Point Spread Function Optimization for Communication-assisted UAV-borne MIMO TomoSAR

This paper tackles the optimization of the point spread function (PSF) of unmanned aerial vehicle (UAV)-borne multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) tomography systems. A swarm of UAV-borne SAR systems is deployed to image an area to obtain its height profile. To achieve a high-quality three-dimensional (3D) image of the scene, the PSF has to exhibit low sidelobes. The heavy computations, required for image generation, are performed on the ground. To this end, the sensor data collected by the UAV-SARs is offloaded in real time via a frequency division multiple access (FDMA) air-to-ground backhaul link. In this work, the UAV formation and the power allocated for offloading are jointly optimized for the minimization of the PSF sidelobe levels. To this end, we propose a novel solution based on the particle swarm optimization (PSO) algorithm, which meets practical sensing and communication constraints. Our simulation results demonstrate that the proposed solution can significantly improve sidelobe suppression compared to several benchmark schemes.

Movable-Antenna-Enhanced ISAC: Optimal Antenna Trajectory and Beamforming Design

Integrated sensing and communication (ISAC) is a key enabling technology for next-generation wireless networks. However, most existing ISAC systems rely on fixed-position antennas, which restrict performance when balancing sensing and communication objectives. Movable antenna (MA) technology introduces additional spatial degrees of freedom through antenna mobility, yet existing studies on MA-enabled ISAC schemes mainly consider static antenna repositioning and fail to fully exploit this capability. By leveraging spatio-temporal sampling enabled by antenna motion, optimized MA trajectories can synthesize large virtual aperture arrays, thereby improving angular resolution and reducing sensing ambiguity. To this end, this paper investigates a dynamic MA-enabled ISAC system and studies the joint design of MA trajectories and transmit beamforming. We formulate a joint trajectory and beamforming optimization problem to minimize sensing beampattern mismatch under communication quality-of-service constraints. A branch-and-bound-based algorithm is developed to obtain the globally optimal solution. Numerical results show that the proposed framework significantly outperforms baseline schemes with only one or two antenna repositioning steps, demonstrating its practical feasibility.

Closed-form Model for Radiation Pattern of Pinching Antennas

In this article, we develop an analytical radiation-pattern model for pinching-antenna systems (PASS) based on a two-dimensional dielectric slab waveguide. The model is derived in two steps. First, we employ coupled-mode theory (CMT) to derive a closed-form expression for the field coupled into the pinching antennas (PAs). Second, we use this analytical field profile as a scattering source model and derive the far-field radiation pattern via a two-dimensional radiation integral. We validate the proposed model against full-wave finite-element simulations performed in COMSOL Multiphysics, showing that it accurately reproduces the directional radiation characteristics of PASS. In contrast, most existing works model PAs as omni-directional point radiators, which simplifies system-level analysis but does not accurately capture the underlying electromagnetic radiation mechanism. Because the proposed model is given in closed form, it can be easily integrated into existing system-level PASS models to replace the assumed omni-directional pattern with a physically motivated directional radiation pattern. Finally, numerical simulations quantify the performance degradation that arises when the directional behavior of PAs is neglected in a representative wireless communications scenario.

Multipath Channel Metrics and Detection in Vascular Molecular Communication: A Wireless-Inspired Perspective

Motivated by classical communications engineering, early works in molecular communication (MC) largely adopted established modeling and signal processing concepts from wireless electromagnetic communication systems. In the context of the human cardiovascular system (CVS), MC channel models evolved from simple unbounded and single-duct environments mimicking individual blood vessels to complex vessel network (VN) topologies, generally at the expense of analytical tractability. Up until now, this has largely prohibited rigorous communication-theoretic analysis of large-scale VNs. In this work, we leverage a recently established closed-form analytical channel model for VNs, named mixture of inverse Gaussians for hemodynamic transport (MIGHT), to conduct the first systematic communication-theoretic study of MC in complex, large-scale VNs. Based on MIGHT, we derive a Poisson channel noise model and unveil structural analogies between multipath wireless communications (MWC) and advective-diffusive MC in VNs. In particular, we establish classical MWC metrics, namely the root mean squared (RMS) delay spread, the mean excess delay, and the coherence bandwidth, for MC in VNs and derive closed-form expressions for the channel frequency response and power delay profile (PDP). Building on this characterization, we propose a VN-adapted, coherent decision-feedback (DF) detector and show how the derived multipath metrics can inform the choice of critical system parameters like the symbol duration, the sampling time, and the memory length. Additionally, we evaluate the detector's performance in different VNs exhibiting inter-symbol interference (ISI). Together, these contributions open the door to a systematic, MWC-inspired MC system design for large-scale VNs.

Clustered Movable Pinching Antennas: Realizing Beamforming Gains and Target Diversity in ISAC Systems with Look-Angle-Dependent RCS

We investigate a novel integrated sensing and communication (ISAC) system enabled by pinching antennas (PAs), which are dynamically activated along a dielectric waveguide. Unlike prior designs, the PAs are organized into multiple clusters of movable antennas. The movement of the antennas within each cluster enables transmit beamforming, while the spatial separation of different clusters allows the system to illuminate the target from multiple angular perspectives.

Rotatable Antenna-Empowered Wireless Networks: A Tutorial

Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. Among them, rotatable antenna (RA) has emerged as a promising technology for enhancing wireless communication and sensing performance through flexible antenna orientation/boresight rotation. By enabling mechanical or electronic boresight adjustment without altering physical antenna positions, RA introduces additional spatial degrees of freedom (DoFs) beyond conventional beamforming. In this paper, we provide a comprehensive tutorial on the fundamentals, architectures, and applications of RA-empowered wireless networks. Specifically, we begin by reviewing the historical evolution of RA-related technologies and clarifying the distinctive role of RA among flexible antenna architectures. Then, we establish a unified mathematical framework for RA-enabled systems, including general antenna/array rotation models, as well as channel models that cover near- and far-field propagation characteristics, wideband frequency selectivity, and polarization effects. Building upon this foundation, we investigate antenna/array rotation optimization in representative communication and sensing scenarios. Furthermore, we examine RA channel estimation/acquisition strategies encompassing orientation scheduling mechanisms and signal processing methods that exploit multi-view channel observations. Beyond theoretical modeling and algorithmic design, we discuss practical RA configurations and deployment strategies. We also present recent RA prototypes and experimental results that validate the practical performance gains enabled by antenna rotation. Finally, we highlight promising extensions of RA to emerging wireless paradigms and outline open challenges to inspire future research.

Robust and Secure Near-Field Communication via Curved Caustic Beams

Near-field beamfocusing with extremely large aperture arrays can effectively enhance physical layer security. Nevertheless, even small estimation errors of the eavesdropper's location may cause a pronounced focal shift, resulting in a severe degradation of the secrecy rate. In this letter, we propose a physics-informed robust beamforming strategy that leverages the electromagnetic (EM) caustic effect for near-field physical layer security provisioning, which can be implemented via phase shifts only. Specifically, we partition the transmit array into caustic and focusing subarrays to simultaneously bypass the potential eavesdropping region and illuminate the legitimate user, thereby significantly improving the robustness against the localization error of eavesdroppers. Moreover, by leveraging the connection between the phase gradient and the EM wave departing angle, we derive the corresponding piece-wise closed-form array phase profile for the subarrays. Simulation results demonstrate that the proposed scheme achieves up to an 80% reduction of the worst-case eavesdropping rate for a localization error of 0.25 m, highlighting its superiority for providing robust and secure communication.

Autoencoder-based Optimization of Multi-user Molecule Mixture Communication Systems

In this paper, we introduce an autoencoder (AE)-based scheme for end-to-end optimization of a multi-user molecule mixture communication system. In the proposed scheme, each transmitter leverages an encoder network that maps the user symbol to a molecule mixture. The mixtures then propagate through the channel to the receiver, which samples the channel using a non-linear, cross-reactive sensor array. A decoder network then estimates the symbol transmitted by each user based on the sensor observations. The proposed scheme achieves, for a given signal-to-noise ratio, lower symbol error rates than a baseline scheme from the literature in a single-user setting with full channel state information. We additionally demonstrate that the proposed AE-based scheme allows reliable communication when the channel is unknown or changing. Finally, we show that for multiple access the system can account for different user priorities. In summary, the proposed AE-based scheme enables end-to-end system optimization in complex scenarios unsuitable for analytical treatment and thereby brings molecular communication systems closer to real-world deployment.

Codebook-Based Self-Sustainable RIS: Optimal Splitting Schemes and Power Allocation

This paper studies the codebook-based configuration of a reconfigurable intelligent surface (RIS) that extends the coverage of a base station (BS) while utilizing energy harvesting to facilitate self-sustainable operation. For a given coverage area, we design a RIS codebook and propose a mathematical framework for analyzing the efficiency of three common energy harvesting schemes: power splitting (PS), element splitting (ES), and time splitting (TS). Thereby, we use a tile-based architecture at the RIS to exploit the advantages of both radio-frequency (RF) combining and direct-current (DC) combining. Moreover, we account for deterministic and random transmit signals for beam training and data transmission, respectively, and show their impact on the RF-DC conversion efficiencies at the rectifiers. Our main objective is to minimize the average transmit power at the BS by jointly optimizing the splitting ratio for the incident signal at the RIS and the power allocated to each RIS codeword. While the optimal power allocation is derived analytically, we show that the optimal splitting ratio can be determined by performing a grid search over a single optimization variable. Our performance evaluation reveals that the efficiency of the optimized splitting schemes depends on the adopted power consumption model and the number of tiles at the RIS. In particular, our results show that depending on the system parameters a different splitting scheme will achieve the lowest transmit power at the BS.

Goal-Oriented Framework for Optical Flow-based Multi-User Multi-Task Video Transmission

Efficient multi-user multi-task video transmission is an important research topic within the realm of current wireless communication systems. To reduce the transmission burden and save communication resources, we propose a goal-oriented semantic communication framework for optical flow-based multi-user multi-task video transmission (OF-GSC). At the transmitter, we design a semantic encoder that consists of a motion extractor and a patch-level optical flow-based semantic representation extractor to effectively identify and select important semantic representations. At the receiver, we design a transformer-based semantic decoder for high-quality video reconstruction and video classification tasks. To minimize the communication time, we develop a deep deterministic policy gradient (DDPG)-based bandwidth allocation algorithm for multi-user transmission. For video reconstruction tasks, our OF-GSC framework achieves a significant improvement in the received video quality, as evidenced by a 13.47% increase in the structural similarity index measure (SSIM) score in comparison to DeepJSCC. For video classification tasks, OF-GSC achieves a Top-1 accuracy slightly surpassing the performance of VideoMAE with only 25% required data under the same mask ratio of 0.3. For bandwidth allocation optimization, our DDPG-based algorithm reduces the maximum transmission time by 25.97% compared with the baseline equal-bandwidth allocation scheme.