Title :
Recurrent neural network techniques in multiuser detection
Author :
Moodley, N. ; Mneney, S.H.
Author_Institution :
Sch. of Electr., Electron. & Comput. Eng., Univ. of KwaZulu-Natal, Durban
Abstract :
This paper explores the use of recurrent neural networks for sub-optimal detection in code division multiple access (CDMA) systems. The focus is to propose a Hopfeld-based neural network which overcomes the problem of local minima. We investigate past models that are based on the Hopfield neural network (HNN). We highlight the ability of stochastic algorithms to achieve global minimum solutions and propose a stochastic model based on probabilistic firing mechanisms
Keywords :
Hopfield neural nets; code division multiple access; mobile radio; multiuser detection; simulated annealing; statistical analysis; CDMA; HNN; Hopfeld-based neural network; code division multiple access; global minimum stochastic models; mobile radio communications; multiuser detection; probabilistic firing mechanisms; recurrent neural networks; simulated annealing; sub-optimal detection; Artificial neural networks; Detectors; Hopfield neural networks; Intelligent networks; Mobile communication; Multiaccess communication; Multiuser detection; Neural networks; Recurrent neural networks; Stochastic processes;
Conference_Titel :
AFRICON, 2004. 7th AFRICON Conference in Africa
Conference_Location :
Gaborone
Print_ISBN :
0-7803-8605-1
DOI :
10.1109/AFRICON.2004.1406639