A Joint Jamming Detection and Link Scheduling Method Based on Deep Neural Networks in Dense Wireless Networks

The scheduling in a dense wireless network with interfering links is a very challenging problem, especially in the environments with additional jammers. In this work, we propose a joint jamming detection and link scheduling method based on deep neural networks (DNN). The proposed method admits a branched structure and mainly consists of two subnetworks, where the first subnetwork aims to detect and locate the jammer by utilizing the geographical information and received signal power, while the second one determines the link scheduling with the aid of the previously obtained jamming detection results. Furthermore, inspired by the multi-task learning method, we propose a hybrid-goal training approach to accelerate the training process. Numerical experiments have confirmed that the proposed DNN-based solution can achieve both superior jamming localization accuracy and highly competitive link scheduling performance.