Predicting polarization state based on quaternion neural networks to facilitate channel prediction

Mobile communications are often affected by various fading phenomena. Since channel state information (CSI), important to overcome channel fading, is always changing over time, channel prediction is necessary to know the correct CSI. Current channel prediction methods are mainly based on the amplitude and phase of the received signal. However, because the polarization state of the propagating wave also changes with time, the polarization mismatch problem occurs between an arriving wave and a receiving antenna, resulting in lower communication quality. In this paper, we propose the prediction of polarization states by using a quaternion neural network (QNN), which makes full use of the geometric properties of polarization states represented on the Poincare sphere to achieve a high-precision prediction. The experimental results show that, by predicting the polarization state, we can obtain more accurate CSI and a lower bit error rate.