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- W2020571005 abstract "Two neural networks for implementing an optimum multiuser detector (MUD) in code-division multiple-access (CDMA) channels are proposed. By viewing the optimum MUD problem as an NP-complete combinatorial optimum problem, we apply the discrete Hopfield neural network (DHNN) which has the ability for fast optimization computing to solve this kind of optimum problem. This is the first neural network approach. Also, by viewing the optimum MUD problem in a CDMA channel as an optimum nonlinear classification decision problem, we apply a backpropagation neural network (BPNN) which, through arbitrary nonlinear transformations and adaptive learning, can implement this classification decision optimally and adaptively. Both neural networks have certain application potentials in implementing the optimum MUD in CDMA systems. The DHNN is more suitable for the case when the users in the system are stationary, while the BPNN is suitable for the case when users in the system are mobile, such as mobile cellular CDMA communication systems or personal communication systems (PCS)." @default.
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- W2020571005 modified "2023-09-23" @default.
- W2020571005 title "Neural network approaches to implementation of optimum multiuser detectors in code-division multiple-access channels" @default.
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