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