Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.01474

Cramér-Rao Bound Optimization for Joint Beamforming and Mode Selection in RDARS-Assisted ISAC Systems

Ahmad Reza Hassanshahi·Rouhollah Amiri·Fereidoon Behnia
View original source ↗Read source document ↗Export metadata ↓

Abstract

Integrated Sensing and Communication (ISAC) is a foundation of 6G networks, demanding architectures that simultaneously enhance sensing accuracy and communication reliability. This paper presents a Reconfigurable Distributed Antenna and Reflecting Surface (RDARS) aided ISAC framework, where an RDARS overcomes the limitations of conventional passive Reconfigurable Intelligent Surfaces (RIS) and Distributed Antenna Systems (DAS). By enabling each element to dynamically operate in either \textit{reflection} or \textit{connection} mode, RDARS synergistically harnesses reflection gain, distribution gain, and an additional mode-selection gain. We investigate the joint optimization of transmit beamforming at the base station and dynamic mode selection at the RDARS to minimize the sensing performance metric, namely the Cramér-Rao Bound (CRB) for target localization, while guaranteeing a minimum required Signal-to-Interference-plus-Noise Ratio (SINR) for multiple communication users. To solve the resulting non-convex and mixed-integer problem, we develop an efficient iterative algorithm based on the Alternating Optimization (AO) framework, effectively leveraging Majorization-Minimization (MM) and Penalty methods. Comprehensive simulations validate the proposed design, demonstrating that the dynamic RDARS configuration achieves a superior trade-off between the Position Error Bound (PEB) and communication SINR, significantly outperforming benchmark passive RIS and DAS systems.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ahmad Reza Hassanshahi, Rouhollah Amiri, Fereidoon Behnia. 2026-10-01. Cramér-Rao Bound Optimization for Joint Beamforming and Mode Selection in RDARS-Assisted ISAC Systems. https://arxiv.org/abs/2610.01474

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

KEEP EXPLORING

Related papers

arXiv2026

Unrolled RF Holographic Imaging: Structured Sparsity and Low-Rank EM Model Adaptation

Radio-Frequency (RF) holographic imaging reconstructs a volumetric map of the permittivity contrast from phase-coherent samples of the scattered electromagnetic (EM) field. The resulting inverse problem is severely ill-posed, as the receivers are orders of magnitude fewer than the unknown 3D volume elements (voxels). It is classically regularized by sparsity-promoting solvers such as the Iterative Shrinkage-Thresholding Algorithm (ISTA). Two assumptions limit these solvers: the L1 penalty is spatially uniform, and the EM forward model is typically approximated as a linear operator. This paper revisits the problem through algorithm unrolling, in which the solver iterations become the layers of a compact trainable network that preserves the EM forward model and learns only a few interpretable parameters from limited data. Building on the Learned ISTA (LISTA), a Weighted LISTA (W-LISTA) is first proposed, which learns a spatially-varying L1 regularization, steering the sparsity prior towards target shapes consistent with the deployment. Second, the Low-Rank Weighted LISTA (LoRaW-LISTA) applies a low-rank adaptation (LoRa) of the holographic operator to compensate for model mismatches from linearized EM approximations. Both methods are validated on full-wave EM simulations and on a 2.45GHz indoor measurement campaign with human-body phantoms. Combining the spatially-varying regularization with the low-rank adaptation of the EM model improves the signal-to-clutter ratio by about 70% over the ISTA and LISTA baselines, recovering structural details where classical iterative solvers fail. Inference takes less than 30s to reconstruct 1m^3 of scene on conventional GPUs. The proposed tools are rapidly adaptable building blocks for the sensing layer of emerging smart radio environments.

eess.SP↗
arXiv2026

Spatial Thickness Mapping in Heterogeneous Plate Using Wave Physics-Informed Regression

Traditional guided wave methods for structural health monitoring typically assume uniform material properties, which limit their ability to characterize heterogeneous structures with spatially varying thickness, damage, or material properties. These are challenges commonly encountered in corrosion assessment, composite delamination detection, and structural degradation monitoring. This paper presents a wave physics-informed regression approach that enables spatially resolved characterization of material properties by extracting local dispersion curves across a structure. Our approach focuses on a highly interpretable but flexible physics-informed framework that can be solved using fast algorithms and achieve robust numerical solutions. This paper discusses the mathematical design of the framework, the algorithm, and its interpretation. The framework was applied to a guided wave wavefield imaging dataset from a thin aluminum plate with non-uniform thickness around a hole to validate its practicality. The framework creates an accurate thickness map (correlation coefficient 0.94 with x-ray CT validation) as well as extracts the frequency-dependent velocities of waves within those regions.

eess.SP↗
arXiv2026

Physics-Guided Bayesian Optimization for High-Dimensional Mixed-Variable MIMO Base Station Design

This paper proposes a physics-guided Bayesian optimization for high-dimensional mixed-variable multiple-input and multiple-output (MIMO) base station (BS) design. The considered problem jointly selects a subset of candidate sites for BS deployment and optimizes the azimuth angles, downtilt angles, and transmit power spectral densities of the BSs, while each configuration is evaluated using computationally expensive site-specific ray tracing. To efficiently optimize the system configuration, the proposed method constructs a low-cost physics-based proxy from precomputed propagation information. The proxy-estimated communication coverage is used as the Gaussian process (GP) prior mean, and a residual GP with three-dimensional physical features learns the discrepancy between the proxy and full evaluations. Ray-tracing-based evaluations in two urban scenarios show that the proposed method achieves up to approximately 15 percentage points higher coverage than conventional and high-dimensional optimization baselines under the same evaluation budget.

eess.SP↗