AI Interference Prediction for Terrestrial–Satellite Bandwidth Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cellular devices often experience communication collisions and interference due to using specific frequencies for terrestrial and non-terrestrial networks, leading to delayed transmissions.
Innovation Solution
Utilizing an artificial intelligence mechanism to predict terrestrial network downlink transmission interference on non-terrestrial network uplink transmissions, and dynamically adjusting bandwidth utilization schemes to mitigate interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cellular devices use specific frequencies for terrestrial and non-terrestrial networks, then network connectivity is established, but communication collisions and interference occur
Solution Approach 1:
The system performs preliminary interference prediction using AI models before actual communication occurs. Historical data from both terrestrial and non-terrestrial networks is analyzed to train prediction models that forecast interference levels, allowing the system to take preventive actions to avoid communication collisions
Solution Approach 2:
The system continuously monitors actual interference levels and compares them with predicted values. This feedback loop allows the AI model to be refined and updated, improving prediction accuracy over time. The feedback mechanism enables dynamic adjustment of frequency allocation and power control strategies
2Device complexity
If bandwidth utilization schemes are fixed, then system configuration is simple, but communication efficiency decreases due to interference
Solution Approach 1:
The system transitions from fixed bandwidth utilization schemes to dynamic schemes that adapt in real-time based on predicted interference levels. The AI model continuously analyzes network conditions and adjusts bandwidth allocation, power control parameters, and frequency selection to optimize communication efficiency while managing complexity through automated decision-making
Data Source
AI summary
Systems and methods for predicting network interference. First history data on terrestrial network cell-to-user device transmissions in a terrestrial network is obtained. And second history data on user device-to-non-terrestrial network satellite transmissions in a non-terrestrial network is obtained. An artificial intelligence mechanism is employed on the first and second history data to generate a model of historical terrestrial network cell downlink transmission interference on historical non-terrestrial network satellite uplink transmissions of a non-terrestrial network satellite in the non-terrestrial network. The current terrestrial network cell downlink transmission interference on current non-terrestrial network satellite uplink transmissions is predicted based on the generated model. At least one bandwidth utilization scheme is modified for at least one terrestrial network cell of the terrestrial network and the non-terrestrial network satellite of the non-terrestrial network based on the predicted current terrestrial network cell downlink transmission interference.