Joint Data and Model Driven Channel-Free Signal Detection based Learned Factor Graph

We propose a learned factor graph based on convolutional neural network (CNN) and Bi-directional Long Short Term Memory (BiLSTM) to realize signal detection under the scenario of no channel model. It can solve the inevitable over-reliance on channel state information (CSI) of model-based signal detection methods and avoid the shortcomings of large training scale of general data-driven methods by using relatively small training samples. The proposed method uses a network of CNN-BiLSTM structure with strong learning capabilities to determine the statistical relationship of the channel model which is what traditional model-based methods rely on. Based on above, the parameter estimation (Gaussian mixture model considering Akaike information criterion) and non-parametric estimation (adaptive kernel density) are adopted to learn a factor node together. The simulations show that, the proposed method can guarantee the accuracy of signal detection and robustness to the training of imperfect CSI.