Dynamic Null Tone Pattern Adaptation for Interference Measurement
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Solution Overview
Problem
Existing wireless communication systems, particularly in 5G NR, face challenges in accurately measuring inter-cell interference in the symbol time scale, which affects the efficiency and accuracy of resource allocation and interference management.
Innovation Solution
The implementation of configurable null tone patterns and reinforcement learning (RL) algorithms allows network entities and user equipment (UE) to dynamically adjust null tone patterns based on interference measurements, enhancing the accuracy and efficiency of interference measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If null tone patterns are configured for interference measurement, then measurement precision is improved, but device complexity increases due to the need for dynamic null tone configuration and RL algorithms
Solution Approach 1:
The patent implements dynamic null tone pattern configuration where the network node can adjust the pattern based on real-time interference conditions. The system transitions from static to dynamic null tone allocation, allowing adaptation to changing network environments while maintaining measurement accuracy.
Solution Approach 2:
The patent changes parameters such as null tone density, placement patterns, and configuration frequency to optimize interference measurements. By adjusting these parameters dynamically, the system achieves better measurement precision without requiring overly complex fixed configurations.
2Adaptability or versatility
If dynamic null tone pattern adjustment is implemented, then adaptability to interference conditions is improved, but loss of time increases due to measurement and reconfiguration overhead
Solution Approach 1:
The patent employs periodic interference measurements and null tone pattern adjustments rather than continuous reconfiguration. This periodic approach allows the system to adapt to changing conditions while minimizing the time spent on measurement and reconfiguration overhead.
Solution Approach 2:
The system implements feedback mechanisms where the network node receives interference measurement reports from UEs and adjusts null tone patterns accordingly. This feedback loop enables adaptive configuration while optimizing the timing of adjustments to minimize time loss.
3Productivity
If reinforcement learning algorithms are used for null tone optimization, then productivity of resource allocation is improved, but device complexity increases due to algorithm implementation requirements
Solution Approach 1:
The patent implements self-service mechanisms where the network node autonomously learns and optimizes null tone patterns through RL algorithms without requiring manual intervention. The system automatically adjusts configurations based on observed interference conditions, improving resource allocation efficiency while containing complexity within the network node's control plane.
Data Source
AI summary
Aspects presented herein may improve interference measurements for dynamic and/or bursty transmissions. In one aspect, a UE receives a configuration of a null tone pattern from a network node, where the configuration of the null tone pattern includes a set of null tones associated with a set of resources, where at least one null tone in the set of null tones corresponds to at least one resource in the set of resources. The UE measures interference corresponding to the set of resources associated with the null tone pattern. The UE transmits at least one of a first indication of an adjusted null tone pattern based on the interference corresponding to the set of resources or a second indication of the interference corresponding to the set of resources, where at least one of the first indication or the second indication is transmitted to the network node.