Reinforcement Learning Frequency Allocation for LEO Satellites
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Solution Overview
Problem
Existing frequency allocation techniques based on machine learning are limited to specific communication environments and struggle to mitigate interference between satellite networks using the same frequency band without sharing system information.
Innovation Solution
A reinforcement learning model, specifically a multi-armed bandits model using an upper confidence bound algorithm, is employed to predict and allocate frequency resources independently across satellite networks, updating parameters based on feedback to minimize interference and maximize throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used for frequency allocation in satellite networks, then throughput is maximized and interference is reduced, but the system requires complex learning models and extended training time
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning model using historical data and simulated environments before actual deployment. This allows the model to learn optimal frequency allocation strategies in advance, reducing the complexity of real-time decision-making while maintaining high throughput performance in live satellite networks.
2Reliability
If reinforcement learning model is updated with real-time feedback, then interference mitigation improves, but processing time and computational load increase
Solution Approach 1:
The patent implements feedback mechanisms where the reinforcement learning model receives real-time performance data from satellite communications and adjusts its frequency allocation decisions accordingly. This continuous feedback loop improves interference mitigation by learning from actual network conditions while maintaining efficient processing through optimized update frequencies and incremental learning approaches.
Data Source
AI summary
A frequency resource allocation apparatus comprising a processor is configured to select resources for transmitting a signal to a user based on a learning model, allocate the selected resources to the user, transmit the signal to the user using the selected resources, receive information about whether the transmission of the signal is successful or not from the user via a feedback channel after a delayed time, and update an internal parameter of the learning model with respect to the resources used for transmitting the signal.