基于高阶统计特征的空时分组码盲识别方法

空时分组码的盲识别是认知无线电领域一个新的重要问题。多数现有算法在多接收天线下进行识别，然而这些算法并不完全适用于单接收天线条件。针对上述问题，该文提出一种同时适用于单接收天线和多接收天线的空时分组码盲识别方法。利用空时分组码矩阵内元素的相关性，提出四阶统计量作为盲识别的特征参数，并通过最小欧氏距离的方式检验四阶统计量的差异，达到识别的目的。蒙特卡洛仿真表明，算法识别性能较好，且不需要预先知道信道信息、噪声信息和调制信息，对多普勒频移和相位噪声具有一定的适应性。

An Algorithm for Blind Classification of Space-time Block Code Based on Higher-order Statistics

Blind recognition of Space-Time Block Code (STBC) is a new important task in cognitive radio system. Most of the previous researches require multiple receive antennas, however, in many practical applications, size and power on the receivers may favor single receive antenna solution. To solve the problem above, an algorithm for blind classification of STBC is proposed. Using the correlation of the symbols in STBC block, fourth-order statistics are used as feature, and Euclidean metric between two statistics is used to classify different STBCs. It does not require estimation of the channel, signal-to-noise, and modulation of the transmitted signals. Monte Carlo simulations show the validity of the algorithm with low sensitivity to phase noise and Doppler shift.