Cloud-Edge RAN Intelligence for PDCP Duplication Configuration
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Solution Overview
Problem
Current wireless communication systems lack signaling mechanisms for configuring reliability enhancement mechanisms in cloud/edge RAN intelligence, leading to inefficient radio resource usage and internal configuration within RAN nodes, which is not suitable for disaggregated and cloud-native networks.
Innovation Solution
Proposed signaling mechanisms for configuring 5G reliability enhancement schemes, including packet duplication, conservative rate selection, and network coding, allowing edge/cloud RAN intelligence to instruct RAN nodes for reliability enhancement, with measurement collection, algorithmic analysis, and control signaling from cloud/edge servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reliability enhancement mechanisms are internally configured by RAN nodes, then reliability can be improved, but radio resource usage becomes inefficient and cloud/edge intelligence cannot optimize configuration
Solution Approach 1:
The patent introduces cloud/edge RAN intelligence as an intermediary between core network and RAN nodes. This intermediary receives reliability requirements from core network, performs sophisticated optimization algorithms, and generates optimized configuration parameters for RAN nodes. The intermediary enables centralized optimization while maintaining distributed node autonomy, resolving the contradiction between reliability improvement and resource efficiency.
Solution Approach 2:
The patent implements preliminary configuration of reliability enhancement mechanisms at cloud/edge level before deployment at RAN nodes. The system pre-calculates optimal configuration parameters based on historical data and predicted network conditions, allowing RAN nodes to execute pre-optimized settings that balance reliability requirements with efficient radio resource usage, avoiding reactive internal configuration.
2Productivity
If cloud/edge RAN intelligence configures reliability enhancement, then radio resource usage efficiency improves, but signaling complexity increases
Solution Approach 1:
The patent transforms complex reliability enhancement configurations into simplified parameter changes. Instead of transmitting complete configuration scenarios, the system exchanges optimized parameter values (e.g., modified QoS parameters, adjusted reliability thresholds) that RAN nodes can directly apply. This parameter-based communication reduces signaling complexity while maintaining the benefits of cloud/edge optimization.
Solution Approach 2:
The patent uses configuration templates and standardized signaling patterns that RAN nodes can copy and adapt. The cloud/edge RAN intelligence provides standardized configuration templates for reliability enhancement mechanisms, allowing RAN nodes to instantiate these templates locally without transmitting complex custom configurations. This copying approach simplifies signaling while enabling sophisticated optimization.
3Speed
If RAN nodes perform internal configuration for reliability enhancement, then configuration speed is fast, but adaptability to cloud/edge optimization is lost
Solution Approach 1:
The patent creates a dynamic configuration model where RAN nodes maintain the ability to execute configurations locally (fast response) while simultaneously receiving optimized parameters from cloud/edge RAN intelligence (adaptability). The system dynamically adjusts configuration parameters based on real-time network conditions and cloud/edge optimization algorithms, allowing RAN nodes to operate autonomously when needed while adapting to centralized optimization when required.
Data Source
AI summary
Embodiments of a virtualized radio-access network (RAN) node configured to implement functions including an O-RAN Distributed Unit (DU), an O-RAN Radio Unit (RU), an O-RAN Central Unit-Control Plane (CU-CP), and an O-RAN Central Unit-User Plane (CU-UP), are described herein. In some embodiments, a reliability enhancement strategy is determined according to measurement reports collected from user equipment (UEs), the DU and the CU-UP.