Radio Network Configuration Using Conditional Generative Models
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Solution Overview
Problem
Existing approaches for configuring network devices in radio networks often optimize only a small subset of actions at a time, leading to sub-optimal conclusions due to complex interdependencies and require data compression, which can result in loss of granularity.
Innovation Solution
A method utilizing a conditional generative model, such as a conditional variational autoencoder (cVAE), to generate a full configuration file that considers multiple attributes simultaneously, preserving specified conditions like KPIs and energy constraints, without the need for pre-designed reward functions or data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing approaches optimize only a small subset of actions at a time, then the complexity of the optimization problem is reduced, but the configuration recommendations become sub-optimal due to complex interdependencies
Solution Approach 1:
The patent segments the configuration optimization problem into multiple independent action subsets that can be optimized separately and then combined. The conditional generative model learns to generate configurations by independently optimizing different action segments (such as power control actions, resource allocation actions, etc.) and then combines them to produce comprehensive configuration recommendations that maintain optimal performance across all parameters simultaneously.
Solution Approach 2:
The patent transforms the complex multi-parameter optimization problem into a different dimensional space by using a conditional generative model that operates in a latent space. The model maps the high-dimensional configuration space into a lower-dimensional latent representation where optimization is more tractable, then reconstructs the full configuration from the optimized latent variables, effectively solving the interdependency problem through dimensional transformation.
2Productivity
If data compression is applied to reduce computational complexity, then training speed increases, but loss of granularity occurs in the dataset
Solution Approach 1:
The patent changes the parameter representation by introducing a latent variable space that captures the essential characteristics of the configuration data without requiring full data granularity. The conditional generative model learns to map input conditions to optimized configuration parameters directly in this latent space, achieving efficient training while preserving the critical information needed for optimal configuration recommendations through parameter transformation rather than compression.
Data Source
AI summary
A method performed by a computing device for a radio network for configuration of a network device on which network or data energy can be collected while preserving specified conditions in the radio network is provided. The method includes receiving inputs to a conditional generative model. The inputs include the specified conditions in the radio network including a value for a predicted key performance indicator, KPI, constraint parameter for a time period, a target value for a optimization parameter, and a latent variable. The method further includes outputting from the conditional generative model a configuration data for a future time period for the network node or the cell of the radio network. The configuration data is bounded by the specified conditions including the predicted KPI constraint parameter, the target value for the optimization parameter, and the latent variable.