title: Fast converging minimum probability of error neural network receivers for DS-CDMA communications.
abstract: We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.
authors.affiliation: Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. matyjas@eng.buffalo.edu
authors.forename: John D
authors.initials: JD
authors.lastname: Matyjas
authors.forename: Ioannis N
authors.initials: IN
authors.lastname: Psaromiligkos
authors.forename: Stella N
authors.initials: SN
authors.lastname: Batalama
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Medley
mesh_terms: D003142:Communication; D016571:Neural Networks, Computer; D011336:Probability; D012107:Research Design