Noncoherent Energy-Modulated Massive SIMO in Multipath Channels: A Machine Learning Approach

Abstract

This article considers the design of the transmitter and receiver in a noncoherent massive single-input and multiple-output (SIMO) system over a multipath channel, representing a typical Internet-of-Things (IoT) scenario that consists of multiple single-antenna transmitters and one receiver with a large number of antennas. In particular, the autoencoders, which consist of multiple independent neural networks (NNs), are adopted at the transmitters and the receiver and are trained jointly, while working separately. To avoid the delicate design for mitigating the intersymbol interference (ISI) caused by multipath channels, the modulation schemes at the transmitters and the demodulation rule at the receiver are learned by the NNs over a limited number of channel samples. Moreover, the relationship between the number of channel samples and the performance of the trained transceiver is analyzed. The simulation results show that the proposed method achieves a lower error probability in comparison with the conventional optimization-based methods under typical channel conditions.