Predictive Buffer State Reporting for Low-Latency Uplink Scheduling
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Solution Overview
Problem
Existing buffer state reporting (BSR) mechanisms in wireless communication systems introduce latency in data transmission from terminal devices to network nodes due to the need for actual assignment of data to logic channels before calculating buffer sizes.
Innovation Solution
Implementing a predictive mechanism at the terminal device to estimate buffer sizes based on various factors, including data size, type, transmission rate, user behavior, and traffic patterns, using AI algorithms like LSTM or Arima models, and transmitting a predicted BSR to the network node.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the terminal device waits for actual data assignment to logic channels before calculating and reporting buffer size, then the buffer size calculation is accurate, but the data transmission latency increases
Solution Approach 1:
The terminal device performs preliminary prediction of the buffer size using AI/ML models before actual data assignment to logic channels. The model processes input features (data size, type, transmission rate, user behavior, traffic patterns) to forecast buffer size, allowing the device to report buffer status proactively and receive scheduling grants earlier, thus reducing latency while maintaining reasonable accuracy
Solution Approach 2:
The system implements feedback mechanisms where the terminal device compares predicted buffer sizes with actual buffer sizes after data assignment. This feedback is used to retrain and refine the AI/ML models, continuously improving prediction accuracy over time while maintaining low latency performance
2Loss of time
If the terminal device uses AI algorithms for predictive buffer size estimation, then the data transmission latency is reduced, but the device complexity increases
Solution Approach 1:
The terminal device integrates multi-functional AI/ML capabilities into a unified prediction framework. The same model architecture processes diverse input features (data characteristics, user behavior, traffic patterns, radio conditions) to generate buffer size predictions, reducing overall system complexity compared to having separate mechanisms for each input type
Solution Approach 2:
The system dynamically adjusts model parameters and input feature sets based on operating conditions. For example, the model can switch between different input combinations or complexity levels depending on available computational resources, traffic patterns, and accuracy requirements, allowing flexible adaptation without permanently increasing device complexity
3Productivity
If the terminal device transmits predicted buffer size with probability information, then the network scheduling efficiency is improved, but the signaling overhead increases
Solution Approach 1:
The terminal device selectively transmits probability information only when it provides meaningful scheduling benefits. The model evaluates whether including probability data will significantly improve network scheduling decisions, and only then includes it in the BSR report, avoiding unnecessary signaling overhead while maintaining scheduling efficiency where it matters
Solution Approach 2:
The probability information is efficiently encoded and nested within the existing BSR message structure. Rather than creating separate signaling messages, the probability data is integrated into the buffer status report format, utilizing available message space and reducing overall signaling overhead through compact representation
Data Source
AI summary
Embodiments of the present disclosure provide methods and apparatus for buffer state report. A method performed at a terminal device may comprise: determining a time duration, wherein a buffer size associated with data to be transmitted during the time duration is to be predicted by the terminal device; predicting the buffer size; and transmitting a scheduling request or a buffer state report indicating the predicted buffer size to a network node. A method performed at a network node may comprise: receiving a scheduling request or a buffer state report indicating a predicted buffer size associated with data to be received during a time duration from a terminal device; and transmitting a grant for the data according to the received scheduling request or buffer state report. The latency of data transmission from the terminal device to the network node may be reduced.