PEMNet: Towards Autonomous and Enhanced Environment-Aware Mobile Networks

TL;DR

PEMNet introduces a localized, low-cost framework that embeds detailed radio and traffic data for environment-aware optimization in future mobile networks, reducing overhead and enhancing decision-making.

Contribution

The paper presents PEM, a novel site-specific embedding map built from standard measurements, enabling autonomous, environment-aware network optimization with low overhead.

Findings

Supports PHY, MAC, and network layer optimization.

Reduces training overhead and signaling.

Achieves high fidelity with low cost.

Abstract

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.