The recent advancements in wireless sensing and communication technologies have shown significant progress in addressing cross-domain adaptability, continuous learning, and dynamic interference management. Innovations in Wi-Fi sensing have focused on cross-domain adaptability, introducing methods like KNN-MMD for local distribution alignment, which enhances stability and performance in various sensing tasks. Continuous learning models, such as CCS, enable incremental updates to sensing capabilities without catastrophic forgetting, crucial for large-scale deployment scenarios. Dynamic interference prediction in 6G sub-networks has been advanced through discrete-time dynamic state space models, improving reliability and latency in ultra-dense environments. Additionally, novel transceiver designs for MIMO systems, such as FAG-IM, have demonstrated enhanced spectral efficiency and reduced receiver complexity. Deep learning approaches, like CSI-BERT2, have been pivotal in addressing data limitations for CSI prediction and classification, showcasing state-of-the-art performance. Distributed uplink rate-splitting multiple access (DU-RSMA) has introduced new principles for improving network density and quality of service in 6G networks. Statistical precoder design using graph neural networks has shown efficiency in massive MIMO systems with low pilot overhead. MIMO detection algorithms, leveraging Gaussian mixture models, have improved accuracy and computational efficiency. Lastly, transfer learning frameworks like T-ConGAN have significantly enhanced indoor localization performance by enabling the sharing of RSSI data across different environments.

Noteworthy papers include the introduction of KNN-MMD for cross-domain Wi-Fi sensing, which achieves high accuracy in one-shot scenarios, and the CCS model for continuous learning in wireless sensing, demonstrating superior performance in continuous model services.