As a critical component of sixth-generation (6G) wireless networks, ultra-reliable and low-latency communication (URLLC) is expected to support real-time and reliable information exchange in low-altitude environments. However, achieving URLLC often incurs significant resource overhead, including increased bandwidth consumption, higher transmit power, and denser access point (AP) deployment, which pose significant challenges to both spectral efficiency (SE) and energy efficiency (EE). Besides, existing iterative optimization algorithms are computationally intensive and struggle to meet the latency requirements of URLLC. To address these challenges, we propose a hybrid aerial-terrestrial cell-free massive MIMO (CF-mMIMO) network to support diverse services, along with a channel prediction network and a deep mixture of experts (MoE) network for uplink optimization. First, we design a channel prediction network (CP-Net) to mitigate channel aging caused by high-mobility user equipment (UE). CP-Net employs three Transformer-based sub-networks for aged channel state information (CSI) prediction, while a channel quality-aware loss function is introduced to improve the prediction accuracy of weak links. Based on the predicted CSI, we develop a deep MoE network (MoE-Net) for power allocation comprising three expert models targeting different objectives. Then, we introduce a weighted gating network (WT-Net) to learn an efficient adaptive combination of expert outputs. The proposed framework better captures heterogeneous UE requirements and improves communication performance under URLLC constraints. Numerical results demonstrate the effectiveness of the proposed method.

翻译：作为第六代（6G）无线网络的关键组成部分，超可靠低延迟通信（URLLC）被期望支持低空环境中的实时可靠信息交换。然而，实现URLLC往往需要付出显著的资源开销，包括增加带宽消耗、提升发射功率以及更密集的接入点（AP）部署，这对频谱效率（SE）和能量效率（EE）均构成重大挑战。此外，现有迭代优化算法计算强度大，难以满足URLLC的时延要求。针对这些挑战，我们提出了一种混合空天地无蜂窝大规模MIMO（CF-mMIMO）网络以支持多样化服务，并为此网络设计了信道预测网络和深度混合专家（MoE）网络用于上行链路优化。首先，我们设计了一个信道预测网络（CP-Net）来缓解高移动性用户设备（UE）导致的信道老化问题。CP-Net采用三个基于Transformer的子网络进行老化信道状态信息（CSI）预测，同时引入了一种信道质量感知损失函数以提高弱链路的预测精度。基于预测的CSI，我们开发了一个深度MoE网络（MoE-Net）用于功率分配，该网络包含三个针对不同目标的专家模型。然后，我们引入了一个加权门控网络（WT-Net）来学习专家输出的高效自适应组合。所提出的框架能更好地捕捉UE的异构需求，并在URLLC约束下提升通信性能。数值结果验证了所提方法的有效性。