Waveform Level Intelligent Multi-Task Receiver With BiLSTM

Power of deep neural networks (NNs) has enabled tremendous effective applications for communication receiver design. In this letter, we demonstrate the possibility of constructing merely one NN to straightforwardly recover bit messages from waveform sequences of unknown modulation schemes without an additional timing synchronization module. The typical bidirectional LSTM (BiLSTM) structures are employed to tackle the continuous transmission issue. Moreover, complementary modulation classification layers are trained to sieve the valid bits of multiple modulation schemes. Thus, the whole process of our method can be deemed as multi-task learning. Simulation results reveal that the NN receiver can approach the bit error rate (BER) performance of theoretical BER values in the ideal channel. We further extend the method to harsh conditions with poor transceiver parameters and find that a certain gain can be obtained compared to traditional phase-lock-loop (PLL) based methods. The whole architecture is an effective joint synchronization and detection method, eliminating the complicated selection of appropriate algorithms for multiple modulation schemes, which further enhances the communication receiver's general intelligence level.