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Carrier interference detection and removal for satellite communication

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

AI solution for traffic signal optimization based on multi-source data fusion

For:

Department of transportation (DOT) Department of police (DOP)

Scope:

Generate traffic signal timing plans by analysing the traffic flow status and patterns based on fusing internet data, induction coils data and video data, and control the traffic signal with the generated timing plans in a real-time, self- adaptive and cooperative way.

Goal:

Improve Operation Efficiency

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.

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