Blind Detection of 64QAM Signals with a Complex Discrete Hopfield Network
Zhang Yun①② Zhang Zhi-yong②
①(College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ②(College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
Abstract：A novel algorithm based on Complex Discrete Hopfield Neural Network (CDHNN) is proposed to detect blindly multi-valued QAM signals in this paper. A multi-valued discrete activation function is constructed in both of the real part and imaginary part of CDHNN. Limitation for the energy function of the classic binary-valued discrete Hopfield neural network is analyzed in this paper and a new energy function for CDHNN is also constructed. Further more the stability for multi-valued CDHNN is also proved in the paper. While the weighted matrix of CDHNN is constructed by the complementary projection operator of received signals, the problem of quadratic optimization with integer constraints can successfully solved with the CDHNN, and the QAM signals are blindly detected. Simulation results show that the algorithm reaches the real equilibrium points with shorter received signals and show high speed to detect blindly multi-valued signals.
2011, Vol. 33 
