Blind Detection of QAM Signals Using Continuous Hopfield-type Neural Network
Ruan Xiu-kai① 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 210046, China)
Abstract：A novel blind detection algorithm of multi-valued square/non-square QAM signals using complex Continuous Hopfield-type Neural Network (CHNN) is proposed. The blind detection issue of multi-valued QAM signals is transformed into solving a quadratic optimization problem firstly. The method of mapping the cost function of this optimization one to the energy function of CHNN is shown. A complex activation function to fit this special issue is designed, and the energy function of CHNN is analyzed. Meantime, a special connective matrix is constructed to ensure the detect signals correctly and the general law of making correct choice of the number of neurons is illustrated. Finally, simulation results using square and non-square QAM signals demonstrate the effectiveness and robustness of this new algorithm.
2011, Vol. 33 
