# US 2026/0074872 A1 · Artificial intelligence based channel inpainting for wireless communication systems

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- Number: US 2026/0074872 A1
- Document ID: US20260074872A1
- Status: Published application
- Kind: A1
- Application number: 19/304,539
- Filed: 2025-08-19
- Published: 2026-03-12
- Earliest claimed: 2024-09-10
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Fan Zhang; Usman Akram; Yang Li; Xiaochuan Ma
- Web page: https://patentgrep.com/patent/US20260074872A1

## CPC

- H04L 5/0094: {Indication of how sub-channels of the path are allocated}
- H04L 5/005: {of common pilots, i.e. pilots destined for multiple users or terminals}
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A method includes: transmitting, by a first electronic device, configuration information for sounding reference signals (SRSs) to second electronic devices, the configuration information including non-uniform sampling patterns for the SRSs; receiving, by the first electronic device, non-uniformly sampled SRSs over respective channels from the second electronic devices based on the configuration information; preprocessing the non-uniformly sampled SRSs to extract channel state information of the channels; and reconstructing, using an artificial intelligence model trained to perform channel inpainting, a full band SRS for each of the channels based on the channel state information.

## Claims

### Claim 1

A method comprising:
transmitting, by a first electronic device, configuration information for sounding reference signals (SRSs) to second electronic devices, the configuration information including non-uniform sampling patterns for the SRSs;
receiving, by the first electronic device, non-uniformly sampled SRSs over respective channels from the second electronic devices based on the configuration information;
preprocessing the non-uniformly sampled SRSs to extract channel state information of the channels; and
reconstructing, using an artificial intelligence (AI) model trained to perform channel inpainting, a full band SRS for each of the channels based on the channel state information.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
identifying a sub-band including the non-uniformly sampled SRSs from allocated resource symbols for each of the channels;
combining identified sub-bands from the resource symbols to generate a sparse SRS band for each of the channels;
inputting the sparse SRS band of each of the channels to the AI model;
applying, using the AI model, the channel inpainting to reconstruct the full band SRS of each of the channels; and
performing a wireless communication task based on the full band SRSs of the channels.

### Claim 3 (depends on 2)

The method of claim 2, wherein reconstructing the full band SRS comprises:
dividing a two-dimensional (2D) image of the sparse SRS band into patches to generate patch embeddings;
masking patch embeddings representing an unsounded portion of the sparse SRS band;
processing unmasked patch embeddings representing an unmasked portion of the sparse SRS band using first transformer blocks;
decoding the unmasked patch embeddings and adding zero vectors to the masked patch embeddings to generate a vector sequence representing the unmasked patch embeddings and the masked patch embeddings;
processing the vector sequence using second transformer blocks to predict SRS for the masked patch embeddings based on self-attention; and
reconstructing the full band SRS based on the processed vector sequence.

### Claim 4 (depends on 3)

The method of claim 3, wherein:
the sparse SRS band input to the AI model is in a frequency and antenna domain; and
the 2D image of the sparse SRS band is divided into the patches in the frequency and antenna domain.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
after dividing the 2D image into the patches, applying a discrete Fourier transform (DFT) to the patches to convert the patches from the frequency and antenna domain to an antenna and delay domain; or
after dividing the 2D image into the patches, applying the DFT to the patches to convert the patches from the frequency and antenna domain to the antenna and delay domain and concatenating the converted patches to the sparse SRS band input to the AI model.

### Claim 6 (depends on 2)

The method of claim 2, wherein the AI model is trained by:
calculating a loss function using at least one of a sounded portion of the sparse SRS band or an unsounded portion of the sparse SRS band; or
denoising the sounded portion of the sparse SRS band.

### Claim 7 (depends on 2)

The method of claim 2, wherein the AI model is trained by:
injecting a noiseless channel with a timing and frequency offset (TFO) impairment to a model input to the AI model and an input to a label generation module, the model input comprising the sparse SRS band;
adding noise to the model input;
masking an unsounded portion of the sparse SRS band;
applying a TFO estimation and compensation to the model input;
compensating generated labels with the TFO estimation; and
aligning the model input and the labels in time and frequency.

### Claim 8

A first electronic device comprising:
memory; and
a processor operably coupled to the memory, the processor configured to:
transmit configuration information for sounding reference signals (SRSs) to second electronic devices, the configuration information including non-uniform sampling patterns for the SRSs;
receive non-uniformly sampled SRSs over respective channels from the second electronic devices based on the configuration information;
preprocess the non-uniformly sampled SRSs to extract channel state information of the channels; and
reconstruct, using an artificial intelligence (AI) model trained to perform channel inpainting, a full band SRS for each of the channels based on the channel state information.

### Claim 9 (depends on 8)

The first electronic device of claim 8, wherein the processor is further configured to:
identify a sub-band including the non-uniformly sampled SRSs from allocated resource symbols for each of the channels;
combine identified sub-bands from the resource symbols to generate a sparse SRS band for each of the channels;
input the sparse SRS band of each of the channels to the AI model;
apply, using the AI model, the channel inpainting to reconstruct the full band SRS of each of the channels; and
perform a wireless communication task based on the full band SRSs of the channels.

### Claim 10 (depends on 9)

The first electronic device of claim 9, wherein to reconstruct the full band SRS, the processor is further configured to:
divide a two-dimensional (2D) image of the sparse SRS band into patches to generate patch embeddings;
mask patch embeddings representing an unsounded portion of the sparse SRS band;
process unmasked patch embeddings representing an unmasked portion of the sparse SRS band using first transformer blocks;
decode the unmasked patch embeddings and adding zero vectors to the masked patch embeddings to generate a vector sequence representing the unmasked patch embeddings and the masked patch embeddings;
process the vector sequence using second transformer blocks to predict SRS for the masked patch embeddings based on self-attention; and
reconstruct the full band SRS based on the processed vector sequence.

### Claim 11 (depends on 10)

The first electronic device of claim 10, wherein:
the sparse SRS band input to the AI model is in a frequency and antenna domain; and
the 2D image of the sparse SRS band is divided into the patches in the frequency and antenna domain.

### Claim 12 (depends on 11)

The first electronic device of claim 11, wherein the processor is further configured to:
after dividing the 2D image into the patches, applying a discrete Fourier transform (DFT) to the patches to convert the patches from the frequency and antenna domain to an antenna and delay domain; or
after dividing the 2D image into the patches, applying the DFT to the patches to convert the patches from the frequency and antenna domain to the antenna and delay domain and concatenating the converted patches to the sparse SRS band input to the AI model.

### Claim 13 (depends on 9)

The first electronic device of claim 9, wherein AI model is trained by at least one of:
calculating a loss function using at least one of a sounded portion of the sparse SRS band or an unsounded portion of the sparse SRS band; or
denoising the sounded portion of the sparse SRS band.

### Claim 14 (depends on 9)

The first electronic device of claim 9, wherein AI model is trained by:
injecting a noiseless channel with a timing and frequency offset (TFO) impairment to a model input to the AI model and an input to a label generation module, the model input comprising the sparse SRS band;
adding noise to the model input;
masking an unsounded portion of the sparse SRS band;
applying a TFO estimation and compensation to the model input;
compensating generated labels with the TFO estimation; and
aligning the model input and the labels in time and frequency.

### Claim 15

A non-transitory computer readable medium embodying a computer program, the computer program comprising program code that, when executed by a processor of a first electronic device, causes the first electronic device to:
transmit configuration information for sounding reference signals (SRSs) to second electronic devices, the configuration information including non-uniform sampling patterns for the SRSs;
receive non-uniformly sampled SRSs over respective channels from the second electronic devices based on the configuration information;
preprocess the non-uniformly sampled SRSs to extract channel state information of the channels; and
reconstruct, using an artificial intelligence (AI) model trained to perform channel inpainting, a full band SRS for each of the channels based on the channel state information.

### Claim 16 (depends on 15)

The non-transitory computer readable medium of claim 15, further comprising program code that, when executed by the processor of the first electronic device, causes the first electronic device to:
identify a sub-band including the non-uniformly sampled SRSs from allocated resource symbols for each of the channels;
combine identified sub-bands from the resource symbols to generate a sparse SRS band for each of the channels;
input the sparse SRS band of each of the channels to the AI model;
apply, using the AI model, the channel inpainting to reconstruct the full band SRS of each of the channels; and
perform a wireless communication task based on the full band SRSs of the channels.

### Claim 17 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein:
the program code that, when executed by the processor of the first electronic device, causes the first electronic device to reconstruct the full band SRS comprises program code that, when executed by the processor of the first electronic device, causes the first electronic device to:
divide a two-dimensional (2D) image of the sparse SRS band into patches to generate patch embeddings;
mask patch embeddings representing an unsounded portion of the sparse SRS band;
process unmasked patch embeddings representing an unmasked portion of the sparse SRS band using first transformer blocks;
decode the unmasked patch embeddings and adding zero vectors to the masked patch embeddings to generate a vector sequence representing the unmasked patch embeddings and the masked patch embeddings;
process the vector sequence using second transformer blocks to predict SRS for the masked patch embeddings based on self-attention; and
reconstruct the full band SRS based on the processed vector sequence.

### Claim 18 (depends on 17)

The non-transitory computer readable medium of claim 17, wherein:
the sparse SRS band input to the AI model is in a frequency and antenna domain; and
the 2D image of the sparse SRS band is divided into the patches in the frequency and antenna domain.

### Claim 19 (depends on 18)

The non-transitory computer readable medium of claim 18, further comprising program code that, when executed by the processor of the first electronic device, causes the first electronic device to:
after dividing the 2D image into the patches, applying a discrete Fourier transform (DFT) to the patches to convert the patches from the frequency and antenna domain to an antenna and delay domain; or
after dividing the 2D image into the patches, applying the DFT to the patches to convert the patches from the frequency and antenna domain to the antenna and delay domain and concatenating the converted patches to the sparse SRS band input to the AI model.

### Claim 20 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein the AI model is trained by:
injecting a noiseless channel with a timing and frequency offset (TFO) impairment to a model input to the AI model and an input to a label generation module, the model input comprising the sparse SRS band;
adding noise to the model input;
masking an unsounded portion of the sparse SRS band;
applying a TFO estimation and compensation to the model input;
compensating generated labels with the TFO estimation; and
aligning the model input and the labels in time and frequency.