Abstract:
In this article, it is proposed that a Hopfield neural network (HNN) can be used to jointly equalize and decode
information transmitted over a highly dispersive Rayleigh fading multipath channel. It is shown that a HNN MLSE
equalizer and a HNN MLSE decoder can be merged in order to realize a low complexity joint equalizer and decoder, or
turbo equalizer, without additional computational complexity due to the decoder. The computational complexity of
the Hopfield neural network turbo equalizer (HNN-TE) is almost quadratic in the coded data block length and
approximately independent of the channel memory length, which makes it an attractive choice for systems with
extremely long memory. Results show that the performance of the proposed HNN-TE closely matches that of a
conventional turbo equalizer in systems with short channel memory, and achieves near-matched filter performance in
systems with extremely large memory.
