RAN Intelligent Controller Flow Control for Scalable Cellular Systems
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Solution Overview
Problem
Conventional Radio Access Networks (RANs) face challenges in upgrading and evolving due to their integrated design with application-specific hardware, making them difficult to manage and optimize, especially in cloud-based environments where fronthaul splits and data radio bearers' latency and throughput require efficient management.
Innovation Solution
Implementing a method within the RAN Intelligent Controller (RIC) to dynamically adjust the frequency of Data Delivery Status (DDDS) message transmissions based on parameters from the Distributed Unit (DU) and Centralized Unit (CU), optimizing buffer management and flow control to improve scalability and performance by selecting subsets of Data Radio Bearers (DRBs) for reduced or increased transmission frequencies and managing latency and memory utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the frequency of DDDS message transmissions is increased for all DRBs, then the flow control accuracy and latency management improve, but the CPU and memory overhead increases, reducing system scalability
Solution Approach 1:
The patent applies local quality by differentiating DDDS transmission frequencies based on individual DRB characteristics. Instead of using a uniform transmission frequency for all DRBs, the system identifies specific DRBs that require more frequent updates (e.g., those with latency-sensitive traffic or buffer overflow risks) and applies higher transmission frequencies only to those subsets, while using lower frequencies for other DRBs. This resolves the contradiction by maintaining high flow control accuracy where needed without imposing excessive CPU and memory overhead across the entire system.
Solution Approach 2:
The patent implements dynamics by making DDDS transmission frequencies adaptive rather than static. The system dynamically adjusts transmission frequencies based on real-time buffer status, traffic conditions, and DRB priorities. This allows the system to increase transmission frequency temporarily when flow control accuracy is critical (resolving the reliability aspect) while returning to lower frequencies during normal conditions (reducing overhead), thus dynamically balancing the contradiction between reliability and device complexity.
2Adaptability or versatility
If the number of supported DRBs is increased to improve system capacity, then the network scalability improves, but the buffer management complexity and memory requirements increase
Solution Approach 1:
The patent applies partial action by implementing selective buffer status reporting for subsets of DRBs rather than all DRBs. The system identifies critical DRBs (e.g., those with high priority or latency-sensitive traffic) and applies comprehensive buffer management only to these subsets, while using simplified management for other DRBs. This allows the system to support a large total number of DRBs (improving adaptability) without requiring proportional increases in memory resources for all DRBs simultaneously.
Solution Approach 2:
The patent segments the DRB set into multiple subsets based on traffic characteristics, priority levels, and buffer status. Each subset receives differentiated buffer management attention and DDDS transmission frequencies. This segmentation allows the system to manage memory resources efficiently by focusing detailed buffer management on critical subsets while using coarser management for less critical subsets, thereby supporting high system capacity without linearly increasing memory requirements.
3Ease of operation
If uniform DDDS transmission frequency is used for all DRBs, then the implementation simplicity is maintained, but the latency performance for latency-sensitive applications deteriorates
Solution Approach 1:
The patent applies local quality by assigning different DDDS transmission frequencies to different DRB subsets based on their latency requirements. Latency-sensitive DRBs (such as those carrying real-time video or voice traffic) receive higher transmission frequencies to ensure timely buffer status updates and prevent overflow, while non-latency-sensitive DRBs use lower frequencies. This resolves the contradiction by maintaining implementation simplicity through automated subset identification while improving latency performance where it is critically needed.
Data Source
AI summary
System and methods for Distributed Unit (DU) or Centralized Unit (CU) flow control optimizations for highly scalable cellular systems. A DU is configured to indicate to CU if a specific Data Radio Bearer (DRB) is not getting adequate latency or throughput. Frequency of Downlink Data Delivery Status (DDDS) is increased for some such DRBs and can be decreased for other DRBs if needed. A buffer management method module is configured to improve scalability of a Base Station. Implementations of these operations can be configured for and located at a Radio Access Network Intelligent Controller.