Distributed Neural Network RRM Optimization via Primal-Dual Parameter Sharing
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Solution Overview
Problem
Current centralized radio resource management (RRM) systems face challenges in adapting to changing channel conditions due to high latency and the need for extensive channel state information reporting, which does not scale well with increasing geographical areas or mobile nodes, leading to suboptimal RRM decisions.
Innovation Solution
Implementing a distributed neural network-based RRM optimization technique where local neural networks at edge devices make decisions based on channel measurements, sharing optimization parameters instead of channel measurements, allowing for reduced feedback and online adaptation, and using primal-dual optimization to update parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized RRM systems collect extensive channel state information from all nodes, then RRM decision accuracy can be improved, but system latency increases and scalability deteriorates
Solution Approach 1:
The patent segments the centralized RRM system into distributed edge nodes, each running local neural networks that independently process channel measurements. This segmentation eliminates the need for extensive channel state information collection at a central point, reducing latency while maintaining decision accuracy through localized processing.
Solution Approach 2:
The patent implements local quality by enabling each edge node to perform local inference using locally stored neural network models and local channel measurements. This local processing approach maintains RRM decision accuracy without requiring centralized collection of extensive channel state information, thereby reducing system latency.
2Adaptability or versatility
If centralized RRM systems collect channel measurements from increasing geographical areas and mobile nodes, then comprehensive RRM coverage is improved, but system complexity and feedback requirements worsen
Solution Approach 1:
The patent divides the large-scale RRM system into multiple independent edge nodes, each handling local RRM decisions. This segmentation allows comprehensive geographical coverage without increasing central system complexity, as each edge node autonomously processes local channel measurements and makes RRM decisions.
Solution Approach 2:
The patent enables edge nodes to perform self-service through local neural network inference, where each node independently processes local channel measurements and generates RRM decisions without requiring complex centralized coordination. This self-service approach maintains comprehensive coverage while reducing system complexity.
3Speed
If edge devices perform local neural network inference with limited compute resources, then RRM decision speed is improved, but model accuracy and adaptability worsen
Solution Approach 1:
The patent applies preliminary action by pre-training neural network models centrally and distributing them to edge nodes before deployment. This allows edge devices to perform fast local inference with limited compute resources while maintaining model accuracy, as the models have already learned optimal RRM strategies during centralized pre-training.
Solution Approach 2:
The patent uses copying by replicating the trained neural network model across multiple edge nodes. Each edge node holds a copy of the model and performs local inference independently, achieving fast RRM decision speed at the edge while maintaining model accuracy through the quality of the pre-trained model.
Data Source
AI summary
In one embodiment, a machine learning (ML) model for determining radio resource management (RRM) decisions is updated, with ML model parameters being shared between RRM decision makers to update the model. The updates may include local operations (between an AP and UE pair) to update local primal and dual parameters of the ML model, and global operations (between other devices in the network) to exchange/update global parameters of the ML model.