Complex-Bilinear Recurrent Neural Network for Equalization of A Digital Satelite Channel  

Oleh:

Park, Dong-Chul ; Jeong, Tae-Kyun Jung

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 3 (2002), page 711-725.
Topik: Digital; complex - bilinear; neural network; equalization; digital satelite channel

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Equalization of satellite communication using complex - bilinear recurrent neural network (C - BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time - series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C - BLRNN) for dealing with the complex input values in the paper. C - BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C - BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex - MLPNN equalizer. The results show that the proposed C - BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex - MLPNN equalizer.

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