Composite Time-Frequency Analysis and Siamese Neural Network based Compound Interference Identification for Hopping Frequency System

In a hostile environment, interference identification plays an important role in protecting the authorized communication system and avoiding its performance degradation. In this paper, the interference identification problem for the frequency hopping communication system is discussed. Considering presence of multiple and compound interference in the frequency hopping system, in order to fully extracted effective features of the interferences from the received signals, a composite time-frequency analysis method based on both the linear and bilinear transform is proposed. The time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input into the deep neural network for identification. In particular, the Siamese neural network is adopted as the classifier to perform the interference identification. That is, the paired spectrograms are input into the two sub-networks of the Siamese neural network to extract the features of the paired spectrograms. The Siamese neural network is trained and tested by calculating the gap between the generated features, and the interference type identification is realized by the trained Siamese neural network. The simulation results confirm that the proposed algorithm can obtain higher identification accuracy than both traditional single time-frequency representation based approach and the AlexNet transfer learning or convolutional neural network based methods.

Multi-Agent Reinforcement Learning based Joint Cooperative Spectrum Sensing and Channel Access for Cognitive UAV Networks

Designing clustered unmanned aerial vehicle (UAV) communication networks based on cognitive radio (CR) and reinforcement learning can significantly improve the intelligence level of clustered UAV communication networks and the robustness of the system in a time-varying environment. Among them, designing smarter systems for spectrum sensing and access is a key research issue in CR. Therefore, we focus on the dynamic cooperative spectrum sensing and channel access in clustered cognitive UAV (CUAV) communication networks. Due to the lack of prior statistical information on the primary user (PU) channel occupancy state, we propose to use multi-agent reinforcement learning (MARL) to model CUAV spectrum competition and cooperative decision-making problem in this dynamic scenario, and a return function based on the weighted compound of sensing-transmission cost and utility is introduced to characterize the real-time rewards of multi-agent game. On this basis, a time slot multi-round revisit exhaustive search algorithm based on virtual controller (VC-EXH), a Q-learning algorithm based on independent learner (IL-Q) and a deep Q-learning algorithm based on independent learner (IL-DQN) are respectively proposed. Further, the information exchange overhead, execution complexity and convergence of the three algorithms are briefly analyzed. Through the numerical simulation analysis, all three algorithms can converge quickly, significantly improve system performance and increase the utilization of idle spectrum resources.