RHOSI: Efficient Anti-Jamming Resource Allocation with Holographic Surfaces in UAV-enabled ISAC

This paper investigates the susceptibility of Integrated Sensing and Communication (ISAC) systems to hostile jamming, focusing on an aerial Reconfigurable Holographic Surface (RHS)-aided unmanned aerial vehicle (UAV). The proposed framework, termed RHOSI, enhances ISAC's resilience by dynamically shaping the wireless propagation environment. Specifically, RHOSI introduces a strategy to improve jamming resistance by jointly optimizing transmit beamforming at the hybrid base station, RHS phase shift configuration, and UAV spatial deployment, while ensuring the required echo signal-to-interference-plus-noise ratios for reliable sensing. The resulting non-linear optimization problem features highly coupled variables, which are decomposed into sub-problems and solved using an alternating optimization (AO) approach. Simulation results confirm the practicality and effectiveness of RHOSI in significantly improving the throughput and robustness of ISAC under adversarial jamming.

Flexible RISs: Learning-based Array Manifold Estimation and Phase-shift Optimization

Reconfigurable intelligent surfaces (RISs) are envisioned as a key enabler for next-generation wireless networks, offering programmable control over propagation environments. While extensive research focuses on planar RIS architectures, practical deployments often involve non-planar surfaces, such as structural columns or curved facades, where standard planar beamforming models fail. Moreover, existing analytical solutions for curved RISs are often restricted to specific, pre-defined array manifold geometries. To address this limitation, this paper proposes a novel deep learning (DL) framework for optimizing the phase shifts of non-planar RISs. We first introduce a low-dimensional parametric model to capture arbitrary surface curvature effectively. Based on this, we design a neural network (NN) that utilizes a sparse set of received power measurements to estimate the surface geometry and derive the optimal phase configuration. Simulation results demonstrate that the proposed algorithm converges fast and significantly outperforms conventional planar beamforming designs, validating its robustness against arbitrary surface curvature. We also analyze the impact of the measurement location error on the algorithm's performance.

Shape Adaptive Reconfigurable Holographic Surfaces

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key solution to dynamically adjust wireless propagation by tuning the reflection coefficients of large arrays of passive elements. Reconfigurable Holographic Surfaces (RHS) build on the same foundation as RIS but extend it by employing holographic principles for finer-grained wave manipulation | that is, applying higher spatial control over the reflected signals for more precise beam steering. In this paper, we investigate shape-adaptive RHS deployments in a multi-user network. Rather than treating each RHS as a uniform reflecting surface, we propose a selective element activation strategy that dynamically adapts the spatial arrangement of deployed RHS regions to a subset of predefined shapes. In particular, we formulate a system throughput maximization problem that optimizes the shape of the selected RHS elements, active beamforming at the access point (AP), and passive beamforming at the RHS to enhance coverage and mitigate signal blockage. The resulting problem is non-convex and becomes even more challenging to solve as the number of RHS and users increases; to tackle this, we introduce an alternating optimization (AO) approach that efficiently finds near-optimal solutions irrespective of the number or spatial configuration of RHS. Numerical results demonstrate that shape adaptation enables more efficient resource distribution, enhancing the effectiveness of multi-RHS deployments as the network scales.