NRDU Simulation Server for 5G SA CU Capacity Testing
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Solution Overview
Problem
Current methods lack stable simulators for testing 5G CU capacity in near-field deployment scenarios, requiring high memory and CPU resources, and fail to address DU/RU/UE dependencies, leading to inefficient and costly network testing.
Innovation Solution
A new radio distributed unit (NRDU) simulation server with a flexible, resource-efficient design that simulates UE and DU scenarios, includes a split control and data path, and supports dynamic deployment based on user requirements, enabling precise error detection and testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing simulation methods are used to simulate large number of UEs and DUs, then testing coverage is improved, but memory and CPU capacity requirements increase significantly
Solution Approach 1:
The system segments the simulation functionality into a control plane simulator and a user plane simulator. The control plane simulator handles signaling and control functions, while the user plane simulator handles data traffic. This segmentation allows independent optimization of resource usage for each plane, reducing overall memory and CPU requirements while maintaining the ability to simulate large numbers of UEs and DUs.
Solution Approach 2:
The simulation server is designed with multi-functional components that can handle multiple testing scenarios simultaneously. The same infrastructure supports functional correctness testing, inter-system compatibility testing, and stress testing with different configurations of UEs and DUs, eliminating the need for separate dedicated systems for each test type.
2Reliability
If stable simulator products are deployed for 5G CU capacity testing, then testing reliability is improved, but device complexity and deployment difficulty increase
Solution Approach 1:
The simulation server employs dynamic configuration capabilities where the number of virtual UEs and DUs, traffic parameters, and testing scenarios can be adjusted on-the-fly without requiring system reconfiguration or redeployment. This dynamic adaptability provides testing stability across different scenarios while keeping the deployment process simple and flexible.
Solution Approach 2:
The system uses virtualization to create virtual copies of network elements (UEs, DUs, CU) that can be instantiated and configured through software. These virtual instances provide stable and repeatable test conditions without requiring physical hardware deployment, thereby reducing deployment complexity while maintaining testing reliability.
3Measurement precision
If near-field deployment scenarios are simulated with high fidelity, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
The simulation applies different levels of fidelity to different parts of the network model based on testing requirements. Critical areas such as error detection paths and measurement points are simulated with high precision, while less critical areas use simplified models. This local quality differentiation maintains measurement precision where needed while reducing overall resource consumption.
Data Source
AI summary
Embodiments are directed to a new radio distributed unit (NRDU) simulation server for testing in 5G standalone (SA) communication network. The NRDU simulation server includes a memory, a processor, a communicator. The processor includes a test controller connected to the plurality of simulated UEs configured to receive at least one testing profile associated with a DUT and a multi user equipment (UE) simulator (MUCE) configured to dynamically generate a plurality of simulated UEs based on the at least one testing profile. The processor also includes a DU simulator configured to generate a plurality of simulated DUs based on the at least one testing profile; an IP security Manager (IPSEC MGR) configured to simulate internet protocol (IPSEC) functionality between at least one DU and the DUT; and a first traffic generator and a second traffic generator configured to generate a traffic profile based on the at least one testing profile.