Enhanced Direction-Sensing Methods and Performance Analysis in Low-Altitude Wireless Network via a Rotation Antenna Array

Due to the directive property of each antenna element, the received signal power can be severely attenuated when the emitter deviates from the array boresight, which will lead to a severe degradation in sensing performance along the corresponding direction. Although existing rotatable array sensing methods such as recursive rotation (RR-Root-MUSIC) can mitigate this issue by iteratively rotating and sensing, several mechanical rotations and repeated eigendecomposition operations are required to yield a high computational complexity and low time-efficiency. To address this problem, a pre-rotation initialization with recieve power as a rule is proposed to signifcantly reduce the computational complexity and improve the time-efficiency. Using this idea, a low-complexity enhanced direction-sensing framework with pre-rotation initialization and iterative greedy spatial-spectrum search (PRI-IGSS) is develped with three stages: (1) the normal vector of array is rotated to a set of candidates to find the opimal direction with the maximum sensing energy with the corresponding DOA value computed by the Root-MUSIC algorithm; (2) the array is mechanically rotated to the initial estimated direction and kept fixed; (3) an iterative greedy spatial-spectrum search or recieving beamforming method, moviated by reinforcement learning, is designed with a reduced search range and making a summation of all previous sampling variance matrices and the current one is adopted to provide an increasiong performance gain as the iteration process continues. To assess the performance of the proposed method, the corresponding CRLB is derived with a simplified rotation model. Simulation results demonstrate that the proposed PRI-IGSS method performs much better than RR-Root-MUSIC and achieves the CRLB in term of mean squared error due to the fact there is no sample accumulation for the latter.

Reinforcement Learning-Based Secure Near-field Directional Modulation Enhanced by Rotatable RIS

This paper investigates secure Directional Modulation (DM) design enhanced by a rotatable active Reconfigurable Intelligent Surface (RIS). In conventional RIS-assisted DM networks, the security performance gain is limited due to the multiplicative path loss introduced by the RIS reflection path. To address this challenge, a Secrecy Rate (SR) maximization problem is formulated, subject to constraints including the eavesdropper's Direction Of Arrival (DOA) estimation performance, transmit power, rotatable range, and maximum reflection amplitude of the RIS elements. To solve this non-convex optimization problem, three algorithms are proposed: a multi-stream null-space projection and leakage-based method, an enhanced leakage-based method, and an optimization scheme based on the Distributed Soft Actor-Critic with Three refinements (DSAC-T). Simulation results validate the effectiveness of the proposed algorithms. A performance trade-off is observed between eavesdropper's DOA estimation accuracy and the achievable SR. The security enhancement provided by the RIS is more significant in systems equipped with a small number of antennas. By optimizing the orientation of the RIS, a 52.6\% improvement in SR performance can be achieved.

Subspace Fusion Sensing for Cooperative ISAC

This paper proposes a subspace fusion sensing algorithm for cooperative integrated sensing and communication. First, we stack the received signals from access points (APs) into a third-order tensor and construct the equivalent virtual antenna (EVA) array via tensor unfolding. Then, a data association-free subspace-based fusion sensing algorithm is developed utilizing the EVA arrays from distributed APs. A derivation of Cramer-Rao lower bound (CRLB) is also presented. Finally, simulation results validate the effectiveness of the proposed algorithm compared to traditional techniques.

Enhancing AAV-Enabled Secure Communications via Synthetic Aperture Beamforming

In this paper, we consider a synthetic aperture secure beamforming approach for a virtual multiple-input multiple output (MIMO) broadcast channel in the presence of hybrid wiretapping environments. Our goal is to design the flight node deployment constructed by a single-antenna mobile autonomous aerial vehicle (AAV), corresponding transmission symbol strategy, transmit precoding, and received beamforming to maximize the system channel capacity. Leveraging the synthetic aperture beamforming, we aim to provide spatial gain along a predefined angle in free space while reducing it in others and thus enhance physical layer (PHY) security. To this end, we analyze the expression of the asymptotic channel eigenvalues to optimize the AAV flight node deployment. For the optimal precoding design, an energy-efficient method that minimizes the transmit power consumption is studied based on the given virtual MIMO channel, while meeting the quality of service (QoS) for the base station (BS), leakage tolerance of eavesdroppers (Eves), and per-node power constraints. The power minimization problem is a non convex program, which is then reformulated as a tractable form after some mathematical manipulations. Moreover, we design the received beamforming by applying the linearly constrained minimum variance (LCMV) method such that the jamming can be effectively suppressed. Numerical results demonstrate the superiority of the proposed method in promoting capacity.

TokenCom: Vision-Language Model for Multimodal and Multitask Token Communications

Visual-Language Models (VLMs), with their strong capabilities in image and text understanding, offer a solid foundation for intelligent communications. However, their effectiveness is constrained by limited token granularity, overlong visual token sequences, and inadequate cross-modal alignment. To overcome these challenges, we propose TaiChi, a novel VLM framework designed for token communications. TaiChi adopts a dual-visual tokenizer architecture that processes both high- and low-resolution images to collaboratively capture pixel-level details and global conceptual features. A Bilateral Attention Network (BAN) is introduced to intelligently fuse multi-scale visual tokens, thereby enhancing visual understanding and producing compact visual tokens. In addition, a Kolmogorov Arnold Network (KAN)-based modality projector with learnable activation functions is employed to achieve precise nonlinear alignment from visual features to the text semantic space, thus minimizing information loss. Finally, TaiChi is integrated into a multimodal and multitask token communication system equipped with a joint VLM-channel coding scheme. Experimental results validate the superior performance of TaiChi, as well as the feasibility and effectiveness of the TaiChi-driven token communication system.

Score-Based Conditional Flow Models for MIMO Receiver Design with Superimposed Pilots

Accurate channel state information (CSI) is vital for multiple-input multiple-output (MIMO) systems. However, superimposed pilots (SIP), which reduce overhead, introduce severe pilot contamination and data interference, complicating joint channel estimation and data detection. This paper proposes a conditional flow matching receiver (CFM-Rx), an unsupervised generative framework that learns directly from received signals, eliminating the need for labeled data and improving adaptability across diverse system settings. By leveraging flow-based generative modeling, CFM-Rx enables deterministic, low-latency inference and exploits model invertibility to capture the bidirectional nature of signal propagation. This framework unifies flow matching with score-based diffusion modeling via a moment-consistent ordinary differential equation (ODE), replacing stochastic differential equation (SDE) sampling with a deterministic and efficient process. Furthermore, it integrates receiver-side priors to ensure stable, data-consistent inference. Extensive simulation results across various MIMO configurations demonstrate that CFM-Rx consistently outperforms conventional estimators and state-of-the-art data-driven receivers, achieving notable gains in channel estimation accuracy and symbol detection robustness, particularly under severe pilot contamination.

Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-free Massive MIMO Systems with Reciprocity Calibration

Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.

Theoretical and Empirical Study of Spatial Power Focusing Effect for Sparse Arrays at Terahertz Band

This work investigates the spatial power focusing effect for large-scale sparse arrays at terahertz (THz) band, combining theoretical analysis with experimental validation. Specifically, based on a Green's function channel model, we analyze the power distribution along the $z$-axis, deriving a closed-form expression to characterize the focusing effect. Furthermore, the factors influencing the focusing effect, including phase noise and positional deviations, are theoretically analyzed and numerically simulated. Finally, a 300 GHz measurement platform based on a vector network analyzer (VNA) is constructed for experimental validation. The measurement results demonstrate close consistence with theoretical simulation results, confirming the spatial power focusing effect for sparse arrays.

A Two-Stage ISAC Framework for Low-Altitude Economy Based on 5G NR Signals

The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging standard 5G NR signals, such as the Synchronization Signal Block (SSB), presents fundamental limitations in sensing resolution. To address the issue, this paper proposes a two-stage coarse-to-fine sensing framework that utilizes standard 5G NR initial access signals augmented by a custom-designed sparse pilot structure (SPS) for high-precision unmanned aerial vehicles (UAV) sensing. In Stage I, we first fuse information from the SSB, Type\#0-PDCCH, and system information block 1 (SIB1) to ensure the initial target detection. In Stage II, a refined estimation algorithm is introduced to overcome the resolution limitations of these signals. Inspired by the sparse array theory, this stage employs a novel SPS, which is inserted into resource blocks (RBs) within the CORSET\#0 bandwidth. To accurately extract the off-grid range and velocity parameters from these sparse pilots, we develop a corresponding high-resolution algorithm based on the weighted unwrapped phase (WUP) technique and the RELAX-based iterative method. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to prune the redundant detections arising from beam overlap. Comprehensive simulation results demonstrate the superior estimation accuracy and computational efficiency of the proposed framework in comparison to other techniques.

4D Imaging in ISAC Systems: A Framework Based on 5G NR Downlink Signals

Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) wireless networks, supporting spectrum sharing and hardware integration. Beyond communication enhancement, ISAC also enables high-accuracy environment reconstruction and imaging, which are crucial for applications such as autonomous driving and digital twins. This paper proposes a 4D imaging framework fully compliant with the 5G New Radio (NR) protocol, ensuring compatibility with cellular systems. Specifically, we develop an end-to-end processing chain that covers waveform generation, echo processing, and multi-BS point cloud fusion. Furthermore, we introduce Zoom-OMP, a coarse-to-fine sparse recovery algorithm for high-resolution angle estimation that achieves high accuracy with reduced computational cost. The simulation results demonstrate that the proposed framework achieves robust 4D imaging performance with superior spatial accuracy and reconstruction quality compared to conventional benchmarks, paving the way for practical ISAC-enabled environment reconstruction in 6G networks.