FPGA realization of a neural-network-based nonlinear channel equalizer

The software simulation as well as the hardware implementation of equalizers for transmissions through nonlinear communication channels based on artificial neural networks structure is presented in this paper. We consider four-quadrature-amplitude-modulation technique as an example and compare the performance of two different structures of equalizer, namely, the linear least-mean-square-based equalizer (LIN) and the functional link artificial neural networks (FLANN). The learning curve and symbol error rate for the two structures are respectively evaluated by computer simulation. Besides, the systems have been implemented using field-programmable-gate-array devices. As FLANN uses functions to expand the dimensionality of the input signals, it has about the same system complexity as LIN. But FLANN can achieve fast processing speed under parallel processing structure. Simulation results have demonstrated that FLANN presents much better error performance than LIN, especially when the communication channel is highly nonlinear.