WAVEFORM AGNOSTIC LEARNING-ENHANCED DECISION ENGINE FOR ANY RADIO
One or more aspects of the present disclosure are directed to a software-based solution that can classify interference signals in real-time affecting a radio equipment and provide/implement an interference mitigations scheme to combat the interference signal and restore communication system of the radio equipment. In one aspect, a radio equipment includes memory having computer-readable instructions stored therein and one or more processors. The one or more processors are configured to execute the computer-readable instructions to receive at least one interference signal via an antenna of the radio; determine one or more layers characteristics of one or network layers used for transmission of signals for the radio; classify the interference signal using one or more features in the interference signal and the one or more layers characteristics; and determine an interference mitigation scheme for countering the interference signal.
1 . A radio equipment comprising:
memory having computer-readable instructions stored therein; and
one or more processors configured to execute the computer-readable instructions to:
receive at least one interference signal via an antenna of the radio;
determine one or more layers characteristics of one or network layers used for transmission of signals for the radio;
classify the interference signal using one or more features in the interference signal and the one or more layers characteristics; and
determine an interference mitigation scheme for countering the interference signal.
2 . The radio equipment of claim 1 , wherein the one or more processors are further configured to:
determine a feature matrix based on a combination of the one or more features and the one or more layers characteristics; and
classify the interference signal using the feature matrix.
3 . The radio equipment of claim 2 , wherein the one or more processors are configured to classify the interference signal using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output.
4 . The radio equipment of claim 1 , wherein one or more processors are configured to determine the interference mitigation scheme using a trained neural network, the trained neural network being configured to receive the classified interference signal as an input and provide as output the interference mitigation scheme.
5 . The radio equipment of claim 1 , wherein the one or more processors are further configured to implement the interference mitigation scheme by modifying at least one parameter associated with signal transmission using the radio.
6 . The radio equipment of claim 5 , wherein the at least one parameter is a configuration of one or more network layers.
7 . The radio equipment of claim 1 , wherein the one or more network layers including a physical layer, a MAC layer and a network layer of a modem of the radio equipment.
8 . One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors of a radio equipment, cause the radio equipment to:
receive at least one interference signal via an antenna of the radio;
determine one or more layers characteristics of one or network layers used for transmission of signals for the radio;
classify the interference signal using one or more features in the interference signal and the one or more layers characteristics; and
determine an interference mitigation scheme for countering the interference signal.
9 . The one or more non-transitory computer-readable media of claim 8 , wherein the execution of the compute-readable instructions further causes the radio equipment to:
determine a feature matrix based on a combination of the one or more features and the one or more layers characteristics; and
classify the interference signal using the feature matrix.
10 . The one or more non-transitory computer-readable media of claim 9 , wherein the execution of the compute-readable instructions further cause the radio equipment to classify the interference signal using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output.
11 . The one or more non-transitory computer-readable media of claim 8 , wherein the execution of the compute-readable instructions further causes the radio equipment to determine the interference mitigation scheme using a trained neural network, the trained neural network being configured to receive the classified interference signal as an input and provide as output the interference mitigation scheme.
12 . The one or more non-transitory computer-readable media of claim 8 , wherein the execution of the compute-readable instructions further causes the radio equipment to implement the interference mitigation scheme by modifying at least one parameter associated with signal transmission using the radio.
13 . The one or more non-transitory computer-readable media of claim 12 , wherein the at least one parameter is a configuration of one or more network layers.
14 . The one or more non-transitory computer-readable media of claim 8 , wherein the one or more network layers including a physical layer, a MAC layer and a network layer of a modem of the radio equipment.
15 . A method comprising:
receiving, at a controller of a radio equipment, at least one interference signal via an antenna of the radio;
determining, by the controller, one or more layers characteristics of one or network layers used for transmission of signals for the radio;
classifying, by the controller, the interference signal using one or more features in the interference signal and the one or more layers characteristics; and
determining, by the controller, an interference mitigation scheme for countering the interference signal.
16 . The method of claim 15 , further comprising:
determining a feature matrix based on a combination of the one or more features and the one or more layers characteristics; and
classifying the interference signal using the feature matrix.
17 . The method of claim 16 , wherein the interference signal is classified using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output.
18 . The method of claim 15 , wherein the interference mitigation scheme is determined using a trained neural network, the trained neural network being configured to receive the classified interference signal as an input and provide as output the interference mitigation scheme.
19 . The method of claim 15 , wherein the interference mitigation scheme is implemented by modifying at least one parameter associated with signal transmission using the radio.
20 . The method of claim 15 , wherein the at least one parameter is a configuration of one or more network layers.