<ul><li>Researchers propose a novel deep learning-based scheme for efficient OFDM channel estimation in wireless communication systems.</li><li>The scheme includes a dual-attention-aided super-resolution neural network (DA-SRNN) for channel reconstruction and continual learning (CL)-aided training strategies for generalization.</li><li>The DA-SRNN utilizes a channel-spatial attention mechanism and a lightweight super-resolution module.</li><li>The CL-aided training strategies help the neural network adapt to different channel distributions and improve performance.</li></ul>

Continual Learning-Aided Super-Resolution Scheme for Channel Reconstruction and Generalization in OFDM Systems

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