Beamforming Inferring by Conditional WGAN-GP for Holographic Antenna Arrays

TL;DR

This paper introduces a novel AI-based beamforming inference method using a conditional Wasserstein GAN with gradient penalty, significantly reducing overhead while maintaining near-optimal performance for holographic antenna arrays.

Contribution

It proposes a new GAN-based scheme for beamforming inference that requires minimal channel information, reducing overhead by over 50% compared to traditional methods.

Findings

Achieves comparable performance to weighted MMSE algorithm.

Reduces channel estimation and beamforming overhead by over 50%.

Demonstrates effectiveness with simulation results.

Abstract

The beamforming technology with large holographic antenna arrays is one of the key enablers for the next generation of wireless systems, which can significantly improve the spectral efficiency. However, the deployment of large antenna arrays implies high algorithm complexity and resource overhead at both receiver and transmitter ends. To address this issue, advanced technologies such as artificial intelligence have been developed to reduce beamforming overhead. Intuitively, if we can implement the near-optimal beamforming only using a tiny subset of the all channel information, the overhead for channel estimation and beamforming would be reduced significantly compared with the traditional beamforming methods that usually need full channel information and the inversion of large dimensional matrix. In light of this idea, we propose a novel scheme that utilizes Wasserstein generative adversarial network with gradient penalty to infer the full beamforming matrices based on very little of channel information. Simulation results confirm that it can accomplish comparable performance with the weighted minimum mean-square error algorithm, while reducing the overhead by over 50%.