Dynamic Network Traffic Steering With AI-Based Cell Handover Control
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Solution Overview
Problem
Existing communication networks face inefficiencies in power consumption and network performance due to exponential network traffic and complex tasks, with conventional traffic steering methods being reactive and lacking predictive capabilities, leading to suboptimal spectrum utilization and increased handover failures.
Innovation Solution
A data-driven traffic steering approach using graph neural networks and deep reinforcement learning models to optimize handovers based on a utility function that balances user equipment quality of service, network energy consumption, and handover frequency, considering spatial dependencies and user equipment classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reactive traffic steering methods are used, then network traffic can be managed, but spectrum utilization is suboptimal and handover failures increase
Solution Approach 1:
The system performs preliminary actions by proactively predicting future network states and handover requirements using machine learning models before actual handovers occur. The utility function evaluates potential handover scenarios in advance, allowing the network to prepare optimal handover decisions rather than reacting to failures after they occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network performance metrics, handover outcomes, and utility function evaluations. This feedback loop enables the machine learning models to learn from past handover decisions and their outcomes, improving future handover predictions and reducing failure rates through iterative optimization.
2Productivity
If more handovers are performed to optimize traffic distribution, then network efficiency improves, but power consumption increases
Solution Approach 1:
The system changes parameters by dynamically adjusting handover thresholds and timing based on network conditions, user equipment characteristics, and predicted traffic patterns. The utility function incorporates multiple parameters including spectral efficiency, power consumption, and handover frequency, allowing optimization of the trade-off between network efficiency and energy usage by selecting handover strategies that maximize overall utility rather than simply increasing handover frequency.
3Reliability
If complex predictive models are deployed for proactive traffic steering, then handover optimization improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer consisting of machine learning models and utility function evaluators that bridge the gap between raw network data and handover control decisions. These intermediary components process complex predictions and evaluations, translating them into actionable handover commands while shielding the rest of the network from the complexity of the predictive algorithms.
Data Source
AI summary
Facilitating dynamic network traffic steering using artificial intelligence in advanced communication networks is provided. A method includes determining respective results of application of a utility function to respective combinations of potential handovers of a specified user equipment from a source cell to respective target cells of a group of target cells. The method also includes, based on the respective results of the application of the utility function, determining that a first combination of the respective combinations increases a value of the utility function as compared to other combinations of the respective combinations, other than the first combination. Further, the method includes, during a defined interval associated with a traffic steering process, facilitating the handover of the specified user equipment from the source cell to the first target cell. Other user equipment other than the specified user equipment within the communication network are not handed over during the defined interval.