Radio Access Network Scheduling With Real-Time L1 Adaptation
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Solution Overview
Problem
Current L1 scheduling in radio access networks is static and fixed, failing to adapt to changing network conditions and resource availability, leading to inefficient resource utilization and potential system breakdowns, especially in environments requiring on-demand high-performance services.
Innovation Solution
A method for dynamically modifying L1 scheduling based on real-time network usage data, using AI/ML algorithms to simulate and adjust scheduling parameters according to environmental changes and hardware capabilities, enabling dynamic resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static and fixed L1 scheduling is used, then system simplicity is maintained, but resource utilization efficiency deteriorates under changing network conditions
Solution Approach 1:
The patent transforms the static L1 scheduling system into a dynamic one by introducing runtime modification capabilities. The scheduler instructions are no longer fixed at compile time but can be adjusted during operation based on network conditions, hardware performance feedback, and workload characteristics. This allows the system to adapt scheduling parameters dynamically while maintaining a relatively simple base architecture.
Solution Approach 2:
The patent implements feedback mechanisms where the scheduler monitors actual hardware performance, network conditions, and resource utilization metrics. This feedback is used to iteratively refine and modify scheduling instructions, enabling the system to learn from operational data and continuously optimize resource allocation without requiring complete system redesign.
2Adaptability or versatility
If static scheduling parameters are used, then system stability is maintained, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The patent employs preliminary action by pre-defining multiple scheduling instruction sets with different parameters and characteristics. When network conditions change, the system can rapidly switch between pre-prepared instruction sets or combine them, avoiding the need for complex real-time calculations that might compromise stability. This preparation in advance enables quick adaptation while maintaining systematic control.
Solution Approach 2:
The patent achieves adaptability by modifying scheduling parameters such as time slot allocations, frequency resource distributions, and priority weights without changing the fundamental scheduling architecture. These parameter adjustments allow the system to respond to varying network conditions while preserving the stable core scheduling logic and maintaining reliability.
3Productivity
If manual curation of L1 scheduling parameters is used, then implementation simplicity is maintained, but performance optimization deteriorates
Solution Approach 1:
The patent enables the scheduling system to self-optimize by automatically generating and adjusting scheduling instructions based on observed performance data and network conditions. The system performs self-diagnosis and self-tuning, reducing the need for manual parameter curation while achieving superior performance optimization through data-driven decisions and automated adjustment mechanisms.
Solution Approach 2:
The patent uses preliminary simulation and training phases where scheduling strategies are pre-optimized using historical data and predictive models. This preliminary action allows the system to start with pre-tuned parameters that have already been optimized through offline analysis, reducing implementation complexity while maintaining high performance potential that can be further refined during operation.
4Speed
If fixed frame slot scheduling is used, then timing predictability is maintained, but response to on-demand services deteriorates
Solution Approach 1:
The patent introduces dynamic time slot allocation where the duration, timing, and structure of frame slots can be adjusted based on real-time service demands. High-priority on-demand services can trigger immediate scheduling opportunities or compressed time slots, allowing the system to respond rapidly while maintaining predictable scheduling for standard services through the preserved frame structure.
Data Source
AI summary
A method of scheduling data transmission in a radio access network is provided. The radio access network comprises a scheduler configured to orchestrate communication of data between one or more base stations and a plurality of User Equipments, UEs, according to a set of instructions. The method comprises receiving real-time network usage data. The method further comprises dynamically modifying the set of instructions based on the network usage data. The method further comprises implementing the modified set of instructions so that the scheduler is configured to orchestrate communication of data between the one or more base stations and the plurality of UEs according to the modified set of instructions.