In this thesis, we have proposed the generalized MLP/BP-based DFEs and applied to wireline communications. Moreover, we also apply the MLP/BP-based soft DFEs with bit-interleaving soft decision channel coding to wireless communications. For further improvements, we are deducing a general form of MLP/BP neural networks in the complex domain. It should be applied to wireless communications.
Now, we are tuning the training parameters of the GMLP/BP-based soft DFEs to solve the overfitting problem for wireless communications. Moreover, the MIMO MLP/BP-based soft DFEs and the MIMO GMLP/BP-based soft DFEs will be developed for wireless communications.
To realize the proposed neural-based equalization schemes for different applications, several hardware designs are currently under investigation. The MLP/BP-based DFEs and the GMLP/BP-based DFEs can be applied to wireline high-speed peripheral interface.
The MIMO MLP/BP-based DFEs and the MIMO GMLP/BP-based DFEs should be applied to high-speed system bus. The MLP/BP-based soft DFEs with bit-interleaving TCM are possible solutions in wireless communications. Although the architecture of the proposed equalization schemes is more complex than that of the conventional methods, we think that the rapid progress of VLSI technology will afford more complex approaches for better performance. Also, we can use digital signal processors to realize the proposed neural-based equalization schemes as soft-define radios (SDR). The implementations of the proposed neural-based channel equalization schemes are our research activities in the
future.
Our approaches show very good results for channel equalization applications.
Further research activities have been initiated to explore how to improve and implement such techniques for wireline and wireless communications. Still, there are many open problems for further research activities.
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