MACHINE LEARNING BASED CHANNEL ESTIMATION METHOD FOR FREQUENCY-SELECTIVE MIMO SYSTEM

Abstract

A machine learning based method for channel estimation for a multiple-input multiple-output, MIMO, system, the method including receiving a measured signal y[k] at a receiver of the system; finding subcarriers k of the measured signal y[k]; estimating, with a convolutional neural network, CNN, channel amplitudes ĝ[k] of the measured signal y[k]; reconstructing a channel Ĥ[k], between the receiver and a transmitter of the system, based on the channel amplitudes ĝ[k] and a low resolution whiten measurement matrix Yw; and adjusting a parameter of the system based on the reconstructed channel Ĥ[k]. The channel amplitudes ĝ[k] are simultaneously estimated by the CNN.

Description

Claims

1. A machine learning based method for channel estimation for a multiple-input multiple-output, MIMO, system, the method comprising: receiving a measured signal y[k] at a receiver of the system;finding subcarriers k of the measured signal y[k];estimating, with a convolutional neural network, CNN, channel amplitudes ĝ[k] of the measured signal y[k];reconstructing a channel Ĥ[k], between the receiver and a transmitter of the system, based on the channel amplitudes ĝ[k] and a low resolution whiten measurement matrix Υw; andadjusting a parameter of the system based on the reconstructed channel Ĥ[k],wherein the channel amplitudes ĝ[k] are simultaneously estimated by the CNN.

2. The method of claim 1, wherein the receiver is a user terminal and the MIMO system is a hybrid mmWave MIMO system.

3. The method of claim 1, wherein the step of estimating comprises: calculating a correlation vector c[k] based on a correlation of the measured signal y[k];calculating a correlation matrix Cα[k] based on the correlation vector c[k];estimating a channel amplitude matrix Ĝ[k] with the CNN, based on the correlation matrix Cα[k]; andforming the channel amplitudes ĝ[k] by vectorizing the channel amplitude matrix Ĝ[k].

4. The method of claim 1, wherein the step of reconstructing comprises: iteratively selecting highest amplitudes from the channel amplitudes ĝ[k] and calculating a residual r[k] as a difference between (1) a whiten form of the measured signal y[k], and (2) a projection of the measured signal y[k] along the low resolution whiten measurement matrix Υw, until a predetermined threshold ∈ is reached;computing a sparse channel vector hV[k] as the projection of the measured signal y[k]; andcalculating the channel H[k] based on a vectorization of the sparse channel vector hV[k], a dictionary matrix for a transmit array response of a transmitter, and a dictionary matrix for a receive array response of the receiver,wherein the low resolution is about 4 times a number of the receiver antennas times a number of the transmitter antennas.

5. The method of claim 1, wherein the steps of finding, estimating, reconstructing and adjusting take place in a user terminal.

6. The method of claim 1, wherein the steps of finding, estimating, reconstructing and adjusting take place in a base station of the system.

7. A transceiver performing a machine learning channel estimation for a multiple-input multiple-output, MIMO, system, the transceiver comprising: an interface configured to receive a measured signal y[k] at a receiver of the system; anda processor connected to the interface and configured to, find subcarriers k of the measured signal y[k],estimate, with a convolutional neural network, CNN, channel amplitudes ĝ[k] of the measured signal y[k],reconstruct a channel Ĥ[k], between the receiver and a transmitter of the system, based on the channel amplitudes ĝ[k] and a low resolution whiten measurement matrix Υw, andadjust a parameter of the system based on the reconstructed channel Ĥ[k],wherein the channel amplitudes ĝ[k] are simultaneously estimated by the CNN.

8. The transceiver of claim 7, wherein the processing is further configured to: calculate a correlation vector c[k] based on a correlation of the measured signal y[k];calculate a correlation matrix Cα[k] based on the correlation vector c[k];estimate a channel amplitude matrix Ĝ[k] with the CNN, based on the correlation matrix Cα[k]; andform the channel amplitudes ĝ[k] by vectorizing the channel amplitude matrix Ĝ[k].

9. The transceiver of claim 7, wherein the processor is further configured to: iteratively select highest amplitudes from the channel amplitudes ĝ[k] and calculating a residual r[k] as a difference between (1) a whiten form of the measured signal y[k], and (2) a projection of the measured signal y[k] along the low resolution whiten measurement matrix Υw, until a predetermined threshold ∈ is reached;compute a sparse channel vector hV[k] as the projection of the measured signal y[k]; andcalculate the channel Ĥ[k] based on a vectorization of the sparse channel vector hV[k], a dictionary matrix for a transmit array response of a transmitter, and a dictionary matrix for a receive array response of the receiver,wherein the low resolution is about 4 times a number of the receiver antennas times a number of the transmitter antennas.

10. The transceiver of claim 7, further comprising: a first plurality of antennas configured to receive the measured signal; anda second plurality of antennas configured to transmit a signal.

11. A refined machine learning based method for channel estimation for a multiple-input multiple-output, MIMO, system, the method comprising: receiving a measured signal y[k] at a receiver of the system;finding subcarriers k of the measured signal y[k];estimating, with a convolutional neural network, CNN, channel amplitudes ĝ[k] of the measured signal y[k];reconstructing and refining a channel Ĥ[k] between the receiver and a transmitter of the system based on the channel amplitudes ĝ[k] and a high resolution whiten measurement matrix ϒwr;andadjusting a parameter of the system based on the reconstructed channel Ĥ[k],wherein the channel amplitudes ĝ[k] are simultaneously estimated by the CNN.

12. The method of claim 11, wherein the receiver is a user terminal and the MIMO system is a hybrid mmWave MIMO system.

13. The method of claim 11, wherein the step of estimating comprises: calculating a correlation vector c[k] based on a correlation of the measured signal y[k];calculating a correlation matrix Cα[k] based on the correlation vector c[k];estimating a channel amplitude matrix Ĝ[k] with the CNN based on the correlation matrix Cα[k]; andforming the channel amplitudes ĝ[k] by vectorizing the channel amplitude matrix Ĝ[k].

14. The method of claim 11, wherein the step of reconstructing comprises: iteratively selecting highest amplitudes from the channel amplitudes ĝ[k] and calculating a residual r[k] as a difference between (1) a whiten form of the measured signal y[k], and (2) a projection of the measured signal y[k] along the high resolution whiten measurement matrix ϒwr,until a predetermined threshold ∈ is reached;computing a sparse channel vector hV[k] as the projection of the measured signal y[k]; andcalculating the channel Ĥ[k] based on a vectorization of the sparse channel vector hV[k], a dictionary matrix for a transmit array response of a transmitter, and a dictionary matrix for a receive array response of the receiver,wherein the high resolution is equal to or larger than 64 times a number of the receiver antennas times a number of the transmitter antennas.

15. The method of claim 11, wherein the steps of finding, estimating, and reconstructing and adjusting take place in a user terminal.

16. The method of claim 11, wherein the steps of finding, estimating, and reconstructing and adjusting take place in a base station of the system.

17. A transceiver performing a machine learning channel estimation for a multiple-input multiple-output, MIMO, system, the transceiver comprising: an interface configured to receive a measured signal y[k] at a receiver of the system; anda processor connected to the interface and configured to, find subcarriers k of the measured signal y[k],estimate, with a convolutional neural network, CNN, channel amplitudes ĝ[k] of the measured signal y[k],reconstruct and refine a channel H[k] between the receiver and a transmitter of the system based on the channel amplitudes ĝ[k] and a high resolution whiten measurement matrix ϒwr,andadjust a parameter of the system based on the reconstructed channel Ĥ[k],wherein the channel amplitudes ĝ[k] are simultaneously estimated by the CNN.

18. The transceiver of claim 17, wherein the processor is further configured to: calculate a correlation vector c[k] based on a correlation of the measured signal y[k];calculate a correlation matrix Cα[k] based on the correlation vector c[k];estimate a channel amplitude matrix Ĝ[k] with the CNN based on the correlation matrix Cα[k]; andform the channel amplitudes ĝ[k] by vectorizing the channel amplitude matrix Ĝ[k].

19. The transceiver of claim 17, wherein the processor is further configured to: iteratively select highest amplitudes from the channel amplitudes ĝ[k] and calculating a residual r[k] as a difference between (1) a whiten form of the measured signal y[k], and (2) a projection of the measured signal y[k] along the high resolution whiten measurement matrix ϒwr,until a predetermined threshold ∈ is reached;compute a sparse channel vector hV[k] as the projection of the measured signal y[k]; andcalculate the channel Ĥ[k] based on a vectorization of the sparse channel vector hV[k], a dictionary matrix for a transmit array response of a transmitter, and a dictionary matrix for a receive array response of the receiver,wherein the high resolution is equal to or larger than 64 times a number of the receiver antennas times a number of the transmitter antennas.

20. The transceiver of claim 17, further comprising: a first plurality of antennas configured to receive the measured signal; anda second plurality of antennas configured to transmit a signal.