Title :
Blind adaptive multiuser detection using a recurrent neural network
Author :
Liu, Shubao ; Wang, Jun
Author_Institution :
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China
Abstract :
Multiuser detection has gained much attention in recent years for its potential to improve greatly the capacities of CDMA communication systems. A recurrent neural network is presented for solving the nonlinear optimization problem involved in multiuser detection in CDMA. Compared with other neural networks, the presented neural network can converge globally to the exact optimal solution of the nonlinear optimization problem with nonlinear constraints and has relatively low structural complexity. Computer simulation results are presented to show the optimization capability. The performance in CDMA communication systems is also studied by means of simulation.
Keywords :
adaptive signal detection; code division multiple access; multiuser detection; optimisation; recurrent neural nets; spread spectrum communication; telecommunication computing; DS-CDMA; adaptive detection; blind adaptive multiuser detection; blind detection; nonlinear constraints; nonlinear optimization problem; recurrent neural network; structural complexity; wireless communication systems; Adaptive filters; Adaptive systems; Fading; Hopfield neural networks; Multiaccess communication; Multiuser detection; Neural networks; Real time systems; Recurrent neural networks; Wireless communication;
Conference_Titel :
Communications, Circuits and Systems, 2004. ICCCAS 2004. 2004 International Conference on
Print_ISBN :
0-7803-8647-7
DOI :
10.1109/ICCCAS.2004.1346362
