Antenna Positioning and Beamforming Optimization in MA Enabled Secure ISAC Systems: A Gradient-Based Meta Learning Approach

Integrated sensing and communications (ISAC) significantly improves spectral efficiency but introduces security risks regarding the interception of embedded communication signals. This paper proposes an movable antenna (MA)-enabled secure ISAC system that utilizes the spatial degrees of freedom of MA to mitigate these risks. Then, a problem is formulated to maximize the system secrecy rate by jointly optimizing antenna positioning, transmit beamforming, and artificial noise. However, the principal challenge arises from the non-convexity of the optimization problem and the strong coupling of the optimization variables. Generally, traditional optimization methods for this problem suffer from complex mathematical derivations, while existing deep learning approaches rely heavily on the training data distribution. To address these issues, we introduce a gradient-based meta learning (GML) algorithm, which works without pre-training and demonstrates favorable performance. Specifically, the algorithm establishes a neural network for each optimization variable, where the gradient of the objective function with respect to the variable serves as the input, and the output of the network determines the variable's update step. By handling the constraints and constructing penalty terms, the global loss function is used to guide the optimization process. Extensive numerical simulations confirm that the proposed algorithm achieves satisfactory performance in terms of both communication security and sensing capabilities.

GML-Based Optimization for Movable Antenna Wireless Networks: Challenges and Opportunities

Movable antenna (MA) is proposed as an emerging technology for future wireless networks. By leveraging the additional spatial degrees of freedom, MA can proactively reshape the wireless propagation environment, thereby enhancing network performance.However, fully unlocking the potential of MA networks necessitates the joint optimization of MA antenna positioning and beamforming. For this non-convex and highly coupled problem, existing solutions exhibit significant limitations. Therefore, this paper proposes a gradient-based meta learning (GML) optimization framework. Specifically, we first elaborate on the hardware architecture and channel characteristics of MA, based on which we analyze the primary challenges in optimizing MA wireless networks. Subsequently, we introduce the fundamental logic of the GML framework and compare it with existing methods. Furthermore, we discuss the constraint handling strategies for applying the proposed optimization framework to MA networks. A specific case is studied to show the performance of proposed framework based on numerical simulation. Finally, this paper outlines future research directions for both the GML framework and MA wireless networks.

DMSNet: Cross-Band Learning for Multi-Target Sensing in Multi-Band ISAC

Multi-band integrated sensing and communication (ISAC) offers complementary high- and low-frequency echo information for multi-target sensing. However, existing dual-band ISAC sensing methods have a limited ability to exploit deep complementary information across heterogeneous bands and often incur high computational costs. To address these limitations, we propose a Dual-Band Multi-Target Sensing Neural Network (DMSNet) for joint target number and parameter estimation. Under representative simulation conditions, DMSNet outperforms the best baseline in target number estimation, increasing count accuracy from 89.01 % to 91.74 % and Macro-F1 from 90.80 % to 93.07 %. For parameter estimation, compared with the best baselines, DMSNet reduces the median absolute errors of range, velocity, and angle by 82.2%, 56.9%, and 73.2%, respectively. Moreover, DMSNet reduces runtime by 68.7 % relative to the fastest existing dual-band ISAC sensing method.

Beyond Single-Band: Analysis and Resource Allocation for Multi-band ISAC Systems

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for sixth-generation wireless networks to empower high-precision sensing. The demand for superior sensing resolution and the reality of spectrum fragmentation have driven the research of multi-band ISAC. Multi-band ISAC provides frequency diversity through independent observations across disparate bands, mitigating sensing performance fluctuations caused by frequency-selective radar cross-section compared to single-band counterparts. In this paper, we propose a framework for analytical performance characterization and resource optimization in multi-band ISAC systems. Specifically, analytical closed-form detection and false alarm probabilities for multi-band OFDM signals are derived, providing a theoretical foundation for subsequent resource allocation. Then, a joint power and time-frequency resource allocation scheme is developed and solved via a proposed ADMM-based algorithm to maximize detection performance. Numerical results validate the accuracy of the closed-form derivations and demonstrate the superior robustness of multi-band signals. Notably, the proposed optimization scheme achieves an 18 dB detection gain over traditional single-band baselines at a 90\% detection probability.

Event-Level Sensing for Intelligent 6G ISAC

The intelligent evolution of mission-critical networks, such as the Internet of vehicles (IoV) and the low-altitude economy (LAE), requires sixth-generation (6G) networks to move beyond discrete physical parameter estimation toward deeper environmental understanding. However, existing integrated sensing and communications (ISAC) studies mainly focus on target-level sensing, which provides fragmented snapshots of the physical world and lacks the behavioral semantic capability to interpret intent. This limitation hinders the intelligent evolution of such networks and prevents 6G from acquiring the essential sensing foundation to evolve into an "intelligent service engine". To bridge this gap, ISAC must advance toward event-level sensing, which models continuous-time states to enable persistent recognition and prediction of target intent and behavioral semantics. This article presents a comprehensive overview of event-level sensing in 6G ISAC networks. We first introduce its fundamental concepts, sensing types, and representative scenarios. We then review key enabling techniques across waveform design, target state estimation and tracking, and event recognition. Furthermore, focusing on IoV and LAE scenarios, we discuss representative applications of ISAC event-level sensing and the intelligent enhancement of downstream operational functions enabled by event-level information. Finally, we highlight future research trends and potential directions to further advance ISAC event-level sensing toward intelligent and proactive 6G networks.

Wideband Near-Field Sensing in ISAC: Unified Algorithm Design and Decoupled Effect Analysis

To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe computational burdens and high spatial-frequency coherence on conventional grid-based algorithms. Furthermore, a unified framework exploiting both wideband (WB) and near-field (NF) effects is lacking, and the analytical conditions for simplifying this model into decoupled approximations remain uncharacterized. To address these challenges, the proposed algorithm mathematically decouples the mutual coherence function and introduces a novel angle-distance sampling grid with customized distance adjustments, drastically reducing dictionary dimensions while ensuring low coherence. To isolate the individual WB and NF impacts, two coherence-based metrics are formulated to establish the effective boundaries of the narrowband near-field (NB-NF) and wideband far-field (WB-FF) regions, where respective multiple signal classification (MUSIC) algorithms are utilized. Simulations demonstrate that the CS-based method achieves robust performance across the entire regime, and the established boundaries provide crucial theoretical guidelines for WB and NF effect decoupling.

Integrated Sensing, Communication and Control enabled Agile UAV Swarm

Uncrewed aerial vehicle (UAV) swarms are pivotal in the applications such as disaster relief, aerial base station (BS) and logistics transportation. These scenarios require the capabilities in accurate sensing, efficient communication and flexible control for real-time and reliable task execution. However, sensing, communication and control are studied independently in traditional research, which limits the overall performance of UAV swarms. To overcome this disadvantage, we propose a deeply coupled scheme of integrated sensing, communication and control (ISCC) for UAV swarms, which is a systemic paradigm that transcends traditional isolated designs of sensing, communication and control by establishing a tightly-coupled closed-loop through the co-optimization of sensing, communication and control. In this article, we firstly analyze the requirements of scenarios and key performance metrics. Subsequently, the enabling technologies are proposed, including communication-and-control-enhanced sensing, sensing-and-control-enhanced communication, and sensing-and-communication-enhanced control. Simulation results validate the performance of the proposed ISCC framework, demonstrating its application potential in the future.

Beyond Target-Level: ISAC-Enabled Event-Level Sensing for Behavioral Intention Prediction

Integrated Sensing and Communication (ISAC) holds great promise for enabling event-level sensing, such as behavioral intention prediction (BIP) in autonomous driving, particularly under non-line-of-sight (NLoS) or adverse weather conditions where conventional sensors degrade. However, as a key instance of event-level sensing, ISAC-based BIP remains unexplored. To address this gap, we propose an ISAC-enabled BIP framework and validate its feasibility and effectiveness through extensive simulations. Our framework achieves robust performance in safety-critical scenarios, improving the F1-score by 11.4% over sensor-based baselines in adverse weather, thereby demonstrating ISAC's potential for intelligent event-level sensing.

Coherent Compensation-Based Sensing for Long-Range Targets in Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) is a promising candidate technology for 6G due to its improvement in spectral efficiency and energy efficiency. Orthogonal frequency division multiplexing (OFDM) signal is a mainstream candidate ISAC waveform. However, there are inter-symbol interference (ISI) and inter-carrier interference (ICI) when the round-trip delay exceeds the cyclic prefix (CP) duration for OFDM signals, which limits the maximum sensing range of ISAC system. When detecting a long-range target, the wide beam inevitably covers the close-range target, of which the echo's power is much larger than that of the long-range target. In order to tackle the above problem, a multiple signal classification (MUSIC) and least squares (LS)-based spatial signal separation method is proposed to separate the echo signals reflected from different targets. Moreover, a coherent compensation-based sensing signal processing method at the receiver is proposed to enhance the signal to interference plus noise power ratio (SINR) of the OFDM block for generating the range-Doppler map (RDM) with higher SINR. Simulation results reveal that the proposed method greatly enhances the SINR of RDM by 10 dB for a target at 500 m compared with two-dimensional fast Fourier transform (2D-FFT) method. Besides, the detection probability is also significantly improved compared to the benchmarking method.

Subspace Fitting Approach for Wideband Near-Field Localization

Two subspace fitting approaches are proposed for wideband near-field localization. Unlike in conventional far-field systems, where distance and angle can be estimated separately, spherical wave propagation in near-field systems couples these parameters. We therefore derive a frequency-domain near-field signal model for multi-target wideband systems and develop a subspace fitting-based MUSIC method that jointly estimates distance and angle. To reduce complexity, a Fresnel approximation MUSIC algorithm is further introduced to decouple the distance and angle parameters. Numerical results verify the effectiveness of both proposed approaches.