To achieve mono-static sensing in the integrated sensing and communication network, an additional receiving panel for sensing is needed to cover the blind area around the base station. However, high-resolution estimation requires a wideband signal with large-scale antennas, making the receiver expensive and power hungry with traditional high sampling-rate and bit-depth analog-to-digital converters (ADCs). In this paper, a low complexity sensing receiver with one-bit quantization is proposed. In addition, an adaptive time-varying threshold architecture is designed to solve the dynamic range problem of one-bit ADC. An algorithm combining tensor unfolding and one-bit compressive sensing is proposed first to estimate the parameters of strong signals, based on which they could be reconstructed and the thresholds of one-bit ADCs are tuned adaptively using an adjustable switch network. After that, parameters of the weak target could be estimated in a sequential manner. The Cramér-Rao lower bound of the weak signal is derived to verify the effectiveness of the method. Numerical results show that our method eliminates the dynamic range problem in theory and after adaptive quantization, the weak-to-strong power ratio has an improvement of 27 dB, which is mainly affected by the estimation accuracy of the strong signals.

The task offloading technology plays a vital role in the Internet of Vehicles (IoV) by satisfying diversified vehicular demands, such as energy consumption and processing delay of computing tasks. Unlike the current related works, which not only ignored wireless interference when making information exchange, but also overlooked the available resources of parked and moving vehicles, this paper proposes a comprehensive solution. First, we model vehicle speed using a truncated Gaussian distri bution, replacing simplistic average speed models in prior studies. Wireless interference in V2V/V2I communications significantly impacts communication quality and reliability, leading to packet loss, increased latency, and reduced throughput. For instance, in high-density traffic scenarios, interference can disrupt com munication links, hindering effective task offloading. Next, by incorporating wireless interference and effective communication duration in V2V and RSUs, we propose an analytical framework for task offloading that jointly optimizes energy consumption and processing delay, leveraging resources from parked/moving vehicles and RSUs. Furthermore, inspired by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), we design an Interference-Aware Multi-Agent Deep Deterministic Policy Gradient (IA-MADDPG) algorithm. The algorithm ensures resource load balancing while reducing energy consumption and latency, and improves the task offloading completion rate. Simulations validate the effectiveness of IA-MADDPG, demonstrating supe rior convergence speed, energy efficiency, and latency reduction compared to existing methods.

The evolution of industrial intelligent manufacturing necessitates wireless communication systems capable of replacing conventional wired infrastructures, offering superior flexibility, scalability, and reduced maintenance overhead. While 5 G New Radio (NR) Ultra-Reliable Low-Latency Communication (uRLLC) standards (Release 15-17) have shown promise for mission-critical applications, current implementations remain constrained by their unidirectional optimization paradigm, unable to simultaneously satisfy the dual imperatives of sub-millisecond latency ($\lt 1$ms) and 99.9999% reliability demanded by industrial control systems. To address these challenges, we present a transformative subband full-duplex (SBFD) network architecture that ensures persistent time-domain spectral availability for concurrent uplink/downlink operations, thereby eliminating direction-switching latency. Our solution introduces three key innovations: (1) an interference-aware SBFD resource allocation framework that strategically isolates UL/DL subbands to minimize cross-link interference (CLI), (2) a dual-optimization algorithm that jointly maximizes spectral efficiency while guaranteeing channel-adaptive reliability thresholds, and (3) a practical implementation scheme compatible with existing 5G NR physical layer specifications. Extensive simulations under realistic factory channel models demonstrate 58.3% reduction in aggregate CLI and 41.2% improvement in control command decoding accuracy compared to legacy half-duplex systems. This research establishes a new paradigm for wireless industrial networks, effectively closing the performance gap between 5G URLLC specifications and the exacting demands of Industry 4.0 applications.

Unmanned aerial vehicles (UAVs) are poised for explosive growth in the low-altitude economy, causing spectrum congestion and posing a challenge to airspace regulation. Although integrated sensing and communication (ISAC) enables simultaneous communication and sensing, alleviating the spectrum shortage, the capability of one single base station (BS) is generally limited. Therefore, a multi-BS cooperative ISAC system is developed to perceive the status of UAVs at the cell edge. Multiple BSs share the same time-frequency resources and adopt a time-division scheme to avoid mutual interference between communication and sensing functionalities. Specifically, the frame structure of the communication system is modified to accommodate the sensing functionality. A robust interference nulling based beam pattern is first proposed to prevent the line-of-sight (LoS) interference between BSs from overrunning the dynamic range of the analog-to-digital converter (ADC). Moreover, we designed a space-time block codec-based orthogonal frequency division multiplexing (OFDM) to separate echo signals originating from different BSs, which transforms the inter-BS reflected interference into bistatic sensing signals. Furthermore, a data-level fusion method based on the signal-to-interference-plus-noise ratio (SINR) of the range profile is applied to improve the positioning accuracy. The numerical results reveal that the proposed beam pattern greatly avoids LoS interference. The echo signals originating from neighboring BSs can assist in target detection and angle of arrival (AoA) estimation. Compared to soft fusion and single-BS schemes, the proposed fusion method enhances positioning precision by an order of magnitude, and is practically feasible even in the presence of clock synchronization errors.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless networks, enabling simultaneous high-speed communication and precise environmental awareness. This paper presents a novel ISAC imaging method, which leverages sparse multi-view channel state information (CSI) from existing communication infrastructure to reconstruct scattering fields, thereby achieving high-fidelity 3D imaging and environment reconstruction without the need for dedicated sensing hardware. A Neural Scattering Field Network (NSFNet) is designed to accomplish this task. The framework consists of two key components: 1) EdgeFusionNet, which extracts robust geometric features from sparse multi-view CSI using a multi-scale 3D CNN with edge-guided attention, and 2) MLP-based decoder that explicitly regresses view-dependent scattering coefficients, thereby addressing both the limited-view sampling challenge and the physical view-dependency of scattering. A self-supervised training strategy combining reconstruction loss and total variation regularization ensures accurate and smooth reconstructions. Experimental results demonstrate that NSFNet significantly outperforms compressed sensing and ablation deep learning baselines in complex scenarios, achieving superior performance in terms of F1-score (>0.83) and Chamfer Distance (<0.15 m). Furthermore, the method maintains stable performance under practical signal-to-noise ratio conditions and varying user equipment deployment densities, offering a scalable and hardware-efficient solution for ISAC-enabled environmental sensing. The proposed approach bridges the gap between sparse communication channel measurements and high-resolution 3D imaging, paving the way for seamless integration of sensing and communication in next-generation wireless networks.

With the rapid development of the low-altitude economy, the widespread deployment of unmanned aerial vehicles (UAVs) necessitates effective sensing technologies for airspace monitoring. Integrated sensing and communication (ISAC) enables mobile communication base stations (BSs) to function as sensing nodes, providing a promising solution for UAV detection. Accurate identification of UAVs often relies on the extraction of distinctive micro-Doppler signatures generated by their rotating blades. However, in urban environments, these signatures are frequently obscured due to low signal-to-noise ratio (SNR) and strong dynamic interference from vehicles, pedestrians, and, in particular, birds. To address this challenge, this paper proposes a robust micro-Doppler feature extraction method based on multicarrier integration and a rotor micro-Doppler null space pursuit (rmD-NSP) algorithm. In one real-world scenario, both a bird and a UAV appeared within the same range cell, with the UAV’s micro-Doppler signals heavily masked by the bird’s strong reflections. After applying the proposed algorithm, distinct micro-Doppler features are successfully extracted, revealing approximately eight rotor blade flashes of the UAV within a 0.1s interval and two wingbeat cycles of the bird within the 0.5s observation window.

A subarray-based near-field joint location and velocity estimation framework is proposed for sensing a moving target using extremely large-scale antenna arrays. To tackle the intricate near-field non-linear phase, the piecewise-far-field channel model is adopted, approximating the near-field channel by partitioning the transmit and receive arrays into subarrays and applying the near-field assumption between subarrays and the far-field assumption within each subarray. Based on this model, the complex near-field estimation problem can be transformed into a far-field joint multiple bistatic radar parameter estimation problem, enabling separable location and velocity estimation. An efficient three-stage algorithm is developed, exploiting joint sparsity across transmit-receive subarray pairs. In the first stage, a mixed-norm minimization method is employed to obtain coarse estimates of the location and complex channel gain, which are refined using the gradient descent method in the second stage. Finally, the velocity is estimated using the multiple signal classification spectrum estimation method, based on the refined location estimate. Simulation results demonstrate the effectiveness of the proposed framework and reveal a trade-off in system design: location estimation accuracy improves with increased subarray size, while velocity estimation benefits from a greater number of smaller subarrays.

The rapid advancement of the low-altitude economy (LAE) necessitates a fundamental shift from fragmented systems toward deeply integrated communication, sensing, navigation, and control capabilities. To this end, this paper proposes a low-altitude digital-intelligent network (LADIN) as an overarching architecture, with integrated sensing and communication (ISAC) serving as the core enabling technology that pervasively unifies its three layers. At the heterogeneous infrastructure layer, we detail an ISAC waveform design based on orthogonal frequency division multiplexing, enabling dual-purpose hardware to simultaneously achieve high-speed data transmission and high-precision environmental sensing. Within the intelligent data fusion layer, ISAC’s role expands into a multimodal fusion paradigm, providing the crucial electromagnetic sensing modality. This layer constructs a unified spatiotemporal feature space by introducing pluggable back-projection adapters and spatiotemporal modeling. These adapters systematically integrate heterogeneous data from ISAC, optical cameras, and light detection and ranging (LiDAR) by inverting their respective observation models, thereby overcoming representational disparities and association ambiguities. At the service and management layer, this coherent representation directly drives algorithmic processes and control policies. ISAC resources are virtualized into dynamically allocable assets, enabling closed-loop control that responds to the real-time state of the feature space, such as reconfiguring base station operational modes based on live situational awareness. Validation through multi-frequency collaborative sensing and multimodal fusion use cases demonstrates significant performance gains in tracking robustness, detection of near-zero radar cross-section targets such as balloons, and seamless urban airspace governance, conclusively establishing the transformative potential of a deeply integrated, ISAC-centric approach for future LAE systems.

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security methods, which depends on the additional communication interference costing extra power resources, in this paper, we investigate a novel physical layer security solution, under which the inherent radar sensing interference of the vehicles is utilized to secure wireless communications. To measure the performance of physical layer security methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that the sensing interference utilization, combined with joint design of transmission power and straight trajectory of the transmitter, achieves a secrecy rate of 3.92bps/Hz for different noise powers for the case of perfect channel state information (CSI). The proposed method maintains robustness, achieving a 60.17% improvement of TRSA_SR under unavailable CSI and location information of the Eve.

A novel modular extremely large-scale multiple-input-multiple-output integrated sensing and communication system is investigated in this paper. The piecewise-far-field channel model is employed to characterize both communication and sensing channels, capturing the far-field propagation within each subarray and the near-field effects among subarrays due to the small subarray aperture and large inter-subarray spacing. Then, a joint transmit-receive beamforming problem is formulated to optimize communication spectral efficiency while satisfying the sensing signal-to-clutter-plus-noise ratio requirement. To solve this problem, an alternating optimization framework is proposed to iteratively update the transmit beamformer and receive beamformer until convergence. For a fixed receive beamformer, a closed-form optimal analog beamformer is firstly derived by exploiting the near-field propagation characteristics among subarrays, transforming the transmit hybrid beamforming problem into a low-dimensional digital beamforming optimization and substantially reducing the computational complexity. Then, two efficient algorithms are proposed to solve the rank-constrained digital beamforming problem. First, the semi-closed form of the optimal digital beamformer is derived and shown to form a complex Stiefel manifold. Based on this structure, a joint Riemannian-Euclidean gradient descent algorithm is developed for iterative optimization. Second, an semidefinite relaxation-based approach is proposed, where a near-optimal solution is obtained through rank constraint relaxation and randomization. Extensive simulations validate the superiority of the proposed algorithms, revealing that the optimal subarray scale balances spatial multiplexing and beamforming gains based on user distance, while increasing subarray numbers significantly enhances range resolution due to more pronounced spherical wavefronts.

The increasing consumer application demands of unmanned aerial vehicles (UAVs) in low-altitude airspace lead to many challenges for traditional UAV monitoring in terms of small UAV target sensing probability, target imaging resolution, equipment cost, etc. To solve these problems cost-effectively, the integrated sensing and communication (ISAC) technology based on cellular networks shows potential communication and sensing capabilities by sharing the same hardware equipment. This paper proposes a novel UAV target imaging algorithm using the channel state information (CSI) based ISAC signal to improve the target sensing probability and achieve target imaging. An ISAC signal model is designed to reconstruct the target image from CSI based on the time-frequency transformation relationship between target motion and CSI. Considering the sparsity of target scattering points, a two-dimensional alternating direction method of multipliers joint autofocus (2D-ADMM-AF) algorithm is proposed using compressed sensing theory, which achieves super-resolution reconstruction of the target image while eliminating the high side lobe problem caused by the sparse aperture problem that may exist in the ISAC scene. Further, the image entropy is introduced in the reconstruction process to achieve target translation compensation, eliminating the image blur problem caused by the translational motion of the UAV target. The simulation results show that compared with the traditional algorithm, the image entropy reconstructed by the 2D-ADMM-AF is reduced by 27% and the operating efficiency is improved by 85.5%. In addition, the real data of the DJI M300 UAV is collected using the ISAC testbed to verify the ability to image targets with RCS=0.3m2, 0.7m×0.7m.

With the rapid application of unmanned aerial vehicles (UAVs) in urban areas, the identification and tracking of hovering UAVs have become critical challenges, significantly impacting the safety of aircraft take-off and landing operations. As a promising technology for 6G mobile systems, integrated sensing and communication (ISAC) can be used to detect high-mobility UAVs with a low deployment cost. The micro-Doppler signals from UAV rotors can be leveraged to address the detection of low-mobility and hovering UAVs using ISAC signals. However, determining whether the frame structure of the ISAC system can be used to identify UAVs, and how to accurately capture the weak rotor micro-Doppler signals of UAVs in complex environments, remain two challenging problems. This paper first proposes a novel frame structure for UAV micro-Doppler extraction and the representation of UAV micro-Doppler signals within the channel state information (CSI). Furthermore, to address complex environments and the interference caused by UAV body vibrations, the rotor micro-Doppler null space pursuit (rmD-NSP) algorithm and the feature extraction algorithm synchroextracting transform (SET) are designed to effectively separate UAV’s rotor micro-Doppler signals and enhance their features in the spectrogram. Finally, both simulation and hardware testbed demonstrate that the proposed rmD-NSP algorithm enables the ISAC base station (BS) to accurately and completely extract UAV’s rotor micro-Doppler signals. Within the observation period of 0.1 s, ISAC BS successfully captures eight rotations of the DJI M300 RTK UAV’s rotor in urban environments. Compared to the existing AM-FM NSP, NSP, MTD, EMD and VMD signal decomposition algorithms, the integrity of the rotor micro-Doppler features is improved by 60%.

The emergence of connected autonomous vehicles (CAVs) has transformed the realms of transportation and communications. Meeting the demands of future CAVs networks requires the seamless integration of two essential functions: communications and sensing. However, in dense CAVs scenarios, meeting the demands for one-to-one beam-CAV services become challenging due to limited spatial freedom and severe beams interference. In this paper, a novel sensing-assisted CAV s clustering and beams alignment method is proposed. Based on the echo signal, the kinematic parameters are measured, and the extended Kalman filter (EKF) is designed for angle tracking. On this basis, a CAV s clustering method is also proposed. Simulation results verify superior tracking performance compared to other methods. The root mean square error (RMSE) is less than 0.03 and the sum rates can be improved by up to 4 times.

Ultra-reliable and low latency (uRLLC) plays an important role in the context of automatic driving, in terms of the road safety, which mainly depends on fast data transmission and precise positioning. However, handling this data and positioning efficiently faces a significant challenge, because of high data volume and vehicles' mobility. To support the high rate of sensing data and positioning information sharing between vehicles and base stations, mmWave communication technology, featured by high frequency and bandwidth, is identified as an potential solution. In addition, constrained by the strong directionality of mmWave, to improve the performance of high reliability, low latency and precise sensing, realtime mmWave beam direction regulation is urgently needed. In this paper, based on the multi-source sensing information, we propose a joint design of fast and robust communication and sensing 3D mmWave beam tracking (MSI-ISACBT) algorithm for two kinds of dynamic motions. In detail, for the case of low dynamic motion, the beam direction can be updated by using historical state information and reflected echo signal strength, while it can be adjusted according to the image information acquired by the camera for the case of high dynamic motion. Simulations validate the effectiveness and correctness of the proposed MSI-ISACBT. Compared with other popular methods, MSI-ISACBT achieve a stable throughput of 2.8Gbps and more smooth beam angle control with 16ms delay.

Integrated sensing and communication integrated sensing and communications (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this article, we investigate transmit beamforming design for the multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multitarget sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among the reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multiobjective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between the communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, the simulation results validate that the proposed method achieves a better tradeoff between the multiuser communication and multitarget sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multitarget resolution capability and angle estimation accuracy.

Integrated sensing and communications (ISAC) has received considerable attention from both industry and academia. By sharing the spectrum and hardware platform, ISAC significantly reduces costs and improves spectral, energy, and hardware efficiencies. To support the large number of communication users (CUs) and sensing targets (STs), the design of multiple access (MA) is a fundamental issue in ISAC. MA techniques in ISAC are expected to avoid mutual interference between sensing and communicating functions under the critical constraints of both functions. In this article, we present an overview on approaches of MA for ISAC, from orthogonal transmission strategies to nonorthogonal ones, realized in time, frequency, code, spatial, delay-Doppler, power, and/or multiple domains. We discuss their individual implementation schemes and corresponding resource allocation strategies, as well as highlight future research opportunities.

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated by sensors, and transmitted to the control center through uplink wireless communications. The corresponding control commands are generated and sent back to the devices through downlink wireless communications. Since wireless communications, sensing and control are tightly coupled, there are big challenges on the modeling and design of such closed-loop systems. In particular, existing theoretical tools of these functionalities have different modelings and underlying assumptions, which make it difficult for them to collaborate with each other. Therefore, in this paper, an analytical closed-loop model is proposed, where the performances and resources of communication, sensing and control are deeply related. To achieve the optimal control performance, a co-design of communication resource allocation and control method is proposed, inspired by the model predictive control algorithm. Numerical results are provided to demonstrate the relationships between the resources and control performances.

Dual function radar and communications (DFRC) systems are the focus of growing research attention. The common DFRC setup considers simultaneous probing and information transmission to a remote receiver, typically involving complex radar-oriented waveforms, whose detection can induce a notable burden on the receiver. In many DFRC applications, the communication receivers are devices which are limited in terms of hardware, power, and memory resources. These receivers are required to extract the desired information from the received dual-function waveform, while operating with a given bit budget. In this paper, we design bit constrained communication receivers in dual-function systems, by considering hybrid analog/digital architectures and treating their operation as task-based quantization. We study two forms of analog processing in these hybrid receivers, allowing to combine inputs in different time instances and antennas or only in different antennas at the same time instance. Simulation results demonstrate that the proposed task-based quantization strategy outperforms receivers operating only in the digital domain with the same total number of quantization bits.

Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manner. To enhance the target recovery performance of FAR in complex electromagnetic environments, and particularly, its range-Doppler recovery performance, multi-Carrier AgilE phaSed Array Radar (CAESAR) was proposed. CAESAR extends FAR to multi-carrier waveforms while introducing the notion of spatial agility. In this paper, we theoretically analyze the range-Doppler recovery capabilities of CAESAR. Particularly, we derive conditions which guarantee accurate reconstruction of these range-Doppler parameters. These conditions indicate that by increasing the number of frequencies transmitted in each pulse, CAESAR improves performance over conventional FAR, especially in complex environments where some radar measurements are severely corrupted by interference.

Partitioning the antenna array into different subarrays is a flexible scheme in the joint radar and communication system. However, the traditional fixed partition of the antenna array cannot make full use of the complete aperture. In this paper, we propose a novel antenna partition scheme. In this scheme, the antenna is randomly and dynamically chosen as radar or communication unit. The dynamic randomness introduces extra channel capacity of the communication system, and enables the radar system approximately obtain the resolution and sidelobe level of a full antenna array simultaneously. The channel capacity, Cramér Rao Bound and the ambiguity function are theoretically analyzed. Pareto Front is used to demonstrate the performance improvement of the proposed system over the traditional fixed partition system.