AI Inference Agent Integration in RAN for Low-Overhead Model Updates
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Solution Overview
Problem
The integration of AI/ML in RAN requires significant computational investment and minimizes the computational efficiency, and there is a need to minimize computational costs while maintaining computational efficiency, while ensuring effective AI/ML model accuracy and wireless performance optimization.
Innovation Solution
A system integrating an AI/ML inference agent with a software-defined RAN, utilizing shared hardware resources for inner-loop operations and dynamic model adjustments, and employing gRPC/eBPF interfaces for real-time interaction, along with shared-memory and interface modes for efficient communication and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML is integrated into RAN to optimize wireless performance, then wireless performance optimization is improved, but computational costs increase
Solution Approach 1:
The system segments AI/ML operations into two distinct loops: inner-loop operations executed locally at the RAN node for real-time wireless performance optimization, and outer-loop operations executed in the cloud for model training and updates. This segmentation allows the RAN to perform lightweight inference locally while offloading computationally intensive tasks to cloud resources, thereby optimizing the balance between wireless performance and computational costs.
Solution Approach 2:
The patent introduces an AI/ML inference agent as an intermediary component that bridges the RAN node and cloud server. This inference agent handles local inner-loop operations for real-time decision-making while communicating with the cloud for outer-loop model updates. The intermediary architecture enables efficient resource utilization by performing necessary computations locally while leveraging cloud capabilities for complex model management.
2Measurement precision
If AI/ML models are deployed in RAN to enhance optimization capabilities, then model accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides model operations into accuracy-critical inner-loop inference executed locally at the RAN node and computationally intensive outer-loop training performed in the cloud. This segmentation maintains high model accuracy through local real-time inference while reducing device complexity by offloading model training and updates to cloud resources.
Solution Approach 2:
The patent applies partial action by implementing only the necessary inner-loop inference operations at the RAN node rather than deploying full model training capabilities. The system performs partial computations locally for real-time optimization while relying on cloud resources for complete model management, thereby achieving adequate accuracy without excessive computational complexity.
3Speed
If real-time AI/ML inference is performed at RAN nodes for fast model updates, then operational speed is improved, but computational resources are consumed
Solution Approach 1:
The system segments computational tasks by executing only essential inner-loop inference operations locally at the RAN node for fast real-time model updates, while offloading computationally intensive outer-loop training and model management to cloud servers. This segmentation achieves fast operational speed for critical decisions while conserving computational resources through selective local processing.
Solution Approach 2:
The RAN node performs self-service by executing inner-loop inference operations locally for real-time model updates without requiring continuous cloud connectivity or computational support. The inference agent autonomously handles time-critical computations at the RAN node while periodically synchronizing with the cloud for model updates, thereby achieving fast operational speed with reduced computational resource consumption.
Data Source
AI summary
The disclosure described herein generally relates to a system integrating Radio Access Network (RAN) with Artificial Intelligence and Machine Learning (AI/ML) inference agent and, more particularly, to the use of a system integrating an AI/ML inference agent and a RAN unit. The system is software defined, involving model operations such as model inference, model update and model fallback or backup in a real-time system. The inference performance is maintained without training new data from outer resources. It brings no additional cost when updating the model within inner-loop and fallback or backup decision is also within inner-loop without additional resources.