Fluid antenna arrays (FAAs), support multiuser downlink transmission by activating a subset of reconfigurable ports. The activation mask jointly determines the effective channel and the sparse radiating aperture, which requires a balance among sum rate, sidelobe suppression, hardware constraints, and online complexity. Channel driven selection can cluster active ports and increase sidelobes, whereas sidelobe oriented synthesis is typically channel independent and can sacrifice sum rate. This paper proposes learned blockwise port activation (L-BPA), for real time sidelobe aware FAA downlink beamforming. L-BPA activates a fixed number of ports in each aperture block, which supports grouped switching hardware and limits port clustering. A lightweight convolutional network scores ports using multiuser channel features, port coordinates, and user power statistics. Training combines blockwise straight through masks with a differentiable peak sidelobe level (PSLL), surrogate. During inference, learned scores are combined with multiscale geometric repulsion, followed by regularized zero forcing precoding over the reduced effective channel. L-BPA reduces the average PSLL by 3.26 dB relative to uniform sparse activation while achieving a slightly higher sum rate. It also reduces the PSLL by 8.13 dB and 10.10 dB relative to greedy and gain based selection, respectively, without iterative online search.
Yuanhui Wu, Zhen-Tian Zhang, Hanjiang Hong et al.· arXiv.org· 3 citations
The deployment of integrated sensing and communication (ISAC) systems poses new challenges to physical-layer security (PLS), as sensing waveforms directed toward targets may be exploited by eavesdroppers (Eves), causing information leakage. Meanwhile, security enhancement must jointly consider multi-user quality-of-service (QoS) and radar sensing performance, resulting in coupled and conflicting objectives. The intrinsic limitation arises from the rigid spatial constraints of conventional fixed-position antennas (FPAs) and the restricted stream-level interference management of non-rate-splitting frameworks, which collectively provide insufficient degrees of freedom (DoFs) to reconcile these competing requirements, especially under imperfect channel state information (CSI). To address these gaps, we propose a robust secure transmission framework that synergistically integrates the reconfigurable spatial DoFs enabled by fluid antennas (FAs) and the flexible stream-level DoFs provided by rate-splitting multiple access (RSMA). In this architecture, the RSMA common stream is repurposed to serve a dual role: it is decoded by legitimate users (LUs) for information delivery while also acting as a controlled jamming component to impair an Eve. We formulate a secure sum-rate maximization problem by jointly optimizing the base station beamformers and the FA positions at the LUs, subject to power budget, RSMA decoding, and minimum sensing requirements. An alternating-optimization (AO) framework is developed to address the coupled design under both perfect and imperfect CSI scenarios. In the imperfect-CSI case, semi-infinite constraints induced by bounded estimation errors are converted into linear matrix inequalities (LMIs) via the S-procedure. Simulation results show that the proposed FA-RSMA design achieves significant secure sum-rate gains and improved robustness over FPA and non-rate-splitting baselines.
Cixiao Zhang, Yin Xu, Hanjiang Hong et al.· IEEE Transactions on Communi...· 0 citations
Current digital semantic communication systems have primarily focused on maintaining compatibility with conventional constellation-based modulation. In contrast, index modulation (IM) represents a more spectrally and energy-efficient alternative by exploiting additional dimensions for information conveyance. Recognizing this potential, this paper bridges the gap between IM and semantic communications by proposing a novel spatial semantic communication (SSC) system leveraging cutting-edge fluid antenna-IM (FA-IM) technology. Compatible with existing joint source-channel coding (JSCC) architectures, the proposed SSC system employs the residual quantization (RQ) approach to discretize analog semantic features for subsequent digital IM transmission. Notably, the proposed SSC system synergizes RQ and IM via a semantic-aware stream splitting scheme, which ensures that critical semantic information undergoes less severe channel fading, thereby further optimizing semantic transmission performance. Simulation results validate that the proposed SSC system effectively integrates the high fidelity of RQ, the reliability of semantic-aware splitting, and the spatial efficiency of FA-IM, thereby providing a robust solution for future digital semantic transmission.
Xinghao Guo, Yin Xu, Dazhi He et al.· IEEE Transactions on Communi...· 1 citation
The rapid proliferation of Uncrewed Aerial Vehicles (UAVs) necessitates reliable monitoring systems for airspace security. While Channel State Information (CSI) offers a precise and flexible modality for UAV sensing, leveraging CSI from multiple distributed devices can significantly enhance recognition accuracy through spatial diversity. However, existing deep learning-based methods often struggle to effectively integrate multi-source data, either neglecting explicit device interactions or suffering from prohibitive computational costs when scaling to larger networks. To address these challenges, we propose the Complex Convolutional FiLM-enhanced Multi-device Bidirectional Mamba Network (CFM-BiMamba). Specifically, we introduce a shared complex-valued feature extractor to reduce fusion redundancy, incorporating device-specific characteristics via Feature-wise Linear Modulation (FiLM). Furthermore, a bidirectional Mamba backbone is employed to capture long-range temporal dependencies efficiently, coupled with a lightweight attention module to robustly fuse multi-view features. Experimental results on both simulated and real-world measured datasets demonstrate that CFM-BiMamba consistently outperforms state-of-the-art baselines, striking a superior balance between recognition accuracy and computational efficiency in multi-device sensing scenarios.
Yanming Liu, Pengxuan Gao, Kai Ying et al.· IEEE Transactions on Network...· 0 citations
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