With 5G deployment and the evolution toward 6G, mobile networks must make decisions in highly dynamic environments under strict latency, energy, and spectrum constraints. Achieving this goal, however, depends on prior knowledge of spatial-temporal variations in wireless channels and traffic demands. This motivates a joint, site-specific representation of radio propagation and user demand that is queryable at low online overhead. In this work, we propose the perception embedding map (PEM), a localized framework that embeds fine-grained channel statistics together with grid-level spatial-temporal traffic patterns over a base station's coverage. PEM is built from standard-compliant measurements -- such as measurement report and scheduling/quality-of-service logs -- so it can be deployed and maintained at scale with low cost. Integrated into PEM, this joint knowledge supports enhanced environment-aware optimization across PHY, MAC, and network layers while substantially reducing training overhead and signaling. Compared with existing site-specific channel maps and digital-twin replicas, PEM distinctively emphasizes (i) joint channel-traffic embedding, which is essential for network optimization, and (ii) practical construction using standard measurements, enabling network autonomy while striking a favorable fidelity-cost balance.

翻译：随着5G的部署和向6G的演进，移动网络必须在严格的时延、能耗和频谱约束下，在高度动态的环境中做出决策。然而，实现这一目标依赖于对无线信道和业务需求时空变化的先验知识。这促使我们构建一种可联合、站点特定且在线查询开销低的无线电传播与用户需求表征方法。在本工作中，我们提出了感知嵌入地图（PEM），这是一个局部化框架，它将细粒度的信道统计信息与基站覆盖范围内的网格级时空业务模式共同嵌入。PEM基于标准合规的测量数据（如测量报告和调度/服务质量日志）构建，因此能够以低成本大规模部署和维护。集成到PEM中的这种联合知识支持跨物理层、MAC层和网络层的增强型环境感知优化，同时显著降低训练开销和信令负担。与现有的站点特定信道地图和数字孪生副本相比，PEM的显著特点在于：（i）对网络优化至关重要的联合信道-业务嵌入；（ii）利用标准测量数据进行实际构建，在实现网络自主性的同时，达成了良好的保真度与成本平衡。