Interference Management

Interference management in wireless communication networks aims to optimize resource allocation and mitigate signal degradation caused by overlapping transmissions. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks and deep reinforcement learning, along with graph representation learning, to predict and adapt to interference patterns in diverse scenarios, including 5G, Wi-Fi, and UAV networks. These advancements improve network performance metrics such as throughput and reliability, addressing challenges posed by increasing network density and the complexity of modern communication systems. The integration of uncertainty quantification into these models further enhances their robustness and trustworthiness.