Predictive UL Buffer Status Reporting for Low-Latency Scheduling

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Solution Overview

Problem

Current NR uplink scheduling mechanisms rely on buffer status reports (BSRs) sent by user equipment (UE), leading to high latency and extensive UL signaling, which is challenging for delay-intolerant services like URLLC.

Innovation Solution

Implementing artificial intelligence (AI) and/or machine learning (ML) at the UE to predict UL data requirements, allowing for proactive buffer status reporting (BSR) based on historical data and network configurations, enabling predictive BSRs to be sent when resources become available.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If traditional BSR-based uplink scheduling is used, then the system maintains simple scheduling mechanisms, but latency increases and signaling overhead increases

Engineering Contradiction:

ImprovelatencyVSAvoidscheduling mechanism complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by having the UE predict future UL data arrivals and send predictive BSRs before data actually arrives in the buffer. This allows the network to pre-allocate resources, eliminating the wait time between data arrival and scheduling decision, thus reducing latency while maintaining manageable complexity through structured prediction algorithms

Inventive Principle:

Principle #10Preliminary action

2Loss of energy

If traditional BSR-based uplink scheduling is used, then the system maintains simple reporting protocols, but signaling overhead increases

Engineering Contradiction:

Improvesignaling overheadVSAvoidprediction capability complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The patent applies self-service by enabling the UE to autonomously perform data arrival predictions using ML algorithms and generate predictive BSRs without extensive network intervention. The UE self-manages the prediction process, resource estimation, and timing of BSR transmissions, reducing the signaling burden on the network while the UE handles the complexity of prediction mechanisms

Inventive Principle:

Principle #25Self-service

3Productivity

If predictive BSR is implemented, then latency is reduced and scheduling efficiency improves, but UE processing complexity increases

Engineering Contradiction:

Improvescheduling efficiencyVSAvoidUE processing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies parameter changes by transforming the scheduling approach from reactive (based on current buffer status) to proactive (based on predicted future buffer status). The UE changes its operational parameters by implementing ML models that predict data arrival patterns, transforming how scheduling decisions are made and improving efficiency while managing complexity through parameter-based prediction

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260019866A1Methods and apparatuses for performing uplink (UL) data prediction and reporting predicted UL buffer status

Publication Date: 2026.01.15 INTERDIGITAL PATENT HOLDINGS INC

AI summary

A wireless transmit/receive unit (WTRU) may receive predictive buffer status report (BSR) configuration information. The predictive BSR configuration information may include information indicating an associated time frame and at least one triggering condition. The WTRU may predict an amount of uplink (UL) traffic corresponding to the associated time frame. The WTRU may predict BSR configuration information. The WTRU may determine that at least one triggering conditions has been met. The WTRU may send a predictive BSR, the predictive BSR comprising an indication of the predicted amount of UL traffic.