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This paper focuses on advancing outdoor wireless systems to better supportubiquitous extended reality (XR) applications, and close the gap with currentindoor wireless transmission capabilities. We propose a hybrid knowledge-datadriven method for channel semantic acquisition and multi-user beamforming incell-free massive multiple-input multiple-output (MIMO) systems. Specifically,we firstly propose a data-driven multiple layer perceptron (MLP)-Mixer-basedauto-encoder for channel semantic acquisition, where the pilot signals, CSIquantizer for channel semantic embedding, and CSI reconstruction for channelsemantic extraction are jointly optimized in an end-to-end manner. Moreover,based on the acquired channel semantic, we further propose a knowledge-drivendeep-unfolding multi-user beamformer, which is capable of achieving goodspectral efficiency with robustness to imperfect CSI in outdoor XR scenarios.By unfolding conventional successive over-relaxation (SOR)-based linearbeamforming scheme with deep learning, the proposed beamforming scheme iscapable of adaptively learning the optimal parameters to accelerate convergenceand improve the robustness to imperfect CSI. The proposed deep unfoldingbeamforming scheme can be used for access points (APs) with fully-digital arrayand APs with hybrid analog-digital array. Simulation results demonstrate theeffectiveness of our proposed scheme in improving the accuracy of channelacquisition, as well as reducing complexity in both CSI acquisition andbeamformer design. The proposed beamforming method achieves approximately 96%of the converged spectrum efficiency performance after only three iterations indownlink transmission, demonstrating its efficacy and potential to improveoutdoor XR applications.

Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO