Apparatus and method for compensating channel based on artificial neural network
An apparatus for compensating a channel based on an artificial neural network includes a channel learning device that learns a property of the channel, and a channel compensating device that compensates for an error of the channel based on a learning result of the channel learning device.
1. An apparatus for compensating a channel based on an artificial neural network, the apparatus comprising:
a channel learning device configured to learn a property of the channel; and
a channel compensating device configured to compensate for an error of the channel based on a learning result of the channel learning device,
wherein the channel includes a channel estimated by a data pilot-aided (DPA) scheme.
2. The apparatus of claim 1 , wherein the channel learning device is configured to learn a property of a vehicle-to-everything (V2X) communication channel.
3. The apparatus of claim 1 , wherein the channel learning device is configured to learn channel correlation in a frequency domain.
4. The apparatus of claim 1 , wherein the channel learning device is implemented with an auto-encoder.
5. The apparatus of claim 4 , wherein the auto-encoder is implemented with one input layer, three hidden layers, and one output layer.
6. The apparatus of claim 5 , wherein the hidden layer includes a first hidden layer including 40 hidden units, a second hidden layer including 20 hidden units, and a third hidden layer including 40 hidden units.
7. The apparatus of claim 1 , wherein the channel compensating device is configured to convert the channel estimated by the DPA scheme into a vector of a complex form, and compensate for the error of the channel by applying a pre-optimized learning parameter to the vector.
8. The apparatus of claim 7 , wherein the channel compensating device is configured to sequentially compensate for the error of the channel for a plurality of orthogonal frequency division multiplexing (OFDM) symbols.
9. A method of compensating a channel based on an artificial neural network, the method comprising:
learning, by a channel learning device, a property of the channel; and
compensating, by a channel compensating device, for an error of the channel based on a learning result of the channel learning device;
wherein the channel includes a channel estimated by a data pilot-aided (DPA) scheme.
10. The method of claim 9 , wherein learning the property of the channel includes learning a property of a vehicle-to-everything (V2X) communication channel.
11. The method of claim 9 , wherein learning the property of the channel includes learning channel correlation in a frequency domain.
12. The method of claim 9 , wherein the channel learning device is implemented with an auto-encoder.
13. The method of claim 12 , wherein the auto-encoder is implemented with one input layer, three hidden layers, and one output layer.
14. The method of claim 13 , wherein the hidden layer includes a first hidden layer including 40 hidden units, a second hidden layer including 20 hidden units, and a third hidden layer including 40 hidden units.
15. The method of claim 9 , wherein compensating the error of the channel includes:
converting the channel estimated by the DPA scheme into a vector of a complex form; and
compensating for the error of the channel by applying a pre-optimized learning parameter to the vector.
16. The method of claim 15 , wherein compensating the error of the channel includes sequentially compensating for the error of the channel for a plurality of orthogonal frequency division multiplexing (OFDM) symbols.
17. A non-transitory computer readable medium containing program instructions executed by a processor, the computer readable medium comprising:
program instructions that learn a property of a channel based on an artificial neural network; and
program instructions that compensate for an error of the channel based on a learning result of the channel learning device;
wherein the channel includes a channel estimated by a data pilot-aided (DPA) scheme.