基于实虚型连续多值复数Hopfield神经网络的QAM盲检测

摘要

针对统计量算法盲检测QAM信号的缺陷,该文提出了一个实虚型连续多值复数Hopfield神经网络算法,该网络的实部、虚部各含一个连续多值实激活函数.该文构造了适用于该网络的能量函数,并分别在异步和同步更新模式下证明了该神经网的稳定性.当该神经网的权矩阵借助接收数据补投影算子构成时,该实虚型连续多值复数Hopfield神经网络可有效地实现QAM信号盲检测.仿真试验表明:该算法采用较短接收数据即可到达全局真解点,并且适用于含公零点信道.%Considering the disadvantage of the algorithms based on statistics, a novel algorithm based on Complex Hopfield Neural Network with Real-Imaginaiy-type Soft-Multistate-activatlon-funetion (CHNN_ RISM) is proposed to detect QAM signals blindly. A multi-valued continuous activation function is constructed in both of the real part and imaginary part of CHNN_ RISM. A new energy function for CHON. RISM is constructed in this paper and the stabilities with asynchronous and synchronous operating mode are also analyzed separately. While the weighted matrix of CHNN_ RISM is constructed by the complementary projection operator of received signals, the problem of quadratic optimization with integer constraints can successfully solved with the CHNN_ RISM, and the QAM signals are blindly detected. Simulation results show that the algorithm reaches the real equilibrium points with shorter received signals and appropriate for channel with common zeros.