Double-Side Polarization and Beamforming Alignment in Polarization Reconfigurable MISO System with Deep Neural Networks

TL;DR

This paper introduces a deep neural network-based approach to optimize polarization and beamforming in reconfigurable MISO systems, reducing pilot overhead and improving gain without explicit channel estimation.

Contribution

It proposes a novel DNN-based method for direct polarization and beamforming optimization, outperforming traditional schemes in reconfigurable MISO systems.

Findings

Achieves up to 20% higher beamforming gain compared to conventional methods.

Reduces pilot training overhead by eliminating explicit channel estimation.

Demonstrates effectiveness of DNNs in polarization and beamforming optimization.

Abstract

Polarization reconfigurable (PR) antennas enhance spectrum and energy efficiency between next-generation node B(gNB) and user equipment (UE). This is achieved by tuning the polarization vectors for each antenna element based on channel state information (CSI). On the other hand, degree of freedom increased by PR antennas yields a challenge in channel estimation with pilot training overhead. This paper pursues the reduction of pilot overhead, and proposes to employ deep neural networks (DNNs) on both transceiver ends to directly optimize the polarization and beamforming vectors based on the received pilots without the explicit channel estimation. Numerical experiments show that the proposed method significantly outperforms the conventional first-estimate-then-optimize scheme by maximum of 20% in beamforming gain.