Accurate and real-time prediction of wireless channel conditions, particularly the Signal-to-Interference-plus-Noise Ratio (SINR), is a foundational requirement for enabling Ultra-Reliable Low-Latency Communication (URLLC) in highly dynamic Industry 4.0 environments. Traditional physics-based or statistical models fail to cope with the spatio-temporal complexities introduced by mobile obstacles and transient interference inherent to smart warehouses. To address this, we introduce Evo-WISVA (Evolutionary Wireless Infrastructure for Smart Warehouse using VAE), a novel synergistic deep learning architecture that functions as a lightweight 2D predictive digital twin of the radio environment. Evo-WISVA integrates a memory-augmented Variational Autoencoder (VAE) featuring an Attention-driven Latent Memory Module (LMM) for robust, context-aware spatial feature extraction, with a Convolutional Long Short-Term Memory (ConvLSTM) network for precise temporal forecasting and sequential refinement. The entire pipeline is optimized end-to-end via a joint loss function, ensuring optimal feature alignment between the generative and predictive components. Rigorous experimental evaluation conducted on a high-fidelity ns-3-generated industrial warehouse dataset demonstrates that Evo-WISVA significantly surpasses state-of-the-art baselines, achieving up to a 47.6\% reduction in average reconstruction error. Crucially, the model exhibits exceptional generalization capacity to unseen environments with vastly increased dynamic complexity (up to ten simultaneously moving obstacles) while maintaining amortized computational efficiency essential for real-time deployment. Evo-WISVA establishes a foundational technology for proactive wireless resource management, enabling autonomous optimization and advancing the realization of predictive digital twins in industrial communication networks.

翻译：在高度动态的工业4.0环境中，准确且实时地预测无线信道条件，尤其是信干噪比（SINR），是实现超可靠低时延通信（URLLC）的基础性要求。传统的基于物理或统计的模型难以应对智能仓库中移动障碍物和瞬态干扰所引入的时空复杂性。为此，我们提出了Evo-WISVA（基于VAE的智能仓库进化无线基础设施），这是一种新颖的协同深度学习架构，可作为无线环境的轻量级二维预测数字孪生。Evo-WISVA集成了一个记忆增强的变分自编码器（VAE），该编码器配备了一个注意力驱动的潜在记忆模块（LMM），用于鲁棒且上下文感知的空间特征提取；同时结合了一个卷积长短期记忆网络（ConvLSTM），用于精确的时间预测和序列细化。整个流水线通过联合损失函数进行端到端优化，确保了生成组件与预测组件之间的最优特征对齐。在高保真ns-3生成的工业仓库数据集上进行的严格实验评估表明，Evo-WISVA显著超越了现有最先进的基线方法，平均重构误差降低了高达47.6%。至关重要的是，该模型对未见过的、动态复杂性大幅增加（多达十个同时移动的障碍物）的环境表现出卓越的泛化能力，同时保持了实时部署所必需的摊销计算效率。Evo-WISVA为主动式无线资源管理奠定了基础技术，实现了自主优化，并推动了工业通信网络中预测性数字孪生的实现。