5G • 5G-Advanced (Rel-18)
AI-Driven Uplink Power Control
Neural Path Loss Prediction and Transmission Power Optimization
Deep learning algorithm that anticipates fast-fading and shadowing dips to set exact UE transmit power, avoiding excessive battery drain and uplink interference.
Technical Explanation
Standard fractional power control uses slow filtered RSRP measurements that cannot react to rapid shadowing around buildings. Rel-18 AI uplink power control predicts immediate path loss variations milliseconds in advance, calculating the optimal PUSCH/PUCCH transmission power to meet target SINR while minimizing battery consumption and cross-cell interference.
Key Functions
- Predicts rapid channel path loss dips and shadowing spikes proactively
- Reduces average UE battery consumption by eliminating over-transmission power margins
- Minimizes inter-cell uplink interference across dense urban heterogeneous networks
- Maintains consistent target SINR at base station receiver antennas
Specifications
3GPP TR 38.843, TS 38.213 (Rel-18)
Interfaces
Uu (TPC Commands / PUSCH)
Related 5G Concepts
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