Operation

❯

Predictive Maintenance

❯

Carrier interference detection and removal for satellite communication

Deep learning technology combined with topological data analysis successfully estimates degree of internal damage to bridge infrastructure

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Estimate and detect the risk of catastrophic collapse of old bridges.

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Manufacturer of high-voltage (HV) circuit breakers

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Operators of satellite communication systems Users of satellite communication systems Regulation authorities Space agencies

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Jet engine predictive maintenance service

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Airline industry, Jet engine industry, Airline maintenance industry, cloud-based AI providers, airline insurance industry

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Carrier interference detection and removal for satellite communication

For:

Operators of satellite communication systems Operators of other communication systems (satellite or non-satellite) that are potential sources of interference Users of satellite communication systems Regulation authorities Space agencies

Scope:

Machine-learning-based detection, classification and removal of interference signals for satellite communication systems.

Goal:

Other

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For:

Operation, R&D

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Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.

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Anticipate Risks, Improve Operation Efficiency

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Detect signs of malfunction (failure) in wind power generators

Goal:

Improve Operation Efficiency

Machine learning-driven analysis of batch process operation data to identify causes of poor batch performance

For:

Batch manufacturers such as milk pasteurizers, pharmaceutical makers, paint manufacturers, etc.

Scope:

Detecting issues in a batch manufacturing process that lead to bad quality products or longer cycle times for batch processing.

Goal:

Other

Carrier interference detection and removal for satellite communication

For:

Goal:

Other

Problem addressed

Detection (and possibly classification) of interfering signals in satellite

communication systems (e.g., Digital Video Broadcasting - Satellite - Second

Generation (DVB-S2) or DVB-S2 Extensions (DVB-S2x)), and removal of the

interfering signal using the gained knowledge about the interference

characteristics, with the aim of reducing the error rate at the receiver.

Scope of use case

Machine-learning-based detection, classification and removal of interference

signals for satellite communication systems.

Description

The ML-algorithm operates on the received samples of the

signal consisting of the desired carrier and the interferer.

The ML-algorithm searches for repetitive patterns in the

signal, which are not expected from the known carrier signal.

For instance, the interfering signal can be another DVB-S2 or

DVB-S2x carrier from an adjacent satellite, a radar signal, or

a terrestrial radio relay system. Each of these interfering

signals contains a repetitive pattern, for instance in the form

of pilot symbols or unique words.

Regarding the type of ML method, both supervised and

unsupervised learning can be feasible. However, the

supervised learning scenario requires training using a

number of previously known interferers. This would limit

the detection to a class of selected interfering signals.

The use case can be broken down into different sub-

problems.

Interference detection: This problem can be treated as

anomaly detection, and involves teaching the model

about the undistorted signal from clean data.

Interference classification: Given sufficient training data

on different types of inference signals, the problem can

be treated as a classification problem of undistorted

signals and signals overlapping with a particular type of

distortion. This approach provides the type of distortion

as a result, but may produce unreliable results under the

presence of distortions not trained for. A case that may

necessary to be handled specifically is that of interfering

signals of the same type, e.g., a digital video broadcasting

(DVB) signal overlapping with another DVB signal, as the

statistics of the two signals would be similar, but just the time offset of the synchronization symbols would enable

the identification of the signals.

Signal separation: If interference has been identified,

signal separation may be desirable for further

processing. Parts of the carrier are known (pilot

sequence) or it is possible to transmit known data

signals over the carrier, such that the desired carrier can

be reconstructed at the receiver. The ML-algorithm is

trained using a comparison of the received (and

interfered) signal with the (known) transmitted signal

from the carrier, and determines a model as to how the

interfering samples are added to the carrier. Then the

interference is reduced symbol by symbol from the

carrier based on the trained states of the ML-algorithm.

Raw Data

Times Series

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