Base Station SR Max Retransmission Handling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In 5G communication networks, scheduling request (SR) max retransmissions lead to significant impacts on latency and user experience due to time, resource, signaling, and processing overhead, necessitating a reduction in SR max retransmissions to improve system Key Performance Indicators (KPIs).
Innovation Solution
A method involving a prediction algorithm to identify UEs at risk of SR max retransmissions, allocation of a dummy uplink grant, and prioritization of Resource Blocks (RBs) based on uplink control information (UCI) decoding success indicators to minimize SR max retransmissions, utilizing AI models and weighted moving average techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UE attempts multiple retransmissions of scheduling request (SR), then the probability of successful resource scheduling increases, but the latency and system overhead increase significantly
Solution Approach 1:
The base station performs preliminary identification of UEs likely to experience SR max retransmission failures using a prediction algorithm that analyzes historical data and UE behavior patterns. By detecting potential failures before they occur, the system can take preemptive actions to allocate resources in advance, preventing the need for multiple retransmissions and reducing latency while maintaining scheduling reliability.
2Reliability
If the UE enters random access procedure after SR max retransmission, then the scheduling failure is resolved, but the system overhead and processing load increase
Solution Approach 1:
The base station acts as an intermediary by maintaining a dedicated SR max retransmission UE pool that tracks and manages UEs experiencing scheduling failures. This pool serves as a buffer that allows the system to handle failed SRs systematically through targeted resource allocation rather than forcing all UEs into the complex random access procedure, thereby reducing overall system overhead while ensuring failure resolution.
Solution Approach 2:
The invention extracts problematic UEs experiencing SR max retransmission failures from the general UE population and places them in a separate SR max retransmission UE pool. This separation allows the base station to apply specialized handling mechanisms to these specific UEs, preventing them from consuming system resources through repeated random access procedures and reducing overall processing load.
3Reliability
If dummy uplink grants are allocated to prevent SR max retransmission, then the retransmission failures are reduced, but the resource allocation complexity increases
Solution Approach 1:
The base station employs a prediction algorithm that autonomously identifies UEs likely to experience SR failures and automatically allocates dummy uplink grants without requiring manual intervention or complex decision-making processes. The system uses historical data and established patterns to self-determine which UEs need preemptive resource allocation, reducing SR retransmission failures while keeping the allocation mechanism relatively simple and automated.
Data Source
AI summary
In an example embodiment, a method of reducing SR max retransmissions in a communication network is disclosed. The method includes: accessing an SR max retransmission UE pool stored in a storage of the base station, the SR max retransmission UE pool being a list of plurality of UEs which experienced SR max retransmission. The method further includes identifying at least one UE in the SR max retransmission pool that would enter an SR max retransmission state, based on a last PUSCH uplink time period and a SR max retransmission time period associated with the at least one UE. Further, the method includes allocating a dummy UL grant to the identified at least one UE.