GAN-Based Network Emulation for Self-Organizing Control
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Solution Overview
Problem
The management of large and complex communication networks, including virtualized network functions (VNFs) and self-organizing networks (SONs), faces challenges such as high operating costs, difficulty in modification, lack of adaptability, and slow manual updates, which hinder efficient network optimization and resource management.
Innovation Solution
The implementation of deep generative models, specifically using generator and discriminator neural networks within generative adversarial networks (GANs), to simulate and optimize network entities, automate testing, and perform reinforcement learning, enabling adaptive and efficient management of VNFs and SONs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual network management and optimization is used, then network control is simple, but deployment and maintenance costs are high and updates are slow
Solution Approach 1:
The system enables self-service through automated neural network models that perform network optimization, deployment, and maintenance tasks without requiring manual intervention. The generative adversarial network automatically generates network configurations, predicts performance metrics, and executes optimization decisions, transforming manual management into autonomous operation.
Solution Approach 2:
The patent replaces manual mechanical management processes with automated neural network systems. The generator and discriminator neural networks substitute human operators' decisions with AI-based automation, enabling rapid deployment and maintenance operations that were previously performed manually.
2Reliability
If deep generative models with GANs are implemented, then network optimization performance is improved, but system complexity increases
Solution Approach 1:
The generative adversarial network acts as an intermediary layer between network operators and the complex network optimization tasks. The GAN framework simplifies the interaction by providing automated interfaces for configuration generation, performance prediction, and optimization execution, hiding the underlying computational complexity while delivering improved performance.
Solution Approach 2:
The system manages complexity by dynamically adjusting neural network parameters and hyperparameters based on network conditions. The GAN model adapts its generator and discriminator networks to optimize performance for specific network scenarios, transforming fixed complex architecture into flexible adaptive systems that maintain reliability while managing complexity through parameter optimization.
3Adaptability or versatility
If neural network models are trained and deployed, then adaptability to network conditions is enhanced, but computational resources and training time increase
Solution Approach 1:
The system performs preliminary training of the generative adversarial network models during off-peak times or in advance, pre-computing optimized network configurations and performance predictions. This preliminary action enables the network to adapt quickly to changing conditions without requiring intensive real-time computational resources, reducing energy consumption during actual network operation.
Solution Approach 2:
The patent applies partial action by training neural network models on representative samples of network conditions rather than all possible scenarios. The GAN framework generates synthetic training data that captures essential network patterns, providing sufficient adaptability without the computational burden of exhaustive training on every possible network state, thus optimizing the balance between adaptability and resource consumption.
Data Source
AI summary
A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to train a generator neural network to simulate a network entity using a discriminator neural network that discriminates output associated with the network entity from output generated by the generator neural network. The computer device may be further configured to receive a set of input parameters associated with the simulated network entity; use the generator neural network to generate output for the simulated network entity based on the received set of input parameters; and apply the generated output for the simulated network entity to manage a communication network.