Sidelink Channel Access With QoS-Adaptive Sensing Windows
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Solution Overview
Problem
Current sidelink (SL) resource selection mechanisms in wireless communication networks, particularly in mode 2, lead to inefficient resource utilization due to continuous monitoring of physical SL control channels for incoming transport blocks, which is power-consuming and does not account for current usage characteristics.
Innovation Solution
Implementing dynamic sidelink autonomous channel access techniques, including predicting TB generation, adapting sensing and selection window sizes based on QoS, and using AI/ML models to optimize channel sensing and resource selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of physical SL control channels is implemented for incoming transport blocks, then resource selection reliability is improved, but power consumption increases and resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic sensing window adaptation where the UE adjusts the sensing window size and monitoring frequency based on predicted TB generation times and QoS requirements. Instead of continuous monitoring, the sensing window is dynamically configured to cover only the periods when TB arrivals are expected, thereby maintaining reliable resource selection while significantly reducing power consumption during idle periods
Solution Approach 2:
The patent employs TB arrival prediction mechanisms that use historical data and QoS parameters to forecast when transport blocks will be generated. By performing preliminary prediction of TB arrivals, the system can proactively configure sensing windows only during predicted active periods, avoiding unnecessary continuous monitoring and reducing overall power consumption while maintaining resource selection reliability
2Measurement precision
If continuous monitoring of physical SL control channels is implemented, then resource selection accuracy is improved, but resource utilization efficiency deteriorates due to unnecessary monitoring
Solution Approach 1:
The patent changes the monitoring parameters dynamically based on predicted TB generation patterns and QoS requirements. The sensing window size, monitoring frequency, and resource pool selection are adjusted as parameters to match actual traffic conditions, ensuring accurate resource selection during active periods while avoiding unnecessary monitoring during idle periods, thus improving resource utilization efficiency
Solution Approach 2:
The system transitions from static continuous monitoring to dynamic adaptive monitoring where the sensing window characteristics change based on real-time predictions of TB arrivals. This dynamic adjustment ensures that monitoring accuracy is maintained when needed while resource utilization efficiency improves by eliminating redundant monitoring activities
3Measurement precision
If sensing window size is increased to improve resource selection accuracy, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent dynamically adjusts the sensing window size parameter based on predicted TB generation patterns and QoS requirements. Instead of using a fixed large sensing window, the system adapts the window size to match the actual prediction horizon and traffic characteristics, achieving sufficient measurement precision while minimizing the time consumed by the sensing process
4Adaptability or versatility
If autonomous resource selection is implemented without network coverage, then adaptability is improved, but resource utilization efficiency deteriorates due to lack of coordination
Solution Approach 1:
The patent implements feedback mechanisms where UEs autonomously predict TB arrivals, adapt sensing window parameters, and select resources based on local observations and predictions. The system uses feedback from historical transmission patterns and QoS requirements to continuously optimize resource selection, achieving both autonomous adaptability and improved resource utilization efficiency without requiring network coverage
Data Source
AI summary
Described herein are solutions for dynamic sidelink (SL) autonomous channel selection access. A user equipment (UE) can initiate a sensing window based on a prediction of transport block (TB) generation. A SL-capable UE operating in mode 2 can predict an arrival of a TB in a buffer of the UE and can initiate monitoring of the channel for a configured duration before the predicted arrival of the TB. A sensing and selection window size can be adapted based on traffic quality of service (QoS). The UE can dynamically adapt the sensing and selection windows based on a predicted priority or QoS of the future TBs. These and many other features and examples are described herein.