Predictive Power Headroom Reporting for Low-Latency Uplink Control
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Solution Overview
Problem
Existing techniques for power headroom report (PHR) in 5G NR systems introduce latency in uplink power control due to reliance on estimated path loss thresholds, exacerbating delays in uplink transmission adjustments.
Innovation Solution
Calculating path loss based on predicted channel metrics to generate a predictive power headroom report (PHR) that indicates power headroom at a future time instance, reducing latency in uplink power control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If path loss is calculated based on estimated path loss thresholds, then the system maintains simplicity in power control, but latency increases in uplink transmission adjustments
Solution Approach 1:
The patent applies preliminary action by predicting channel metrics (such as path loss) at a future time instance before actual transmission occurs. The network entity calculates predicted path loss based on current channel conditions and projects them forward in time, allowing the UE to prepare power headroom reports in advance. This proactive approach eliminates waiting time for actual channel measurements at the future time instance, thereby reducing latency in uplink power control while maintaining accuracy.
2Loss of time
If predictive channel metrics are used for PHR, then latency in uplink power control is reduced, but calculation complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming the approach from using actual measured channel metrics at future time instances to using predicted metrics derived from current measurements. The network entity changes the parameter representation from raw measured values to projected values based on prediction algorithms (such as linear extrapolation or machine learning models). This allows the system to work with simplified predictive parameters rather than complex real-time measurements, reducing computational burden while maintaining the latency benefits of prediction.
Data Source
AI summary
Certain aspects relate to power headroom report (PHR) based on a predicted channel metric. For example, an apparatus may obtain, from a network entity, a reference signal and a configuration indicating a set of one or more parameters. The apparatus may calculate a path loss based on a predicted channel metric of the reference signal. The apparatus may generate a power headroom report (PHR) based on at least one of the set of one or more parameters, a set of one or more conditions, or the calculated path loss, wherein the PHR indicates at least a power headroom (PH) at a future time instance. The apparatus may output the PHR for transmission to the network entity, the output being based on the set of one or more conditions.


