Search arXiv⌕ Search

arXiv · 2610.04482

Secure Directional Modulation Enabled by Elevatable and Rotatable Antenna Array for Maritime Communications

Abstract

To address the dual challenges of limited transmission distance and wireless communication security in maritime communications, a directional modulation network enhanced by an elevatable and rotatable antenna array is investigated in this paper. Specifically, the base station, whose array and antenna elements can be rotated and whose antenna height can be adjusted, transmits information to legitimate users while resisting eavesdropping. Moreover, transceiver hardware impairments are taken into account. Accordingly, a worst-case secrecy rate maximization problem is formulated under the joint optimization of precoding vectors, array orientation, antenna orientations, and array height. To solve this non-convex optimization challenge, an alternating optimization scheme and a Transformer-based learning scheme are proposed for the single-user and multi-user scenarios, respectively. Simulation results validate the effectiveness of the proposed algorithms. Security performance gains can be obtained after optimizing the array height, array orientation, and antenna orientations. In addition, compared with the isotropic antenna benchmark, the Transformer-based learning scheme can achieve approximately 90\% power savings, 87\% antenna savings, and serve more legitimate users.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maolin Li, Haini Wu, Feng Shu, Yongpeng Wu, Cunhua Pan, Jianghzou Wang. 2026-10-11. Secure Directional Modulation Enabled by Elevatable and Rotatable Antenna Array for Maritime Communications. https://arxiv.org/abs/2610.04482

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

KEEP EXPLORING

Related papers

DDA-Net: Accurate TDD Channel Estimation via Deep Unfolding the Doppler-Delay-Angle Representation of Channel Signals

In TDD massive MIMO systems, channel estimation under sparse frequency-hopping pilots is challenging: each snapshot captures only one narrow pilot block that hops across frequency, with tens of milliseconds between adjacent snapshots. Finite-window leakage and off-grid effects weaken the ideal Doppler-delay-angle (DDA) sparsity, limiting both classical sparse recovery and purely data-driven approaches lacking an explicit structured transform-domain model. We propose DDA-Net, a model-driven 3D deep unfolding network for joint multi-snapshot channel state reconstruction. DDA-Net integrates an ADMM-based formulation with a closed-form data-consistency update that avoids tensor inversion, a lightweight Doppler-domain learned prior, and delay oversampling to mitigate basis mismatch. It consistently outperforms strong baselines across three channel settings from 3GPP TR 38.901: UMa-NLOS, UMi-NLOS, and CDL-B. Ablations confirm that window-level 3D processing and explicit Doppler modeling yield target-dependent improvements. With minimal target-domain fine-tuning, the UMa-pretrained model surpasses both its zero-shot version and counterparts trained from scratch with the same number of target-domain samples. The Doppler-domain design proves consistently superior to its time-domain equivalent, with a wider margin after fine-tuning. These results demonstrate that combining exact physical data consistency with a learned DDA-domain prior is an effective and sample-efficient approach to channel state acquisition under sparse frequency-hopping pilots.

eess.SP↗

Optimal symmetric low-rank BD-RIS configuration maximizing the determinant of a MIMO link

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) significantly improve wireless performance by allowing tunable interconnections among elements, but their design in multiple-input multiple-output (MIMO) systems has so far relied on complex iterative algorithms or suboptimal approximations. This work introduces a simple yet powerful approach: instead of directly maximizing the achievable rate, we maximize the absolute value of the determinant of the equivalent MIMO channel. We derive a closed-form symmetric unitary scattering matrix whose rank is exactly twice the channel's degrees of freedom ($2r$). Remarkably, this low-rank solution achieves the same determinant value as the optimal unitary BD-RIS. Using log-majorization theory, we prove that the rate loss relative to the optimal unitary BD-RIS vanishes at high signal-to-noise ratio (SNR) or when the number of BD-RIS elements becomes large. Moreover, the proposed solution can be perfectly implemented using a $q$-stem BD-RIS architecture with only $q=2r-1$ stems, requiring a minimum number of reconfigurable circuits. The resulting Max-Det solution is orders of magnitude faster to compute than existing iterative methods while achieving near-optimal rates in practical scenarios. This makes high-performance BD-RIS deployment feasible even with large surfaces and limited computational resources.

eess.SP↗

Constrained Tensor Decomposition-Based Target Sensing for Sparse Non-Uniform Array-Enabled AFDM ISAC Systems

Sparse non-uniform array-enabled affine frequency division multiplexing (AFDM) is a promising candidate for integrated sensing and communication (ISAC), while its performance critically depends on accurate target parameter estimation. In this paper, we propose a constrained tensor decomposition-based sensing framework for delay, Doppler, and angle estimation. Specifically, a manifold-constrained alternating least squares algorithm is developed by exploiting the sparse array geometry structure, enabling robust factor matrix extraction and direct angle estimation. In addition, we analyze the uniqueness condition of the tensor decomposition. From the decomposed factor matrices, delays and Doppler shifts are coarsely estimated by pulse compression, and refined by an iterative one dimensional golden-section search.Moreover, the Cramér-Rao bound (CRB) is derived to characterize the fundamental performance limit of the considered estimation problem. Finally, simulation results validate the effectiveness of the proposed estimation algorithm.

eess.SP↗