed. The Statistic Characteristics of timing synchronization metric are optimized using reconstruction algorithm of CS to improve the accuracy and robustness of timing synchronization. Additionally, these models and methods are applied to solve the problems, such as low resolution of multipath time-delay estimation and high required SNR in multipath channel. Finally, these achievements should be integrated in a systematic way for constructing a basic theory of compressed sampling timing synchronization. And the semi - physical simulation platform will be developed to test the accurateness of models and the performance of algorithms. These research achievements make some attributions in the whole progress of timing synchronization technique. It promotes the CS theory applied into wireless communication forward further.

精确定时同步是无线通信系统的核心支撑技术，现有定时同步方法在传统Nyquist采样定理的限制下，频谱利用率低，且对多径时延、分布式天线和超宽带系统的定时性能不佳。最近出现的压缩采样理论为信号处理技术带来了革命性的突破，项目拟采用这一理论，利用信道的稀疏性和压缩采样的信号重构能力，用远低于传统方法的开销完成系统定时同步，提高频谱效率。提出压缩采样同步的信号模型和性能分析模型，用压缩采样重构方法优化传统定时同步匹配相关度量的统计特性，提高定时同步的精度和健壮性，并解决多径时延估计分辨率低和分布式天线系统在多径信道下同步信噪比需求高的实际问题。最后，我们将集成研究成果，构建压缩采样定时同步机制的基础理论体系，并建立半实物仿真平台对理论进行正确性验证和性能的分析与评价。项目研究成果将为定时同步的整体技术进步做出一定的贡献，进一步推动压缩采样理论在无线通信领域中的发展。

本项目针对现代无线通信系统，尤其是OFDM系统的时延和频偏敏感问题，利用信道的稀疏特性和压缩采样/感知的信号重构能力，设计了新的定时同步算法和重构算法，建立了相应的性能分析模型，形成了相对完整的压缩感知定时同步估计理论体系，并将该方法应用于分布式天线系统，通过大量的仿真实验验证了所研究算法的有效性。. 按照本项目预定目标，圆满完成了所明确的主要研究计划，主要研究成果包括：(1)通过结合OFDM信号模型和压缩感知理论，提出了一种新的压缩感知定时同步信号模型；(2) 基于zadoff序列的相关性，提出了一种稀疏化方法，设计了一种新的压缩感知定时同步算法（CST），减少了训练序列长度，同时提高了定时同步精度；另外，提出了基于黄金分割法（0.618法）优化的主流定时同步算法，减少了相关峰搜索时间；(3)根据CST算法中度量函数的统计特性，提出了阈值选择模型，设计了基于计算阈值的同步算法(CTFS)，提高了定时同步系统的鲁棒性；(4) 利用峰均比较高的Golay序列，设计了一种在硬件上易于实现的确定性序列的压缩感知信道及多径时延估计方法；(5)考虑MIMO分布式天线系统多链路间的相关特性，针对相关大的天线系统，设计了基于确定性分布式压缩感知(DCS)的多径时延估计模型；针对相关大的天线系统，设计了基于ADMM重构的多径时延估计模型，降低了传统时延估计算法的复杂度，并提高了频带利用率。