Spectral Efficiency Maximization for DMA-enabled Multiuser MISO with Statistical CSI

2025-06-11

AI Summary

This work addresses the downlink/uplink ergodic sum-rate maximization problem for a DMA-empowered multi-user MISO system under practical statistical CSI-only knowledge, where instantaneous channel state information is unavailable. To tackle this challenge, we jointly optimize the DMA’s analog beamforming weights and the base station’s digital precoding/combining. Our key contributions are threefold: (i) we derive, for the first time, a closed-form analytical surrogate for the ergodic rate of DMA-MISO systems; (ii) we propose a novel two-stage algorithm integrating Weighted Minimum Mean Square Error (WMMSE) and Penalty Dual Decomposition (PDD), enabling efficient spectral efficiency optimization without instantaneous CSI; and (iii) the proposed method exhibits rapid convergence and high accuracy, significantly outperforming existing benchmarks in fast-fading environments. Numerical results validate the practical viability and deployment potential of DMAs in low-overhead, large-scale MIMO systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Dynamic metasurface antennas (DMAs) offer the potential to achieve large-scale antenna arrays with low power consumption and reduced hardware costs, making them a promising technology for future communication systems. This paper investigates the spectral efficiency (SE) of DMA-enabled multiuser multiple-input single-output (MISO) systems in both uplink and downlink transmissions, using only statistical channel state information (CSI) to maximize the ergodic sum rate of multiple users. For the uplink system, we consider two decoding rules: minimum mean square error (MMSE) with and without successive interference cancellation (SIC). For both decoders, we derive closed-form surrogates to substitute the original expressions of ergodic sum rate and formulate tractable optimization problems for designing DMA weights. Then, a weighted MMSE (WMMSE)-based algorithm is proposed to maximize the ergodic sum rate. For the downlink system, we derive an approximate expression for the ergodic sum rate and formulate a hybrid analog/digital beamforming optimization problem that jointly optimizes the digital precoder and DMA weights. A penalty dual decomposition (PDD)-based algorithm is proposed by leveraging the fractional programming framework. Numerical results validate the accuracy of the derived surrogates and highlight the superiority of the proposed algorithms over baseline schemes. It is shown that these algorithms are effective across various DMA settings and are particularly well-suited for system design in fast time-varying channels.

Problem

• Maximize spectral efficiency in DMA-enabled MISO systems
• Optimize DMA weights using statistical CSI for uplink/downlink
• Develop algorithms for ergodic sum rate maximization

Innovation

• DMA-enabled MISO with statistical CSI
• WMMSE-based uplink algorithm
• PDD-based downlink beamforming optimization