AI-Enabled RLC ARQ With Dynamic Parameter Selection
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Solution Overview
Problem
Wireless communication systems face inefficiencies in RLC layer operations due to excessive RLC SDU losses, increased latency, and resource wastage, particularly in scenarios relying on HARQ, leading to high variability in round-trip delay and excess memory usage, and inefficient resource utilization.
Innovation Solution
Implementing an AI/ML model to dynamically adjust parameters for processing RLC SDUs and PDUs, enabling efficient monitoring of congestion levels and reducing unnecessary retransmissions by discarding RLC SDUs and PDUs based on a learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ARQ procedures are used for RLC layer error correction, then reliability is improved through retransmissions, but latency increases and resource utilization deteriorates due to excessive retransmissions and memory usage
Solution Approach 1:
The patent dynamically adjusts ARQ parameters (such as retransmission thresholds, timeout values, and buffer sizes) based on real-time channel conditions and traffic patterns. This allows the system to reduce retransmissions in good conditions while maintaining reliability in poor conditions, thereby reducing latency and optimizing resource usage.
Solution Approach 2:
The system transitions from static ARQ parameters to dynamic parameter adjustment based on AI/ML model predictions. The AI model continuously learns from historical data and adapts parameters in real-time, enabling the system to respond to changing channel conditions without excessive retransmissions, thus resolving the contradiction between reliability and latency.
2Reliability
If traditional ARQ procedures are used for RLC layer error correction, then reliability is improved through retransmissions, but resource utilization deteriorates due to excess memory usage and inefficient resource allocation
Solution Approach 1:
The patent dynamically adjusts resource allocation parameters including buffer sizes, retransmission limits, and priority levels based on AI model predictions of channel conditions and traffic patterns. This enables efficient memory usage by allocating buffers only when needed and reducing retransmission attempts in favorable conditions, thereby improving resource utilization while maintaining reliability.
Solution Approach 2:
The AI/ML model enables the system to self-optimize resource allocation by automatically learning from historical performance data and making real-time decisions about parameter adjustment. This eliminates the need for manual configuration and allows the system to efficiently manage memory and retransmission resources based on actual network conditions.
3Reliability
If HARQ is used for error correction, then reliability is improved, but device complexity increases due to the need to monitor and manage multiple HARQ processes
Solution Approach 1:
The patent combines multiple HARQ process management functions into a unified AI-driven control mechanism. The AI model consolidates the monitoring and decision-making for multiple HARQ processes, reducing the complexity of individual process management while maintaining overall system reliability through intelligent coordination of retransmission attempts.
Data Source
AI summary
Methods, systems, and devices for wireless communication are described. A wireless device may receive a configuration including a first set of one or more parameters for an automatic repeat request (ARQ) procedure associated with a radio link control (RLC) entity of the wireless device. At least one parameter of the first set of one or more parameters is associated with a plurality of values. The wireless device may select a value of the plurality of values based at least in part on a second set of one or more parameters. The wireless device may transmit a negative acknowledgment (NACK) for a protocol data unit (PDU) of a set of one or more PDUs, and drop the PDU based at least in part on the at least one NACK and the selected value of the plurality of values for the ARQ procedure.