Adaptive interference cancellation for DS-CDMA systems using neural network techniques

The objective of this study is to apply and investigate a neural network-ba sed decision feedback scheme for interference suppression in direct sequenc e code division multiple access (DS-CDMA) wireless networks. It is demonstr ated that a decision feedback functional link equalizer (DFFLE) in combinat ion with an eigenvector network ran closely approximate a Bayesian receiver with significant advantages, such as improved bit-error ratio (BER) perfor mance, adaptive operation, and single-user detection in multiuser environme nt. It is assumed that the spreading codes of the interfering users will be unknown to the receiver. This detector configuration is appropriate for do wnlink communication between a base station and a mobile user in a digital wireless network, The BER performance in the presence of interfering users is evaluated, The improved performance of such a DFFLE receiver for CDMA. i s attributed to the nonlinear decision boundary it evaluates for the desire d user. The receiver structure is also capable of rapid adaptation in a dyn amic communications scenario for which there is entry/exit of users and imp erfect power control. The convergence performance and error propagation of the DFFLE receiver are also considered and exhibit reasonable promise for t hird generation wireless DS-CDMA networks.