PEMNet: Towards Autonomous and Enhanced Environment-Aware Mobile Networks
🤖 AI Summary
This work addresses the challenge of jointly satisfying stringent latency, energy, and spectrum constraints in dynamic 5G/6G mobile networks, where existing approaches struggle to efficiently capture the spatiotemporal dynamics of both wireless channels and user traffic. To overcome this limitation, the paper proposes a Perceptual Embedding Map (PEM)—a lightweight, queryable, and localized environmental representation that, for the first time, enables joint embedding of channel and traffic states using only standard measurement reports, scheduling logs, and QoS logs. Requiring no additional signaling, PEM facilitates low-cost, cross-layer autonomous optimization across the physical, MAC, and network layers. The framework achieves an effective trade-off between representational fidelity and operational overhead, significantly outperforming site-level channel maps and digital twin-based solutions.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
environment-aware mobile networks
spatial-temporal variations
channel-traffic embedding
network autonomy
5G/6G optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Perception Embedding Map
joint channel-traffic embedding
environment-aware optimization
standard-compliant measurements
network autonomy
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