Mode Selection for Device to Device Communication in Dynamic Network: A Statistical and Deep Learning Method

A major challenge in device to device (D2D) communications is determining the appropriate communication modes for each potential D2D pair. In dynamic networks, the continuous movement of devices increases the complexity of channel state modeling, which makes it difficult to predict the quality of network service and select appropriate switching thresholds, ultimately affecting the accuracy of D2D mode selection. This paper proposes a novel D2D mode selection method, which integrates deep learning with statistical learning and includes three modules: signal-to-interference-plus-noise-ratio (SINR) prediction, error analysis, and threshold selection. Specifically, the SINR prediction module employs the gated recurrent unit (GRU) method to predict future SINR values; the error analysis module applies a non-parametric method to construct a probability density function of the prediction error. The combination of these two modules provides a significant improvement in prediction value accuracy. Additionally, in the threshold selection module, two constraints are innovatively introduced to mitigate the problem of frequent switching: average reliability (AR) and probably correct reliability (PCR). Simulation results demonstrate that the proposed method achieves higher system throughput, longer D2D mode residence time, and a lower mode switching frequency compared to other methods.