The field of wireless communications and game theory is rapidly evolving, with a focus on developing innovative solutions to complex problems. Recent research has explored the application of deep learning techniques to improve the efficiency and effectiveness of wireless communication systems. This includes the use of graph neural networks to predict channel state information and optimize beamforming, as well as the development of new modulation schemes and autoencoder architectures. Additionally, game-theoretic approaches are being used to analyze and improve the performance of pursuit-evasion games and network flow games. Notable papers in this area include: Fast and the Furious: Hot Starts in Pursuit-Evasion Games, which introduces a novel approach to positioning pursuers in pursuit-evasion games using graph neural networks. CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction, which presents a hybrid deep learning architecture for channel state information prediction that achieves superior prediction accuracy with substantially lower computational cost. Going with the Flow: Approximating Banzhaf Values via Graph Neural Networks, which proposes a novel learning-based approach using graph neural networks to approximate Banzhaf values in cardinal network flow games. Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks, which proposes a novel architecture that explicitly separates processing of a node's own transmission probability from neighbor interference effects.