Neural receiver for CPM signals

Neural networks have been successfully applied in many fields thanks to their learning and generalization capabilities and to the parallel processing and fault tolerance properties. Typical applications concern images processing, pattern recognition and digital signal processing, such as adaptive filtering and channel equalization. The authors propose the use of neural networks as digital receivers for continuous phase modulation (CPM). Simulation results refer to the European GSM digital cellular radio system. The neural receiver performance has been evaluated for coherent detection, considering an additive white Gaussian noise (AWGN) channel and compared with a maximum likelihood sequences estimator (MLSE) receiver based on the Viterbi algorithm. The paper also presents a hardware implementation of the proposed network based on a digital signal processor (DSP) and on a programmable gate array (PGA)