Deep Learning Framework for 5G Network Design Optimization
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Solution Overview
Problem
5G network design and optimization face significant complexity due to coexistence of 4G and 5G technologies, along with legacy networks, and existing approaches are limited in addressing multiple optimization problems across various scenarios, particularly for air-to-ground communications which require tailored antenna designs and deployment configurations to ensure broadband connectivity for aircraft.
Innovation Solution
A deep learning-based framework using double deep neural networks (DNNs) to model and optimize 5G network behavior, allowing for joint optimization of parameters such as antenna beam tilt, inter-site distance, and number of sectors, enabling efficient deployment and operation of complex networks like air-to-ground systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional network design approaches are used for 5G networks, then the design process becomes manageable with conventional methods, but the network cannot effectively address multiple optimization problems across various scenarios including air-to-ground communications
Solution Approach 1:
The patent applies universality by creating a unified deep learning framework that handles multiple network optimization problems across different scenarios (terrestrial and air-to-ground communications) using a single adaptable system. The framework uses shared neural network components that can be configured for various deployment scenarios, enabling one system to perform multiple optimization functions rather than requiring separate designs for each scenario.
Solution Approach 2:
The patent utilizes parameter changes by employing deep neural networks that learn optimal network parameters (such as antenna beam tilt, inter-site distance, number of sectors) through training on simulation data. The framework adjusts these parameters dynamically based on the input scenario, transforming the design process from manual configuration to automated parameter optimization that adapts to different deployment conditions.
2Reliability
If deep learning frameworks are used to optimize network parameters, then network performance metrics such as user throughput and coverage are improved, but the computational complexity and training requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the deep neural networks using extensive system-level simulations to generate training datasets. The framework performs offline training where the neural networks learn optimal parameter configurations for various scenarios before actual deployment. This preliminary training phase captures complex network behaviors, allowing the trained model to make rapid predictions during operation without requiring complex real-time computations.
Solution Approach 2:
The patent uses copying by creating a virtual model of the network through neural networks that replicates complex network behaviors and performance characteristics. Instead of directly simulating the entire network for each optimization query, the framework uses trained neural network models that copy and approximate network responses, significantly reducing computational complexity while maintaining prediction accuracy.
3Measurement precision
If extensive system-level simulations are conducted to train deep neural networks, then accurate prediction models are obtained, but the training time and computational resources required increase
Solution Approach 1:
The patent applies partial action by generating training datasets that cover the most critical and representative network scenarios rather than exhaustively simulating all possible configurations. The framework focuses computational resources on training for key deployment scenarios and parameter ranges, achieving sufficient prediction accuracy for practical purposes without the excessive computational burden of complete scenario coverage. This selective approach balances training comprehensiveness with resource constraints.
Data Source
AI summary
A method and apparatus for design and optimization of a network are described. A first deep neural network is used to obtain a function that represents a relationship between design parameters of the network and network performance metrics of the network. A second deep neural network is used to obtain a subset of one or more candidate network deployment configurations that optimize the performance metrics for the network. An optimal candidate network deployment configuration for the network is selected from the subset of candidate network deployment configurations wherein the optimal candidate network deployment configuration maximizes performance of the network as defined based on the performance metrics.