Complex-valued pipelined decision feedback recurrent neural network for non-linear channel equalisation
A novel complex-valued non-linear equaliser-based pipelined decision feedback recurrent neural network (CPDFRNN) is proposed in this study for non-linear channel equalisation in wireless communication systems. The CPDFRNN with low computational complexity, a modular structure comprising a number of modules that are interconnected in a chained form, is an extension of the recently proposed real-valued pipelined decision feedback recurrent neural equalisers. Each module is implemented by a small-scale complex-valued decision feedback recurrent neural network (CDFRNN). Moreover, a decision feedback part in each module can overcome the unstable characteristic of the complex-valued recurrent neural network (CRNN). To suit the modularity of the CPDFRNN, an adaptive amplitude complex-valued real-time recurrent learning (CRTRL) algorithm is presented. Simulations demonstrate that the CPDFRNN equaliser using the amplitude CRTRL algorithm with less computational complexity not only eliminates the adverse effects of the nesting architecture, but also provides a superior performance over the CRNN and CDFRNN equalisers for non-linear channels in wireless communication systems.
