5G NR Downlink Scheduling with Delay Tracking for XR Reliability
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Solution Overview
Problem
Existing 5G scheduling mechanisms fail to adequately address the unique challenges of extended reality (XR) applications, which require ultra-low latency, high reliability, and dynamic resource allocation, lacking the necessary flexibility, granularity, and context-awareness.
Innovation Solution
A system and method integrating Delay Tracking Queues (DTQs) and Model Predictive Control (MPC) for dynamic resource allocation, ensuring precise delay tracking and adaptive priority computation to optimize downlink scheduling in 5G NR networks, prioritizing data units based on their remaining time to deadline and dynamically adjusting user priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing 5G scheduling mechanisms are used, then general network throughput is maintained, but delay reliability for XR applications deteriorates due to lack of ultra-low latency support
Solution Approach 1:
The patent segments the scheduling mechanism into multiple components: Delay Tracking Queues (DTQs) that separate data units by delay urgency, and Model Predictive Control (MPC) that independently optimizes resource allocation. This segmentation allows the system to specifically address XR application requirements without compromising overall network scheduling.
Solution Approach 2:
The patent implements dynamic scheduling where user priorities and resource allocations are continuously adjusted based on real-time delay tracking and predictive modeling. The MPC component dynamically optimizes resource allocation decisions to meet varying XR application demands while adapting to changing network conditions.
2Reliability
If dynamic resource allocation is implemented for XR applications, then delay reliability improves, but computational complexity increases due to MPC and adaptive priority computation
Solution Approach 1:
The patent performs preliminary actions by using MPC to predict future resource requirements and delay violations before they occur. The delay tracking mechanism proactively identifies data units at risk of missing deadlines, allowing the scheduler to allocate resources in advance rather than reacting to delays after they occur.
Solution Approach 2:
The patent implements feedback loops where delay tracking information from DTQs continuously feeds into the MPC optimization process. The adaptive priority computation uses feedback from actual delay performance to dynamically adjust user priorities, creating a closed-loop system that self-optimizes while managing computational load through iterative improvement.
3Loss of time
If Delay Tracking Queues are used to prioritize data units, then timely transmission is improved, but queue management complexity increases
Solution Approach 1:
The patent applies local quality by creating specialized DTQ structures with specific properties for handling different types of data units based on their delay requirements. Each queue is optimized for its specific function (tracking, prioritization, deadline monitoring), allowing simple localized operations within each queue while achieving complex overall scheduling behavior.
Data Source
AI summary
This invention discloses a novel downlink scheduling scheme to maximize the number of XR users who meet their strict delay reliability requirements in 5G networks. Each XR user is assigned a predefined delay bound, within which Data Units (DUs)—encompassing packets, frames, or Service Data Units (SDUs)—must be transmitted. Failure to transmit within this bound results in a delay violation, which can degrade the user's experience. Additionally, each user has a specified reliability threshold, denoted as X %, indicating the minimum percentage of DUs that must be successfully delivered within the delay bound to meet the user's quality expectations. The proposed scheduling scheme ensures that the highest possible number of XR users achieve their required delay reliability. To accomplish this, the scheme integrates two critical components: (a) a delay tracking mechanism, and (b) a downlink scheduling strategy that optimizes scheduling based on real-time delay information.