RLC Status Report Timing for Low-Latency Retransmission
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing latency and efficient wireless signaling traffic due to the fixed expiration of the t-StatusProhibit timer for radio link control (RLC) status PDUs, which can lead to processing delays and failure to meet latency constraints.
Innovation Solution
Implementing a dynamic timing mechanism for RLC status PDUs using machine learning models to predict expiration times and event triggers, allowing for adaptive transmission based on RLC and HARQ parameters, key performance indicators, and machine learning models to optimize latency and signaling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed expiration time is used for the t-StatusProhibit timer, then the system structure is simple, but processing latency increases and latency constraints cannot be met
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a fixed timer expiration time to a dynamic expiration time that is determined based on machine learning model predictions. The system continuously predicts when the next status PDU should be transmitted and adjusts the timer expiration accordingly, allowing the timer to adapt to varying network conditions and traffic patterns, thereby reducing processing latency while maintaining appropriate system complexity.
2Quantity of substance
If the timer prohibits additional status PDUs for the entire first expiration time, then signaling traffic is controlled, but the system cannot respond to urgent retransmission needs
Solution Approach 1:
The patent applies dynamics by making the timer expiration time variable rather than fixed. The system determines a second expiration time based on machine learning predictions about when the next status PDU should be sent. This allows the system to maintain signaling traffic control while enabling timely responses to retransmission needs, as the timer can expire sooner if network conditions require it, thereby improving reliability without excessive signaling.
Solution Approach 2:
The patent implements feedback by using machine learning models to predict network conditions and status PDU transmission needs. The system continuously monitors network state and uses this feedback to dynamically adjust the timer expiration time, allowing it to respond to changing conditions and ensure reliable retransmission while controlling signaling traffic volume.
3Loss of time
If machine learning models are used to predict expiration times, then processing latency is reduced, but device complexity increases
Solution Approach 1:
The patent applies mechanics substitution by replacing the traditional mechanical timer-based approach with a machine learning-based prediction system. Instead of using fixed or simple countdown timers, the system employs ML models to predict optimal expiration times based on network conditions, thereby reducing processing latency. The complexity increase is managed by using efficient prediction algorithms and integrating them seamlessly into the existing protocol stack.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A wireless device may determine a dynamic expiration value for a timer (e.g., a radio link control (RLC) status protocol data unit (PDU) retransmission timer), determine one or more event triggers for retransmitting a status PDU, or both, and may retransmit an RLC status PDU based on the expiration value, the one or more event triggers, or both. The UE may determine the second expiration time, the one or more event triggers, or both, based on one or more RLC or hybrid automatic repeat request (HARQ) parameters, one or more predictions associated with RLC PDUs, or both, where a machine learning (ML) model may generate the one or more predictions, the UE may be configured with one or more parameters and key performance indicators (KPIs) for determining the second expiration value, the one or more event triggers, or both.