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.

On the Feasibility of Passive Bistatic ISAC Based on Unmodified LoRa

Integrated Sensing and Communication (ISAC) enables sensing capabilities by reusing communication signals, making it particularly attractive for large-scale deployments through signals of opportunity. While most existing ISAC research targets wideband systems, Low Power Wide Area Network (LPWAN) technologies such as LoRa remain largely unexplored from a radar-like sensing perspective. Existing LoRa-based approaches mainly focus on motion detection or require modifications of the communication waveform, limiting their applicability in deployed networks. This paper investigates the feasibility of radar-like sensing using unmodified LoRa communication signals as signals of opportunity in a purely passive bistatic ISAC configuration. The proposed approach focuses on Doppler-based sensing to enable target separation and super-resolved target estimation without interfering with existing LoRa network operation. The analytically derived sensing capabilities are compared against simulation results and validated through bistatic measurements using two USRP B210 software-defined radios, confirming the feasibility of Doppler-based LoRa sensing under practical conditions and revealing relevant implementation challenges. The results demonstrate that LoRa-based ISAC enables highly scalable, large-area, low-resolution sensing by leveraging existing infrastructure, providing a complementary sensing capability to area-limited high-resolution 6G ISAC systems, and a foundation for future multi-node and data fusion extensions.

Rotatable Antenna-Enabled Near-Field Integrated Sensing and Communication

In this paper, we propose leveraging rotatable antennas (RAs) to enhance near-field communication and sensing by exploiting a new orientation-domain spatial degree-of-freedom (DoF) provided by element-wise antenna rotation. Specifically, we investigate an RA-enabled near-field integrated sensing and communication (ISAC) system with sub-connected hybrid beamforming, where each transmit RA can independently adjust its boresight direction under a practical rotation constraint. A spherical-wave channel model incorporating orientation-dependent antenna gains is established to characterize multi-user communication and target sensing in the presence of clutters. Based on this model, a weighted communication-sensing utility maximization problem is formulated by jointly optimizing the receive beamformer, digital beamformer, analog beamformer, and RA boresight directions. To solve the resulting non-convex problem, an alternating optimization algorithm is developed by combining fractional programming, Riemannian optimization, and a spherical-cap Frank--Wolfe-based boresight update. To further understand the impact of RA rotation on near-field sensing, we derive a closed-form root Cramer--Rao bound (RCRB) expression. Simulation results demonstrate the convergence and effectiveness of the proposed algorithm. It is shown that the RA-enabled hybrid design can match or even outperform the fully-digital FPA benchmark in some regimes, indicating that the orientation-domain DoF introduced by element-wise rotation can compensate for limited RF chains. The RCRB and beampattern results further show that RA rotation improves off-broadside sensing accuracy, enhances range-domain focusing, and suppresses same-angle clutters in the near field.

Towards Standardizing Affine Frequency Division Multiplexing (AFDM) for Future Wireless Networks

Affine frequency division multiplexing~(AFDM) has emerged as a compelling waveform candidate for future wireless networks, owing to its strong resilience to doubly selective channels and its ability to enable the seamless integration of communication and sensing functionalities. Against this context, this article provides a systematic study of AFDM from a standardization perspective. We first introduce the principles of AFDM and discuss the major considerations involved in waveform standardization. We then examine the backwards compatibility of AFDM with 4G/5G multi-numerology frameworks and their anticipated evolution, frequency-modulated continuous-wave (FMCW) radar waveforms, and long-range (LoRa) modulation, demonstrating that AFDM can be incorporated into legacy processing chains with limited modification. Key standardization-critical capabilities are further discussed, including multiple-antenna and multi-user support, and peak-to-average power ratio (PAPR). Finally, we investigate the potential of AFDM in several emerging scenarios, including non-terrestrial networks~(NTN), integrated sensing and communications (ISAC), vehicle-to-everything (V2X), and underwater acoustic (UWA) communications, whereby severe delay-Doppler dispersion places stringent demands on waveform robustness. Through these explorations, it is shown that that AFDM represents a timely and compelling technology for future wireless networks.

Max-Min Secrecy Rate Optimization for Secure ISAC Networks: Global Optimization and Low-Complexity Algorithm

In this paper, we investigate a secure integrated sensing and communication (ISAC) system in which multiple communication users (CUs) coexist with multiple untrusted sensing users (SUs) that may eavesdrop on the confidential information intended for the CUs. To promote security fairness among users, we formulate a max-min secrecy rate optimization problem subject to a transmit power budget and sensing quality requirements characterized by beampattern matching error constraints. The resulting design problem is highly non-convex due to the secrecy rate expressions and non-convex sensing constraints. To address these challenges, we first reformulate the problem using semidefinite relaxation (SDR). Based on the reformulated problem, we develop a branch-and-bound (BB) framework combined with convex relaxations to obtain the globally optimal solution within a prescribed accuracy. To further reduce computational complexity, we propose a low-complexity algorithm based on successive convex approximation (SCA), which iteratively solves a sequence of convex subproblems and converges to a local solution. Numerical results demonstrate that the proposed BB algorithm achieves the global optimum and provides a benchmark for performance evaluation. Moreover, the proposed SCA-based algorithm attains near-optimal secrecy performance with significantly lower computational complexity, making it attractive for practical ISAC deployments.

Inverse Learning assisted V2I Communication for Intent Based 6G ISAC Vehicular Networks

6G is expected to bring unprecedented advancements in the capabilities of vehicular networks. However, the advent of 6G will also introduce changes in the operation of vehicular communication infrastructures such as roadside units (RSUs), including the incorporation of autonomous intent-based network paradigm and integrated sensing and communication (ISAC) capabilities. While ISAC enables sensing and communication within a single 6G network node, intent-based network design paradigm ensures that network nodes such as RSUs, act as autonomous cognitive agents to fulfill the objectives of their respective communication service providers. This paradigm shift necessitates the development of V2I communication strategies that learns and adapts to the sensing-assisted communication and the autonomous decision-making strategies of RSUs. We model the RSU as a constrained utility maximizer, where the utility function characterizes the RSU intent, and formulate an inverse learning (IL) problem to infer the underlying utility function from observed ISAC RSU actions, for example the adaptive beamwidth allocation in response to the kinematic states of vehicles within a vehicular micro-cloud (VMC). The main contributions of this paper are: (i) ATIL, a nonparametric method based on Afriat theorem for fixed utility learning; (ii) FICNNIL, a parametric approach using fully input-concave neural networks, for structured fixed utility learning; and (iii) PICNNIL, a parametric approach based on partially input-concave neural networks, for inverse learning of state-dependent utilities. (iv) Federated inverse learning algorithms FedFICNNIL and FedPICNNIL for fixed and state dependent utility, respectively. We demonstrate the proposed IL-based framework for two V2I communication applications in VMCs, namely predictive scheduling for cooperative data downloading and dynamic cluster-head selection.

Quantization Limitations of Leakage Suppression in Self-Calibrating Monostatic Integrated Sensing and Communication MIMO Systems

Power leaking directly from transmitting into receiving radio-frequency chains is a key challenge in the realization of monostatic sensing applications with multi-antenna communication front-ends, to which a promising solution is digitally precoding transmitted signals for improved leakage suppression. While digital transmit precodings perform well in theory, real-world deployments typically exhibit severely degraded leakage suppression. This work investigates quantization noise as a primary factor limiting the performance of such precoding schemes. A closed-form solution predicting the impact of quantization noise on the performance of arbitrary digital joint leakage estimation and leakage suppression precodings is derived, numerically analyzed, and validated in a hardware testbed.

On the Robustness of AFBM Sensing to Power Amplifier Nonlinearities

We investigate the impact of power amplifier (PA) nonlinearities on the sensing performance of affine filter bank modulation (AFBM). While AFBM offers several advantageous properties for integrated sensing and communications (ISAC) - including reduced out-of-band emission (OOBE), low peak-to-average power ratio (PAPR), and natural robustness to doubly-dispersive (DD) channel effects - mitigating waveform distortion typically requires highly linear PAs. This creates a fundamental contradiction with ISAC applications, which demand high transmit power for reliable sensing. Our analytical results reveal that the structure of the effective AFBM modulation matrix dictates how distortion propagates within the ambiguity function (AF). Furthermore, simulations demonstrate that both the AF and the overall sensing performance of AFBM remain remarkably insensitive to such nonlinearities. These findings highlight the robustness of AFBM, making it a highly viable candidate for practical ISAC deployments constrained by hardware impairments.

Coherent Multiband OFDM Sensing via Low-Complexity Gap Reconstruction

This paper investigates coherent multiband orthogonal frequency division multiplexing (OFDM) sensing within an integrated sensing and communication (ISAC) framework. We consider an intra-band configuration in which two sensing subbands of equal width are allocated symmetrically within the same OFDM channel, while the central portion remains available for communication. We address the reconstruction of missing frequency-domain samples induced by the spectral gap and the suppression of the resulting grating lobes in the delay profile. To this end, we propose a low-complexity iterative reconstruction method consisting of an initial delay-domain equalization stage and an iterative apodization-based operator with data-consistency enforcement. Performance results for multi-target scenarios show that the proposed approach remains close to the full-band reference for moderate gap sizes and degrades only for larger gaps because of residual grating lobes. Compared with the compressed-sensing-based orthogonal matching pursuit (OMP) baseline, it exhibits a more favorable performance trend as the number of targets increases, especially in the practically relevant low-signal-to-noise ratio (SNR) regime, while offering a complexity scaling that is independent of the estimated number of targets.

Robust Secure Beamforming for Movable Antenna Enhanced Integrated Sensing and Communications

In this letter, we investigate robust beamforming design for a movable antenna (MA)-enhanced secure integrated sensing and communications (ISAC) system with imperfect eaves?dropping channel state information (CSI). To improve radar sensing performance, we formulate a radar signal-to-interference?plus-noise ratio (SINR) maximization problem by jointly opti?mizing the transmit beamforming and antenna placement while ensuring communication data security. However, the resulting op?timization problem is inherently intractable due to the nonlinea mapping from antenna positions to channel coefficients, as well as the eavesdropper (Eve) channel uncertainty. To handle these challenges, we propose a block coordinate descent (BCD)-based algorithm incorporating successive convex approximation (SCA) and fractional programming (FP) techniques. Simulation results show that our proposed algorithm exhibits fast convergence and achieves a significant improvement in the radar SINR while guaranteeing communication security.