US-20260128929-A1 - WASSERSTEIN GENERATIVE ADVERSARIAL NETWORKS FOR FREQUENCY-DOMAIN CHANNEL ESTIMATION

Abstract

Methods and devices of a wireless system are provided. A generator in a generative adversarial network (GAN) is trained using generator training data including cluster delay line (CDL) channel samples. The generator is trained to generate a trained generator and generated samples. The GAN is configured to transform, through utilization of a Fourier transform, sample, and truncate the generator training data as latent input to the generator. A discriminator in the GAN is trained using discriminator training data including CDL channel samples. A signal is received at a UE receiver. A CDL channel at the UE receiver is estimated in a frequency domain using a received noisy DMRS through utilization of the trained generator. The trained generator uses truncated, sampled Fourier transform representations of the received signal as latent input. Based on the estimated channel, an equalized signal is generated from the received signal.

Inventors

• Mirette Sadek
• Jung Hyun Bae
• Dongwoon Bai

Assignees

• SAMSUNG ELECTRONICS CO., LTD.

Dates

Publication Date

20260507

Application Date

20251027

Claims (20)

1. 1 . A method comprising: receiving a signal at a user equipment (UE) receiver; estimating a cluster delay line (CDL) channel at the receiver in a frequency domain using a received noisy demodulation reference signal (DMRS) through utilization of a trained generator in a generative adversarial network (GAN), the trained generator using truncated, sampled Fourier transform representations of the received signal as latent input; and using the estimate of the CDL channel, generating an equalized signal from the received signal.
2. 2 . The method of claim 1 , further comprising training, using generator training data including CDL channel samples, a generator to generate the trained generator.
3. 3 . The method of claim 1 , wherein the DMRS is carried by an orthogonal frequency division multiplexing (OFDM) symbol.
4. 4 . The method of claim 1 , further comprising training, using discriminator training data including CDL channel samples, a discriminator in the GAN.
5. 5 . The method of claim 2 , further comprising modifying a generator loss function during the training of the generator to include a normalized mean square error (NMSE).
6. 6 . The method of claim 5 , wherein the NMSE is a result from using a current iteration of the trained generator.
7. 7 . A user equipment (UE) comprising: at least one processor; a receiver; and memory that stores instructions, which when executed by the at least one processor, control the UE to: receive a signal at the receiver; estimate a cluster delay line (CDL) channel received at the receiver in a frequency domain using a received noisy demodulation reference signal (DMRS) through utilization of a trained generator, the trained generator using truncated, sampled Fourier transform representations of the received signal as latent input; and generate an equalized signal from the received signal using the estimate of the CDL channel.
8. 8 . The UE of claim 7 , wherein the trained generator is trained using generator training data including CDL channel samples.
9. 9 . The UE of claim 7 , wherein the DMRS is carried by an orthogonal frequency division multiplexing (OFDM) symbol.
10. 10 . The UE of claim 7 , wherein the generator is trained in a generative adversarial network (GAN).
11. 11 . The UE of claim 10 , wherein the GAN is configured to transform, through utilization of a Fourier transform, sample, and truncate generator training data as latent input to the generator.
12. 12 . The UE of claim 10 , wherein the GAN includes a discriminator, and the discriminator is trained using discriminator training data including CDL channel samples.
13. 13 . The UE of claim 8 , wherein a generator loss function is modified during the training of the generator to include a normalized mean square error (NMSE).
14. 14 . A method performed by a user equipment (UE), the method comprising: receiving a noisy demodulation reference signal (DMRS); estimating a CDL channel in a frequency domain using the received DMRS through utilization of a trained generator, the trained generator using truncated, sampled Fourier transform representations of the received signal as latent input; and using the estimate of the CDL channel, generating an equalized signal from the received signal.
15. 15 . The method of claim 14 , wherein the trained generator is trained using generator training data including CDL channel samples.
16. 16 . The method of claim 14 , wherein the DMRS is carried by an orthogonal frequency division multiplexing (OFDM) symbol.
17. 17 . The method of claim 14 , wherein the generator is trained in a generative adversarial network (GAN).
18. 18 . The method of claim 17 , wherein the GAN is configured to transform, through utilization of a Fourier transform, sample, and truncate generator training data as latent input to the generator.
19. 19 . The method of claim 17 , wherein the GAN includes a discriminator, and the discriminator is trained using discriminator training data including CDL channel samples.
20. 20 . The method of claim 15 , wherein a generator loss function is modified during the training of the generator to include a normalized mean square error (NMSE).

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims the priority benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/716,498, filed on Nov. 5, 2024, the disclosure of which is incorporated by reference in its entirety as if fully set forth herein. TECHNICAL FIELD The disclosure generally relates to wireless communications. More particularly, the subject matter disclosed herein relates to improvements in machine learning for channel estimation. SUMMARY In wireless systems, channel estimation (CE) may be used to estimate the effects of a channel on data transmitted through a wireless medium. Channel estimation may be used for equalization, beamforming, and/or adaptive modulation. Some algorithms for channel estimation may be configured for analog channels. Some algorithms for channel estimation may be configured for time-invariant channels. Some algorithms for channel estimation may be configured for frequency-flat channels. Algorithms for channel estimation such as, for example, linear minimum mean squared error (LMMSE), may be highly dependent on the validity of the channel model assumed and the accuracy of the model parameters used for estimation. For a tapped delay line (TDL) channel model, performance of conventional algorithms may be sensitive to parameter estimates such as, for example, power delay profile (PDP) and signal-to-noise ratio (SNR). For a cluster delay line (CDL) channel model, conventional approaches often fall short due to model inaccuracy and/or model complexity. Frequency-domain (FD) channel estimation typically assumes some well-known stochastic channel model, and target estimating model parameters in order to adapt a basic algorithm. These model parameters may include a channel maximum delay spread, a complete PDP, and/or a Doppler frequency. Channel estimation algorithms often fall short when accurate channel model parameters estimation is not guaranteed. Channel estimation algorithms often fall short when a channel model is approximate. Channel estimation algorithms often fall short when channel models are too complex and need too many parameters. Channel estimation algorithms also may not achieve desired channel estimation quality. Systems and methods are described herein for learning-based channel estimation. Learning-based channel estimation may provide higher estimation quality than conventional approaches. Disclosed systems and methods may include transforming generator input as latent input to a generator. Disclosed systems and methods may improve on previous methods because latent vector input may lead to better inference in terms of normalized mean squared error (NMSE). Therefore, the disclosed systems and methods may offer improvements to channel estimation quality over conventional approaches. Disclosed systems and methods may outperform conventional approaches in terms of uncoded bit error rate (UCBER), coded bit error rate (BER), and/or block error rate (BLER). In an embodiment, a method comprises training, using generator training data including CDL channel samples, a generator in a generative adversarial network (GAN) to generate a trained generator and generated samples. The GAN is configured to transform, through utilization of a Fourier transform, sample, and truncate the generator training data as latent input to the generator. The method comprises training, using discriminator training data including CDL channel samples, a discriminator in the GAN. The method comprises receiving a signal at a user equipment (UE) receiver. The method comprises estimating a CDL channel at the UE receiver in a frequency domain using a received noisy demodulation reference signal (DMRS) through utilization of the trained generator. The trained generator uses truncated, sampled Fourier transform representations of the received signal as latent input. The method comprises using the estimate of the CDL channel, generating an equalized signal from the received signal. In an embodiment, a UE comprises at least one processor, a receiver, and memory that stores instructions, which when executed by the at least one processor, control the UE to receive a signal at the receiver. The instructions, when executed by the at least one processor, control the UE to estimate a CDL channel received at the receiver in a frequency domain using a received noisy DMRS through utilization of a trained generator. The trained generator uses truncated, sampled Fourier transform representations of the received signal as latent input. The instructions, when executed by the at least one processor, control the UE to generate an equalized signal from the received signal using the estimate of the CDL channel. In an embodiment, a method is performed by a UE. The method comprises receiving a noisy DMRS. The method comprises estimating a CDL channel in a frequency domain using the received DMRS through utilization of a trained generator. The trained generator uses truncated, sampled Fourier