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

2025-05-13

AI Summary

This work addresses the holographic beamforming problem in DMA-enabled multi-user MISO networks, aiming to minimize the base station’s total transmit power subject to per-user SINR constraints, while jointly accounting for Lorentzian-domain hardware limitations and the performance–complexity trade-off induced by finite RF chains. We propose the Generalized Lorentzian-Constrained Holographic Beamforming (GMLCH) framework, which jointly models the electromagnetic response of dynamic metasurface antennas (DMAs) and RF hardware constraints. Within this framework, we design the Adaptive Radius Lorentzian-Constrained Holographic (ARLCH) algorithm, which dynamically adjusts the tightness of Lorentzian constraints to expand the feasible solution space. Compared to benchmark schemes, ARLCH reduces transmit power by over 20% while guaranteeing user QoS; the gains become more pronounced as user density increases. This demonstrates ARLCH’s superior efficiency, scalability, and energy efficiency in dense deployment scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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, 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.

Problem

• Optimize holographic beamforming in multi-user MISO networks with DMA constraints
• Minimize transmit power under SINR constraints with Lorentzian-constrained holography
• Enhance scalability and reduce power consumption in dense user scenarios

Innovation

• Dynamic metasurface antennas enable low-cost holographic beamforming
• GMLCH optimizes DMA weights under Lorentzian constraints
• ARLCH enhances power efficiency and scalability in dense networks