RF Signal Interference Detection in Satellite Networks
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Solution Overview
Problem
Interfering signals in wireless RF communication channels degrade signal quality, leading to data loss and increased bit error rates, particularly in satellite communication systems where geosynchronous satellites are affected by adjacent satellites in nearby orbits, causing prolonged signal interference.
Innovation Solution
A client ground station equipped with a first processor samples the RF signal spectrum, computes linear approximations and curve fits to detect interference, and communicates with a server-based second processor to analyze the signal using machine learning to classify and mitigate interference by adjusting transmission parameters or requesting adjacent satellites to change frequencies or discontinue transmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If satellite communication systems use adjacent orbital slots for increased capacity, then network productivity is improved, but signal interference increases causing data loss and bit errors
Solution Approach 1:
The system performs preliminary interference detection by analyzing the RF signal spectrum before data transmission occurs. The ground station computes linear approximations and curve fits of the signal spectrum to identify interfering signals in advance, allowing the system to detect interference patterns and take preventive measures such as adjusting transmission parameters or notifying adjacent satellites to modify their signals, thereby maintaining both high capacity and reliable communication
2Area of stationary object
If geosynchronous satellites transmit signals through crowded orbital regions, then communication coverage is improved, but interference from adjacent satellites increases
Solution Approach 1:
The ground station acts as an intermediary between transmitting and receiving satellites by monitoring the RF signal spectrum and detecting interfering signals. When interference is detected, the ground station communicates with the adjacent satellite causing interference, requesting it to modify its transmission frequency or power. This intermediary role allows the system to maintain broad coverage while actively managing and reducing harmful interference from adjacent satellites
3Measurement precision
If machine learning algorithms are used to classify interference, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system applies machine learning algorithms selectively rather than continuously - using them specifically when interference detection is needed based on preliminary spectrum analysis. The ground station first computes linear approximations and curve fits to identify potential interference, then applies machine learning classification only when warranted by the initial analysis. This partial application of complex processing maintains high detection accuracy while avoiding unnecessary computational overhead and device complexity
Data Source
AI summary
A processor can be configured to execute instructions stored in a memory to obtain a sample of a first received signal and to extract a suspected interference characteristic from the sample. The instructions can additionally be to generate a parameter for input to a machine learning application based on the extracted suspected interference characteristic, the input parameter including a weight or a setting and to transmit a first request to a transmitter of the first received signal to modify a parameter of the transmitter responsive to the input parameter.