Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2504.15737

Energy-Efficient SIM-assisted Communications: How Many Layers Do We Need?

Enyu Shi·Jiayi Zhang·Jiancheng An·Marco Di Renzo·Bo Ai·Chau Yuen
View original source ↗Read source document ↗Export metadata ↓

Abstract

The stacked intelligent metasurface (SIM), comprising multiple layers of reconfigurable transmissive metasurfaces, is becoming an increasingly viable solution for future wireless communication systems. In this paper, we explore the integration of SIM in a multi-antenna base station for application to downlink multi-user communications, and a realistic power consumption model for SIM-assisted systems is presented. Specifically, we focus on maximizing the energy efficiency (EE) for hybrid precoding design, i.e., the base station digital precoding and SIM wave-based beamforming. Due to the non-convexity and high complexity of the formulated problem, we employ the quadratic transformation method to reformulate the optimization problem and propose an alternating optimization (AO)-based joint precoding framework. Specifically, a successive convex approximation (SCA) algorithm is adopted for the base station precoding design. For the SIM wave-based beamforming, two algorithms are employed: the high-performance semidefinite programming (SDP) method and the low-complexity projected gradient ascent (PGA) algorithm. In particular, the results indicate that while the optimal number of SIM layers for maximizing the EE and spectral efficiency differs, a design of 2 to 5 layers can achieve satisfactory performance for both. Finally, numerical results are illustrated to evaluate the effectiveness of the proposed hybrid precoding framework and to showcase the performance enhancement achieved by the algorithm in comparison to benchmark schemes.

Explore related subjects

cs.ITeess.SPmath.IT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Enyu Shi, Jiayi Zhang, Jiancheng An, Marco Di Renzo, Bo Ai, Chau Yuen. 2025-04-22. Energy-Efficient SIM-assisted Communications: How Many Layers Do We Need?. https://arxiv.org/abs/2504.15737

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads

Integrated sensing and communication (ISAC) plays a crucial role in 6G, to enable innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilot and random data payload components, poses challenges for target detection due to two reasons: 1) these two components cause coupled shifts in both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in the bistatic setting. Unfortunately, these challenges could not be tackled by existing target detection algorithms. In this paper, a generalized likelihood ratio test (GLRT)-based detector is derived, by leveraging the known deterministic pilots and the statistical characteristics of the unknown random data payloads. Due to the analytical intractability of exact performance characterization, we perform an asymptotic analysis for the false alarm probability and detection probability of the proposed detector. The results highlight a critical trade-off: both deterministic and random components improve detection reliability, but the latter also brings statistical uncertainty that hinders detection performance. Simulations validate the theoretical findings and demonstrate the effectiveness of the proposed detector, which highlights the necessity of designing a dedicated detector to fully exploited the signaling resources assigned to random data payloads.

cs.IT↗
arXiv2026

On Unbiased Parameter Estimation and Signal Reconstruction

In this paper, we extend the theory of depth-unbiased source localization to unbiased parameter estimation and signal reconstruction for an arbitrary number of non-zero parameters. The topic touches on exact reconstructibility, most commonly studied in compressed sensing and multisource estimation across various imaging problems. The theoretical results derive upper bounds on the number of recoverable parameters in the noiseless case, and define a probability measure to assess the likelihood of recovering all non-zero parameters with correct magnitude order. The work provides a mathematical explanation of the open question regarding the noise robustness of standardized and unbiased methods. The paper also reveals a trade-off between the number of sensors and the signal-to-noise ratio. Numerical experiments demonstrate the theoretical findings.

cs.IT↗
arXiv2026

Minimum enclosing Bregman balls made easy

In this work, we revisit the problem of computing minimum enclosing Bregman balls (Bregman MEBs) of finite sets of parameters. First, we show that Bregman MEBs are equivalent to MEBs of corresponding weighted point sets with respect to the power distance. We then report an efficient Frank--Wolfe $(1+ε)$-approximation algorithm for computing power MEBs, for any $ε>0$. This power MEB approximation algorithm coincides with the Bregman MEB approximation algorithm of Nock and Nielsen (2005) when expressed in the dual gradient space. Finally, we show that the Bregman potential lifting transforms used to construct Bregman Voronoi diagrams can be reinterpreted as the classical paraboloid lifting transform applied to corresponding weighted point sets. In particular, Bregman MEB circumcenters lie on the farthest Bregman Voronoi diagrams or equivalently on the corresponding farthest power diagrams.

cs.IT↗