AI Beam Formation Control for Dynamic RAN Channel Allocation
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Solution Overview
Problem
Existing radio networks face challenges in selecting the best frequency spectrum location and optimum beam formation due to various environmental and network parameters, which complicate the process of launching new channels and managing traffic surges, especially for high-bandwidth services like live video streaming and augmented/virtual reality, without the capability to learn and adapt dynamically.
Innovation Solution
Implementing a software defined domain controller (SDDC) with machine learning and artificial intelligence to collect and analyze network information, enabling on-demand dynamic beam-formation by selecting radio points, bandwidth, and antenna configurations to optimize channel allocation and manage traffic demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional radio network methods are used to select frequency spectrum location and beam formation, then the system structure remains simple, but the network cannot adapt dynamically to varying traffic requirements and environmental parameters
Solution Approach 1:
An AI model is introduced as an intermediary between the radio network and the environment/traffic requirements. The AI model processes network information and traffic demands, then recommends optimal channel configurations including frequency spectrum location and beam formation parameters, enabling dynamic adaptation without direct complex control logic in the radio network itself
Solution Approach 2:
The radio network is equipped with capabilities to automatically collect network information, process traffic requirements, and adjust channel configurations without manual intervention. The system uses machine learning models that continuously learn from environmental parameters and traffic patterns to autonomously optimize beam formation and frequency selection
2Productivity
If manual methods are used to manage channel launches and traffic surges, then the system remains simple to operate, but the process is time-consuming and cannot handle peak demands efficiently
Solution Approach 1:
The AI model performs preliminary analysis of network conditions and pre-prepares optimal channel configurations before traffic surges occur. By continuously monitoring network information and traffic requirements, the system proactively identifies opportunities for channel launches and pre-computes optimal parameters, enabling rapid response when traffic demands increase
Solution Approach 2:
The system implements continuous feedback loops where network performance data and traffic measurements are collected, analyzed by the AI model, and used to automatically adjust channel configurations. This closed-loop control enables the system to learn from past performance and continuously optimize channel launches and traffic management decisions
3Reliability
If comprehensive network parameters are considered for beam formation optimization, then the communication quality is improved, but the complexity of selecting optimum parameters increases
Solution Approach 1:
The AI model serves as an intermediary that handles the complexity of analyzing multiple network parameters including path loss, scattering, absorption, and antenna gain profiles. The model processes these complex interactions and outputs simplified optimal parameter recommendations, shielding the radio network from the computational complexity while achieving high communication reliability
Solution Approach 2:
The system dynamically adjusts multiple parameters including frequency spectrum location, beam formation angles, antenna configurations, and bandwidth allocation based on real-time network conditions. The AI model optimizes these parameters collectively rather than individually, considering their interdependencies to achieve reliable communication under varying environmental conditions
Data Source
AI summary
Aspects of the subject disclosure may include, for example, collecting network information for a mobility network, including processing the network information at a central controller serving the mobility network, building, in the central controller, a machine learning model including building the machine learning model using the network information, and allocating, by the central controller, radio channels of the mobility network based on a recommendation of the machine learning model. Other embodiments are disclosed.