Proactive Scheduling Request Prediction for LTE Latency Reduction
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Solution Overview
Problem
Current LTE scheduling methods, such as dynamic and predictive scheduling, face challenges in reducing latency and resource wastage, especially under varying traffic loads, due to the need for explicit scheduling requests and grants, which can lead to delays and inefficient resource allocation.
Innovation Solution
A method where user equipment predicts data arrival based on traffic characteristics and sends scheduling requests proactively before data enters the transmission buffer, allowing for earlier resource allocation and reducing latency, independent of traffic load situations, by utilizing a predicting unit within the user equipment and a network node to set priority weights for resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic scheduling with explicit scheduling requests is used, then resource allocation flexibility is improved, but latency increases due to the time required for SR and grant exchange
Solution Approach 1:
The base station performs preliminary scheduling by allocating uplink resources to the UE in advance based on predicted traffic patterns, before the UE actually has data to transmit. This eliminates the need for SR-grant exchange latency while maintaining resource allocation flexibility through adaptive prediction algorithms.
Solution Approach 2:
The system uses self-service scheduling where the base station autonomously allocates resources based on traffic predictions without requiring explicit scheduling requests from the UE. The prediction mechanism automatically adapts to traffic patterns and adjusts resource allocation accordingly.
2Productivity
If predictive scheduling is used to reduce latency, then scheduling speed is improved, but resource wastage increases when traffic predictions are inaccurate
Solution Approach 1:
The system implements feedback mechanisms where actual traffic patterns are compared with predictions, and the prediction algorithm is continuously refined based on this feedback. This reduces resource wastage by improving prediction accuracy over time while maintaining fast scheduling speeds.
Solution Approach 2:
The predictive scheduling system dynamically adjusts resource allocation based on real-time traffic observations and changing patterns. The prediction model adapts its parameters dynamically to match actual traffic behavior, reducing resource wastage while maintaining scheduling speed.
3Loss of energy
If semi-persistent scheduling is used to save PDCCH resources, then resource efficiency is improved, but latency increases for delay-sensitive traffic due to fixed scheduling intervals
Solution Approach 1:
The system performs preliminary resource allocation based on predicted traffic arrival times, allowing delay-sensitive traffic to be scheduled immediately upon arrival without waiting for the next semi-persistent scheduling interval. This reduces latency while maintaining PDCCH resource efficiency through targeted predictions.
Data Source
AI summary
Method and user equipment for requesting scheduling resources from a serving radio network node, the method comprises obtaining traffic characteristics associated with radio communication between the radio network node and the UE. The method further comprises predicting, based at least on the obtained traffic characteristics, data to arrive in a transmission buffer; and transmitting a scheduling request to the network node before said data arrives in said transmission buffer.