Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems

Researchers developed a self-evolving in-context learning framework for pilot-to-beamformer design in MU-MISO systems that adapts to multiple channel models without retraining. The scheme outperforms existing methods including WMMSE and Transformer-based approaches by integrating curriculum learning, self-evolving context datasets, and mismatch-aware extensions.

arXiv:2607.11970v1 Announce Type: new Abstract: We develop an enhanced in-context learning ICL framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output MU-MISO systems. The proposed scheme integrates the ICL-Transformer backbone with the pilot encoder-decoder network EDN and the beamformer EDN. A crucial feature of our ICL network is that it can handle multiple channel models without retraining, enabled by the construction of model-specific context datasets. To improve convergence and robustness, we introduce three key innovations: a a curriculum learning CL strategy that smoothly transitions from supervised LMMSE-labeled imitation to unsupervised sum-rate maximization, b a self-evolving mechanism that dynamically expands and refines the context datasets for all channel models during CL-based training, and c a mismatch-aware extension that incorporates several mismatches into the general ICL framework and bypasses explicit channel calibrations. Ablation studies validate the effectiveness of the in-context architecture and enhanced training strategies. Simulation results over diverse communication environments show that the proposed scheme is able to rapidly adapt to both seen and unseen channel models without gradient-based parameter updates, and can mitigate the mismatch issues via intelligent context constructions. Furthermore, our scheme consistently outperforms the existing beamforming schemes under pilot-based settings, including the WMMSE benchmark and the recent Transformer-based methods.