Neural network augmentation for wireless channel estimation and tracking

Patent metadata

Field	Value
Publication number	US-2021399924-A1
Application number	US-202117349744-A
Country	US
Kind code	A1
Filing date	Jun 16, 2021
Priority date	Jun 19, 2020
Publication date	Dec 23, 2021
Grant date	—

Abstract

A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.

First claim

What is claimed is:

1 . A method performed by a communication device, comprising:
generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device; inferring, with a neural network, a residual of the initial channel estimate of the current time step; and updating the initial channel estimate of the current time step based on the residual.

2 . The method of claim 1 , in which:
the initial channel estimate of the current time step comprises a mean and a covariance; and the residual comprises a residual mean based on the mean of the initial channel estimate and a residual covariance based on the covariance of the initial channel estimate.

3 . The method of claim 1 , further comprising generating the initial channel estimate of the current time step and inferring the residual based on a channel observation of the current time step.

4 . The method of claim 3 , further comprising generating the channel observation from a pilot symbol or a data symbol, in which a waveform of the pilot symbol or the data symbol is known from decoding of a previous pilot symbol or a previous data symbol.

5 . The method of claim 3 , further comprising generating the channel observation based on a synthetic pilot estimate in an absence of a received pilot symbol.

6 . The method of claim 1 , further comprising:
generating an actual channel estimate based on updating the initial channel estimate; and decoding a second signal received on the channel based on the actual channel estimate.

7 . The method of claim 1 , further comprising generating the initial channel estimate for the current time step based on an actual channel estimate from a previous time step.

8 . The method of claim 1 , in which the neural network is a recurrent neural network.

9 . An apparatus at a communication device, comprising:
a processor; a memory coupled with the processor; and instructions stored in the memory and operable, when executed by the processor, to cause the apparatus:
to generate an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device;
to infer, with a neural network, a residual of the initial channel estimate of the current time step; and
to update the initial channel estimate of the current time step based on the residual.

10 . The apparatus of claim 9 , in which:
the initial channel estimate of the current time step comprises a mean and a covariance; and the residual comprises a residual mean based on the mean of the initial channel estimate and a residual covariance based on the covariance of the initial channel estimate.

11 . The apparatus of claim 9 , in which execution of the instructions further cause the apparatus to generate the initial channel estimate of the current time step and inferring the residual based on a channel observation of the current time step.

12 . The apparatus of claim 11 , in which execution of the instructions further cause the apparatus to generate the channel observation from a pilot symbol or a data symbol, in which a waveform of the pilot symbol or the data symbol is known from decoding of a previous pilot symbol or a previous data symbol.

13 . The apparatus of claim 11 , in which execution of the instructions further cause the apparatus to generate the channel observation based on a synthetic pilot estimate in an absence of a received pilot symbol.

14 . The apparatus of claim 9 , in which execution of the instructions further cause the apparatus:
to generate an actual channel estimate based on updating the initial channel estimate; and to decode a second signal received on the channel based on the actual channel estimate.

15 . The apparatus of claim 9 , in which execution of the instructions further cause the apparatus to generate the initial channel estimate for the current time step based on an actual channel estimate from a previous time step.

16 . The apparatus of claim 9 , in which the neural network is a recurrent neural network.

17 . A non-transitory computer-readable medium having program code recorded thereon at a communication device, the program code executed by a processor and comprising:
program code to generate an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device; program code to infer, with a neural network, a residual of the initial channel estimate of the current time step; and program code to update the initial channel estimate of the current time step based on the residual.

18 . The non-transitory computer-readable medium of claim 17 , in which:
the initial channel estimate of the current time step comprises a mean and a covariance; and the residual comprises a residual mean based on the mean of the initial channel estimate and a residual covariance based on the covariance of the initial channel estimate.

19 . The non-transitory computer-readable medium of claim 17 , in which the program code further comprises program code to generate the initial channel estimate of the current time step and inferring the residual based on a channel observation of the current time step.

20 . The non-transitory computer-readable medium of claim 19 , in which the program code further comprises program code to generate the channel observation from a pilot symbol or a data symbol, in which a waveform of the pilot symbol or the data symbol is known from decoding of a previous pilot symbol or a previous data symbol.

21 . The non-transitory computer-readable medium of claim 19 , in which the program code further comprises program code to generate the channel observation based on a synthetic pilot estimate in an absence of a received pilot symbol.

22 . The non-transitory computer-readable medium of claim 17 , in which the program code further comprises:
program code to generate an actual channel estimate based on updating the initial channel estimate; and program code to decode a second signal received on the channel based on the actual channel estimate.

23 . The non-transitory computer-readable medium of claim 17 , in which the program code further comprises program code to generate the initial channel estimate for the current time step based on an actual channel estimate from a previous time step.

24 . The non-transitory computer-readable medium of claim 17 , in which the neural network is a recurrent neural network.

25 . An apparatus at a communication device, comprising:
means for generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device; means for inferring, with a neural network, a residual of the initial channel estimate of the current time step; and means for updating the initial channel estimate of the current time step based on the residual.

26 . The apparatus of claim 25 , in which:
the initial channel estimate of the current time step comprises a mean and a covariance; and the residual comprises a residual mean based on the mean of the initial channel estimate and a residual covariance based on the covariance of the initial channel estimate.

27 . The apparatus of claim 25 , further comprising means for generating

Assignees

• Qualcomm Inc

Inventors

• Amjad Rana Ali
• Pratik Kumar
• Welling Max
• Behboodi Arash
• Soriaga Joseph Binamira

Classifications

• H04L25/023

  with extension to other symbols · CPC title

• H04L25/0254 Primary

  using neural network algorithms · CPC title

• H04L25/021

  Estimation of channel covariance · CPC title

• H04L2025/03656

  Initialisation · CPC title

• H04L25/03006

  Arrangements for removing intersymbol interference · CPC title