A Multiuser Detector Based on Lagrange Neural Network
Tang Puying Chen YongqianHuang Shunji (Dept. of Opto-electronic Tech., UEST of China Chengdu 610054)
According to the optimization theory and the neural network (NN) theory, a multiuser detector (MUD) is proposed, which takes the optimum MUD problem as combinatorial optimum problem. Using the neural network which has the ability of fast optimization computing, the Lagrange neural network (LNN) MUD is derived. Theoretical analysis and numerical results show that in aspect of bit-error rate and multiple access interfernce, the LNN MUD is better than the conventional and decorrelated MUD in aspect of "near-far" resistance, the LNN MUD is better than the conventional MUD and worse than the decorrelated detector and the LNN MUD can be easily implemented by VLSI technology.
【CateGory Index】： TP183