Fast Converging Minimum Probability of Error Neural Network Receivers for DS-CDMA Communications  

Oleh:

Batalama, S. N. ; Matyjas, J. D. ; Psaromiligkos, I. N. ; Medley, M. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 2 (Mar. 2004), page 445-454.
Topik: cdma; fast - converging; probability; error neural network; DS - CDMA communications

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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