Predictive Interference Reporting for Adaptive Wireless Scheduling
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies due to outdated interference measurements leading to suboptimal scheduling and link adaptations, resulting in network congestion, degraded performance, and resource underutilization.
Innovation Solution
Utilizing artificial intelligence and machine learning models to generate predicted interference measurements, reporting only for subsets that meet specific conditions, enabling advanced scheduling and link adaptation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional interference measurement and reporting methods are used, then the system maintains simple measurement procedures, but the scheduling decisions become outdated and suboptimal due to interference changes over time
Solution Approach 1:
The system performs preliminary interference measurements over multiple time instances and uses machine learning models to predict future interference conditions before scheduling decisions are made. This advance prediction allows the network to proactively adapt scheduling strategies rather than reactively responding to outdated measurements, resolving the contradiction between measurement timeliness and decision accuracy.
Solution Approach 2:
The system implements a feedback mechanism where interference measurements from multiple time instances are continuously fed into machine learning models that update predictions over time. This continuous feedback loop enables the system to track interference dynamics and maintain accurate scheduling decisions despite time-varying interference conditions, addressing both the reliability and timeliness requirements.
2Loss of information
If interference measurements are reported for all resources, then complete interference information is provided for scheduling decisions, but the reporting overhead and power consumption increase significantly
Solution Approach 1:
The system extracts and reports only the most relevant interference information rather than reporting all measurements. Machine learning models identify and prioritize interference conditions that significantly impact scheduling decisions, filtering out redundant information. This selective reporting maintains decision-making quality while reducing uplink power consumption and signaling overhead.
Solution Approach 2:
The system applies different reporting strategies to different resource types or interference conditions based on their local characteristics. Resources with high interference variability or critical impact on scheduling trigger detailed reporting, while stable or less critical resources use reduced reporting. This localized quality approach ensures information completeness where needed while minimizing overall reporting overhead.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may obtain at least one interference measurement associated with at least one resource. The UE may obtain predicted interference information associated with the at least one resource, wherein the predicted interference information is based at least in part on the at least one interference measurement. The UE may transmit a report indicating the predicted interference information based at least in part on the predicted interference information satisfying at least one condition. Numerous other aspects are described.