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 structure. 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.

翻译：本文聚焦于提升室外无线系统对泛在扩展现实（XR）应用的支持能力，缩小其与当前室内无线传输性能的差距。我们提出一种混合知识数据驱动方法，用于无蜂窝大规模多输入多输出（MIMO）系统中的信道语义获取与多用户波束成形。具体而言，首先提出一种数据驱动的基于MLP-Mixer架构的多层感知机自动编码器进行信道语义获取，其中导频信号、用于信道语义嵌入的CSI量化器以及用于信道语义提取的CSI重建以端到端方式联合优化。其次，基于获取的信道语义，进一步提出一种知识驱动的深度展开多用户波束成形器，该方案能够在室外XR场景中实现良好频谱效率，并对不完美CSI具有鲁棒性。通过将基于传统逐次超松弛（SOR）的线性波束成形方案与深度学习相结合进行展开，所提波束成形方案能够自适应学习最优参数以加速收敛并提升对不完美CSI的鲁棒性。该深度展开波束成形方案既适用于配备全数字阵列的接入点（AP），也适用于混合模数阵列结构的AP。仿真结果表明，所提方案在提升信道获取精度、降低CSI获取与波束成形器设计复杂度方面均具有效性。所提波束成形方法在下行传输中仅需三次迭代即可达到约96%的收敛频谱效率性能，充分展现其在改善室外XR应用方面的效能与潜力。