基于随机共振和极化天线的多阶段频谱感知技术研究

With the continuous growth of mobile broadband access services, the radio spectrum resources shortage is becoming more and more prominent. The Cognitive Radio (CR) is an effective way to solve this problem, which mainly requires the secondary users quickly and accurately sense the spectrum hole, and provide prerequisite for spectrum access, monitoring and management. However, spectrum character is extremely difficult to extract under the condition of weak signal, which has formed a bottleneck in the development of spectrum sensing technology. This project will study the multi-stage spectrum sensing algorithm based on stochastic resonance and polarization antenna and its detection performance in weak signal environment. The specific contents include: using stochastic resonance to improve the output SNR of polarization components, and adaptively searching for the optimal matching relationship between the nonlinear system parameters according to the noise energy; detecting resonance signal using multi-stage spectrum sensing method based on Energy Detection, eigenvalue and Generalized Likelihood Ratio Test, and adjusting sensing strategy according to the detection probability and the computational complexity; aiming at four kinds of complex wireless environment that the partial polarization noise, the wave with arrival angle, polarization mode dispersion and wireless channel fading, the detection performance of proposed algorithm were further analyzed. The purpose of this project is to use new technology to improve the spectrum utilization rate under weak signal condition, and lay a solid foundation for cognitive radio technology to widely apply in the real environment

无线电频谱资源短缺问题随着移动宽带接入业务的不断增长而日益突出，认知无线电是解决该问题的有效途径。它主要要求次用户快速而准确地感知频谱空洞，为频谱接入、监测和管理提供前提。然而微弱信号条件下信号频谱特征极难提取，形成了频谱感知技术发展的一个瓶颈。本项目将研究基于随机共振和极化天线的多阶段频谱感知算法在微弱信号下的检测性能。具体内容包括：采用随机共振提高极化分量的输出信噪比，自适应地根据噪声能量搜索非线性系统参数之间的最优匹配关系；采用能量-特征值-广义似然比的多阶段频谱感知方法检测共振信号，并根据检测概率和计算复杂度调整感知策略；针对部分极化噪声、来波存在到达角度、极化模色散和无线信道衰落四种复杂无线环境因素进一步分析所提算法的感知性能。本项目旨在采用新感知技术提高微弱信号条件下的频谱利用率，为广泛地应用于真实认知无线电环境奠定技术基础。

本项目针对微弱信号和复杂无线信道的条件下传统频谱感知技术的感知性能差的瓶颈问题，采用随机共振技术恢复信号及其频谱的特征，并结合极化天线的多阶段频谱感知模型开展研究。.研究内容包括：（1）建立了基于随机共振的极化天线频谱感知模型，采用随机共振分别对每个极化分量信号进行预处理，自适应地获得最大的输出信噪比，同时将该模型扩展到多天线频谱感知模型上；（2）为了解决随机共振系统从高频信号到低频信号的等价转化问题，分别采用了频率重采样技术和归一化尺度变换技术，并进行了比较；（3）对于随机共振输出的极化信号分量，采用基于广义似然比和特征值的多种频谱感知方法进行检测，并针对存在部分极化噪声、双滤波器等各种复杂无线场景下的频谱感知性能进行了研究。.研究成果表明：（1）等效低频越小(小于0.01Hz），采样频率越大（512倍的载波频率以上），随机共振对于频谱感知的性能提升越明显，但在实际的无线应用中，需要考虑时间复杂度和检测精度的折中；（2）在基于极化天线的多阶段频谱感知模型中，随机共振在信噪比较低、且参数合适的情况下，对广义似然比检测的感知性能提升作用较大；（3）当存在部分极化噪声时，随机共振的效果受到影响，需要用广义哈达玛检测替代广义似然比检测。.本项目的研究成果为在认知无线电系统和无线电监测等领域的应用提供理论依据和实验支撑。