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

The objective of this study is to apply and investigate a neural network-based decision feedback scheme for interference suppression in direct sequence code division multiple access (DS-CDMA) wireless networks. It is demonstrated that a decision feedback functional link equalizer (DFFLE) in combination with an eigenvector network can closely approximate a Bayesian receiver with significant advantages, such as improved bit-error ratio (BER) performance, adaptive operation, and single-user detection in a multiuser environment. It is assumed that the spreading codes of the interfering users will be unknown to the receiver. This detector configuration is appropriate for downlink 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 is attributed to the nonlinear decision boundary it evaluates for the desired user. The receiver structure is also capable of rapid adaptation in a dynamic communications scenario for which there is entry/exit of users and imperfect power control. The convergence performance and error propagation of the DFFLE receiver are also considered and exhibit reasonable promise for third generation wireless DS-CDMA networks.