Dynamic Control Plane Reconfiguration in Open RAN
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current radio access networks (RANs), particularly open-RAN (O-RAN) networks, face challenges in dynamically adapting to changing conditions such as transaction load and latency, leading to inefficiencies in energy consumption and user experience, as they lack automated policies for dynamic redistribution and optimal placement of Near-Real-Time RAN Intelligent Controllers (near-RT RICs) and E2 nodes.
Innovation Solution
A computer-implemented method generates a self-awareness matrix for the RAN, monitoring attributes like transaction load, latency, and energy consumption, allowing for dynamic reconfiguration of the control plane by reassigning or migrating near-RT RICs and E2 nodes to optimize energy usage and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the control plane is statically configured, then the network structure is simple and stable, but the network cannot adapt to changing conditions such as transaction load and latency
Solution Approach 1:
The control plane is transformed from a static configuration to a dynamic one through continuous monitoring of network attributes (transaction load, latency, energy consumption) and automated reconfiguration of near-RT RIC assignments. The system dynamically adjusts the control plane structure based on real-time conditions, allowing near-RT RICs to be reassigned to different E2 nodes as needed.
Solution Approach 2:
A feedback mechanism is implemented where the system continuously monitors network attributes including transaction load, latency, and energy consumption. This feedback loop enables the system to detect when reconfiguration is needed and triggers automated control plane adjustments to optimize network performance based on actual operating conditions.
2Speed
If near-RT RICs are distributed across multiple nodes, then user experience and response time improve, but energy consumption increases
Solution Approach 1:
Instead of maintaining near-RT RIC instances at all possible locations continuously, the system uses partial action by activating and distributing near-RT RICs only when and where needed based on monitored conditions. The automated reconfiguration allows the system to concentrate RIC functions in fewer nodes during low-load periods (reducing energy consumption) while distributing them across multiple nodes during high-load periods (improving response time).
Solution Approach 2:
The system changes the operational parameters of near-RT RICs dynamically by monitoring attributes such as transaction load, latency, and energy consumption. Based on these parameter changes, the system automatically adjusts the distribution and assignment of near-RT RICs to E2 nodes, optimizing the balance between response time and energy consumption.
3Productivity
If automated reconfiguration is implemented, then network efficiency and user experience improve, but system complexity and reconfiguration overhead increase
Solution Approach 1:
The control plane implements self-service through automated monitoring and reconfiguration capabilities. The system autonomously detects when reconfiguration is needed based on monitored attributes and automatically executes the reconfiguration of near-RT RIC assignments without requiring manual intervention. This self-service approach improves network efficiency while managing complexity through automation rather than manual processes.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring network attributes and pre-evaluating reconfiguration needs before actual changes are required. This proactive monitoring allows the system to prepare for optimal reconfiguration timing and minimize the overhead impact by making informed decisions based on accumulated data rather than reactive adjustments.
4Reliability
If monitoring and reconfiguration operations are performed continuously, then network optimization is maximized, but processing overhead and computational resources increase
Solution Approach 1:
Instead of continuous monitoring and reconfiguration, the system implements periodic action by monitoring network attributes at defined intervals and triggering reconfiguration operations only when monitored conditions satisfy predetermined criteria. This periodic approach maintains network optimization while reducing processing overhead by avoiding constant evaluation and unnecessary reconfiguration operations.
Data Source
AI summary
A computer-implemented method for dynamically reconfiguring a control plane of a radio access network. The computer-implemented method includes generating a self-awareness matrix of a radio access network (RAN) that comprises a plurality of E2 nodes, the self-awareness matrix comprises a plurality of records for each respective E2 node from the plurality of E2 nodes, a first record corresponding to a first E2 node comprises, for the first E2 node, one or more attributes of the control plane of the RAN, the first E2 node being assigned to a first Near-Real-Time RAN Intelligent Controller (near-RT RIC). The method further includes, in response to the first record satisfying a predetermined condition based on the one or more attributes of the control plane reconfiguring the control plane of the RAN.