arXiv:2610.23084v1 Announce Type: new Abstract: The growing diversity of wireless services in sixth-generation (6G) wireless communication systems calls for reusable channel representations that support multiple tasks across different propagation environments. However, deploying such representations between user equipment and the base station in frequency-division duplexing systems faces uplink feedback constraints. This paper proposes a distributed framework based on an angle-delay-domain large wireless model (LWMa) for multiple tasks. LWMa exploits angle-delay structure and delay truncation to improve feature extraction and reduce computational cost. A scenario-specific network inspired by Perceiver IO (input-output) compresses embeddings for channel reconstruction tasks, while other tasks use a low-dimensional compact global embedding. Embedding reconstruction training followed by channel-quality-guided fine-tuning preserves information useful for downstream processing. Simulations demonstrate that the proposed method supports effective channel reconstruction and multiple tasks under limited feedback with reduced deployment complexity. Further evaluations show that the recovered embeddings generalize to additional tasks and support split inference.