Paper Info
Title
Application of a Recurrent Neural Network to Space Deversity in SDMA and CDMA Mobile Communication Systems
Abstract
Linear and non-linear adaptive algorithms are investigated for Space Division Multiple Access (SDMA). SDMA is one of the emerging techniques for multiple access of users in mobile radio, which uses spatial distribution of users for their differentiation. The performance of the linear Square Root Kalman (SRK) algorithm for SDMA is compared to that of the non-linear Recurrent Neural Network (RNN) technique. The proposed SDMA-RNN technique is evaluated over Rician fading channels, and it shows improved Bit Error Rate (BER) performance in comparison with the linear SRK-based technique. The performance of SDMA-RNN is also compared with that of Code Division Multiple Access (CDMA) systems, showing that it could be used as a viable alternative scheme for multiple access of users. Finally, a Hybrid CDMA-SDMA system is proposed combining CDMA and SDMA-RNN systems. Hybrid CDMA-SDMA exhibits a very good potential for increase in the capacity and the performance of mobile communications systems.  
Year
DOI
Venue
2001
10.1007/s005210170005
Neural Computing and Applications
Keywords
DocType
Volume
adaptive space diversity combining,space division multiple access sdma,recurrent neural net- work,code division multiple access cdma,real-time recurrent learning algorithm
Journal
10
Issue
ISSN
Citations 
2
1433-3058
2
PageRank 
References 
Authors
0.42
9
2
Name
Order
Citations
PageRank
M. Benson120.42
Ramón Alberto Carrasco27810.67