Frequency-selective beamforming and single-shot beam training with dynamic metasurface antennas

TL;DR

This paper introduces a method to optimize frequency and element configuration in dynamic metasurface antennas to enhance beamforming gain and develop a single-shot beam training technique using limited frequency states.

Contribution

It presents a novel approach to exploit DMA frequency reconfigurability for joint beam training and beamforming, reducing complexity compared to traditional methods.

Findings

Limited resonant frequency states suffice for effective beam training and beamforming.

The proposed method achieves high beamforming gain with fewer frequency configurations.

Single OFDM pilot signal is enough for accurate angular direction estimation.

Abstract

Dynamic metasurface antennas (DMAs) beamform through low-powered components that enable reconfiguration of each radiating element. Previous research on a single-user multiple-input-single-output (MISO) system with a dynamic metasurface antenna at the transmitter has focused on maximizing the beamforming gain at a fixed operating frequency. The DMA, however, has a frequency-selective response that leads to magnitude degradation for frequencies away from the resonant frequency of each element. This causes reduction in beamforming gain if the DMA only operates at a fixed frequency. We exploit the frequency reconfigurability of the DMA to dynamically optimize both the operating frequency and the element configuration, maximizing the beamforming gain. We leverage this approach to develop a single-shot beam training procedure using a DMA sub-array architecture that estimates the receiver's angular direction with a single OFDM pilot signal. We evaluate the beamforming gain performance of the DMA array using the receiver's angular direction estimate obtained from beam training. Our results show that it is sufficient to use a limited number of resonant frequency states to do both beam training and beamforming instead of using an infinite resolution DMA beamformer.