Neural networks in digital data transmission
This paper applies to both the applications of artificial neural networks and to communications systems. To ensure a reliable digital data transmission over noisy insecure channels, information must undergo a coding process. Neural nets can be used as both coders and, much more interesting, decoders, outperforming by far performance achieved with algebraic methods when data is sent over an AWGN channel. In particular, we show that a three-layer feed-forward neural network is sufficient to decode any Reed-Solomon (RS) code. The simulation results for the RS[8,4,3] code over GF(22) show that soft decision neural network decoding after transmission over an AWGN channel could give 1.75dB coding gain relative to hard decision.
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