arXiv (All)
open access
Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks
Efficient physical resource block (PRB) allocation in 5G networks requires accurate demand forecasting. Conventional methods minimize symmetric error metrics (MAE, RMSE), ignoring the operational cost asymmetry where under-provisioning (service degradation) is far costlier than over-provisioning (wa
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks#919389
- Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR#926404
- Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits#1156328
- Long-Term Operational Planning Using Scenario-Based System Load Forecasting#1119213
- Data-Driven Adaptive Resource Allocation for Reliable Low-Latency Uplink Communications in Rural Cellular 5G Multi-Connectivity#138873
Record · ID 1012400
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive