Lorentzian-Constrained Holographic Beamforming Optimization in Multi-user Networks with Dynamic Metasurface Antennas

TL;DR

This paper introduces novel holographic beamforming algorithms for dynamic metasurface antennas in multi-user networks, effectively reducing power consumption and improving scalability by addressing Lorentzian constraints.

Contribution

It proposes the GMLCH and ARLCH algorithms for resource allocation in DMA-based MISO networks, enhancing beamforming performance under Lorentzian constraints.

Findings

ARLCH reduces power consumption by over 20% compared to benchmarks.

ARLCH scales better with increasing number of users.

The proposed algorithms improve beamforming efficiency in dense networks.

Abstract

Dynamic metasurface antennas (DMAs) are promising alternatives to fully digital (FD) architectures, enabling hybrid beamforming via low-cost reconfigurable metasurfaces. In DMAs, holographic beamforming is achieved through tunable elements by Lorentzian-constrained holography (LCH), significantly reducing the need for radio-frequency (RF) chains and analog circuitry. However, the Lorentzian constraints and limited RF chains introduce a trade-off between reduced system complexity and beamforming performance, especially in dense network scenarios. This paper addresses resource allocation in multi-user multiple-input-single-output (MISO) networks under the Signal-to-Interference-plus-Noise Ratio (SINR) constraints, aiming to minimize total transmit power. We propose a holographic beamforming algorithm based on the Generalized Method of Lorentzian-Constrained Holography (GMLCH), which optimizes DMA weights, yielding flexibility for using various LCH techniques to tackle the aforementioned trade-offs. Building upon GMLCH, we further propose a new algorithm i.e., Adaptive Radius Lorentzian Constrained Holography (ARLCH), which achieves optimization of DMA weights with additional degree of freedom in a greater optimization space, and provides lower transmitted power, while improving scalability for higher number of users. Numerical results show that ARLCH reduces power consumption by over 20\% compared to benchmarks, with increasing effectiveness as the number of users grows.