Vector equalization based on continuous-time recurrent neural networks

The problem of vector equalization based on recurrent neural networks as suboptimum scheme is considered from the analog signal processing point of view. We distinguish between discrete-time recurrent neural networks (DTRNNs) and continuous-time ones (CTRNNs). In contrast to the CTRNNs, the DTRNNs have been extensively investigated and implemented for the vector equalization task with good results for channels with little to moderate interference. However, the growing demand for jointly high date rates and power efficiency (green communications) make analog signal processing an interesting topic in the field of wireless transmission. Analog signal processing possesses the potential to be fast and/or power efficient compared with digital signal processing techniques. In addition, very-large-scale integration (VLSI) technology has been shown to fit well as implementation medium for neural networks. All these facts motivate the investigation of CTRNNs as vector equalizer. We show in this paper computer simulations, circuit simulations are in progress.