Abstract. The frequency-selective fading
inherent in multipath channels leads to the inter-symbol interference, which affects the data
transmission reliability. Implementation of the equalizer in data transmission
system exploits the inter-symbol interference thus increasing the energy
efficiency at the receiver. The existing decision feedback equalizer (DFE) and
maximum-likelihood sequence estimator (MLSE) have following disadvantages: DFE
has poor energy efficiency and MLSE is computationally complex. The neural
network equalizer algorithm proposed in this paper has proved to have better
performance in terms of the energy efficiency than DFE, while having the
similar computational complexity. The neural network equalizer was implemented
in the data transmission system along with carrier recovery, clock recovery,
automated gain control, matched filtering, interleaving, de-interleaving,
turbo-coding, turbo-decoding blocks. The energy efficiency of the neural network
equalizer was
experimentally estimated in different conditions: 1) single-path flat radio
frequency channel with AWGN. The effects of carrier frequency offset between
the transmitter and the receiver (up to 5kHz) and the Doppler shift (up to 150
Hz) were investigated; 2) stationary multipath channel environment. The performance of the equalizer with different
number of iterations was measured; 3) non-stationary fast-varying multipath channel. The impact of the step size in
learning procedure of the equalizer on the performance was investigated. In
addition, the combination of the proposed neural network equalizer and
turbo-code was investigated in all the mentioned channels in order to test the
equalizer’s performance in data transmission system.
Key
words: equalizer, multipath channel, neural networks, inter-symbol
interference.
References
1. H.C.Myburgh,
J.C.Olivier. Near-optimal
low complexity MLSE equalization. Wireless Communications and Networking Conference,
WCNC 2008. IEEE Xplore Conference, 2008, pp. 226-230. DOI:
https://doi.org/10.1109/WCNC.2008.45
2. H.C.Myburgh,
J.C.Olivier. Low complexity MLSE equalization in highly dispersive Rayleigh
fading channels. EURASIP Journal on Advances in Signal Processing.
(2010) 2010: 874874. DOI:
https://doi.org/10.1155/2010/874874
3. D.R.Valiullin, P.N.Zakharov. Neural network equalizer
with learning in a multipath channel. «Uspekhi sovremennoi radioelektroniki»
- Achievements of Modern Radioelectronics 2016, No. 11, pp. 200-202 (In
Russian)
For citation:
D. R.Valiullin,
P. N.Zakharov.
Experimental performance estimation of neural network equalizer with learning in
multipath radio frequency channel. Zhurnal Radioelektroniki - Journal of
Radio Electronics. 2017. No. 12. Available at
http://jre.cplire.ru/jre/dec17/10/text.pdf.