An Informer-Based Signal Sequence Detector for Mobile Molecular Communication

The mobile molecular communication (MMC) system has promising prospects in the field of biomedical drug delivery. The signal detection plays significant roles in improving the performance of the MMC system with time-varying channel. However, when the channel state information is unknown, traditional detection strategies are not effective and the detectors using deep learning can exhibit better detection ability. This letter proposes a signal sequence detector for the MMC system based on the Informer model which is one of the deep learning-based detectors. The computation of autocorrelation coefficient of the input sequence is utilized to find the optimal sequence length. Based on the fixed length of the input sequence, we simplify the structure of the Informer model to reduce the inference time. Numerical results demonstrate that the detection ability of the Informer-based detector outperforms the Transformer-based detector for signal sequence detection in the MMC system.