Interference Mitigation

Interference mitigation focuses on improving the reliability and performance of communication systems and machine learning models by reducing the negative impact of unwanted signals or noise. Current research emphasizes the use of advanced signal processing techniques, such as rate-splitting multiple access and convolutional neural networks, along with machine learning approaches like deep reinforcement learning and attention-guided incremental learning, to optimize resource allocation and enhance signal detection. These methods are being applied across diverse fields, including 5G networks, indoor communication systems, automotive radar, and federated learning, demonstrating significant improvements in accuracy, robustness, and efficiency. The resulting advancements have substantial implications for improving the performance and reliability of various technologies.