Neural-net based receiver structures for single- and multi-amplitude signals in interference channels

This paper presents analysis and performance evaluation results for several neural-net based receiver structures which effectively combat additive channel interference, such as co-channel interference (CCI) and adjacent channel interference (ACI). Although the idea of employing neural net based receivers for interference channels is not new, the novel technical contributions of the authors´ paper can be summarized as follows. (i) Propose, analyze and evaluate a training algorithm for Nyquist filtered single- and multi-amplitude signals which is based upon a novel non-uniform signal sampling technique. (ii) Propose and evaluate neural net structures employing a novel non-linear activation function for the detection of multi-amplitude signals. (iii) Present novel bit error rate (BER) performance evaluation results for coherent and noncoherent single- and multi-amplitude signals, including binary phase shift keying (BPSK), quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM), operated in generalized CCI and ACI channels. The authors´ research has demonstrated that, as compared to more conventional detection techniques, the proposed neural net receivers provide significant performance improvements in CCI and/or ACI channels. Their tolerance for inaccuracies in symbol timing synchronization also makes them good candidates for practical modem implementation