Deep In-Context Learning (ICL) for Wireless Communications

Sammanfattning

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, the spectral overhead required by orthogonal Demodulation Reference Signals (DMRS) limits overall system capacity. Traditional linear estimators experience severe degradation under high pilot sparsity or non-orthogonal superposition. To address these limitations, this thesis proposes a Physics-Guided In-Context Learning (ICL) receiver that jointly performs channel estimation (CE) and MIMO detection. By formulating reference signals as contextual prompts through a tokenization scheme, the proposed artificial intelligence (AI) receiver circumvents the strict requirement of classical spatial orthogonality. The architecture adopts an unfolded iterative receiver design, where a Transformer-based backbone with Virtual Width Networks (VWN), Tensor Product Attention (TPA), and Mixture-of-Experts (MoE) layers performs joint representation learning for CE and detection. A differentiable Physics-Guided Feature Construction (PGFC) bridge converts intermediate outputs into soft symbol estimates and explicit physical features, which are then fed into subsequent stages for refinement. Additionally, a novel resampling strategy is adopted during inference to improve detection reliability without requiring any network retraining. The proposed architecture is evaluated through link-level simulations adopting standard 3rd Generation Partnership Project (3GPP) fading channels (e.g., EPA and ETU) across various MIMO-OFDM configurations and modulations. Our results indicate that the proposed architecture approaches Maximum Likelihood Detection (MLD) in baseline configurations. The receiver maintains detection capability under sparse pilot conditions where standard 5G-NR baselines become intractable, and it mitigates pilot-data self-interference in superimposed DMRS scenarios. By achieving reliable detection with reduced pilot overhead, the framework translates DMRS overhead savings into throughput improvements, presenting a scalable AI receiver architecture for spectrally efficient future wireless networks.