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This paper proposes a self-evolving in-context learning framework for direct pilot-to-beamformer design in multi-user MISO systems, integrating a Transformer backbone with a pilot encoder-decoder network and curriculum learning to handle multiple channel models without retraining.
This Ph.D. thesis presents a self-contained textbook on differentiable ray tracing for radio propagation modeling, integrating automatic differentiation (e.g., JAX) into ray tracing pipelines to solve inverse problems and train ML models for next-generation wireless design.
Proposes a message-passing-based two-timescale Bayesian deep learning framework for joint channel and memory hardware impairment tracking in massive MIMO systems.
This paper presents a visionary framework for AI-native 6G networks, proposing a unified foundation model and collaborative multi-agent systems to achieve autonomous, resilient network management beyond fragmented 5G approaches.