Current Issue
2025
Vol. 14,
No. 4
2025,
14(4):
781-796.
Integrated Sensing And Communications (ISAC) based on reusing random communication signals within the existing network architecture may drastically reduce implementation costs, thereby accelerating the integration of sensing functionalities into current communication networks. However, the randomness of communication data introduces fluctuations in sensing performance across different signal realizations, leading to unstable sensing accuracy. To address this issue, we delve into random ISAC signal processing methods and propose a joint transceiver precoding optimization design for Multiple-Input Multiple-Output ISAC (MIMO-ISAC) systems. Specifically, considering target impulse response matrix estimation, we first define the Ergodic Cramér-Rao Bound (ECRB) as an average sensing performance metric under random signaling. By deriving the closed-form expression of the ECRB based on the distribution of complex inverse Wishart matrices, we theoretically reveal the performance loss arising when using random signals for sensing compared to the conventional deterministic orthogonal signals. Furthermore, we formulate the sensing-optimal subproblem by minimizing the ECRB and the communication-optimal subproblem of multiantenna multiuser signal estimation and derive the corresponding sensing-optimal and communication-optimal precoding designs. Subsequently, we extend the proposed transceiver precoding optimization framework to ISAC scenarios by explicitly constraining the communication requirements. Finally, through numerous simulations, we validate the effectiveness of the proposed method. The results demonstrate that the joint transceiver precoding design may allow high-accuracy target response matrix estimation while enabling flexible trade-offs between communication signal estimation and target response matrix estimation errors.
2025,
14(4):
797-808.
Covert Unmanned Aerial Vehicle (UAV) communication has garnered considerable attention for realizing a sustainable Low-Altitude Economy (LAE). Based on the Integrated Sensing And Communication (ISAC) framework, this paper studies the system strategies and resource allocation for a cooperative multi-UAV covert communication network, where multiple UAVs are employed to simultaneously conduct cooperative sensing and covert downlink transmissions to multiple Ground Users (GUs) in the presence of a mobile warden (Willie). To improve communication covertness, UAVs adaptively switch between Jamming Unmanned Aerial Vehicle (JUAV) mode and Information Unmanned Aerial Vehicle (IUAV) mode. To cope with the mobility of Willie, an Unscented Kalman Filtering (UKF)-based method is employed to track and predict Willie's location using delay and Doppler measurements extracted from ISAC echoes. By jointly optimizing the JUAV selection strategy, IUAV-GU scheduling, and communication/jamming power allocation, a real-time fairness transmission maximization problem is formulated. The Alternating Optimization (AO) approach is adopted to decompose the original problem into a series of sub-problems, resulting in an efficient sub-optimal solution. Simulation results demonstrate that the proposed scheme can accurately track Willie and effectively ensure covert downlink transmission.
2025,
14(4):
809-828.
Aiming to enhance sensing resolution and improve spectral efficiency, future Integrated Sensing And Communications (ISAC) systems are expected to incorporate extremely large-scale (XL) arrays and large bandwidths centered around high carrier frequencies. This design necessitates considering wideband and near-field effects. In this paper, the design of partially connected hybrid precoders for ISAC is refined and evaluated, focusing on the wideband near-field scenario and addressing monostatic and bistatic co-located Multiple-Input-Multiple-Output (MIMO). For monostatic MIMO, the Cramer-Rao Bound (CRB) for joint Direction-of-Arrival (DOA) and distance estimations of sensing wideband sources is rederived, serving as a performance metric for sensing. For bistatic MIMO, power irradiated at near-field targets is maximized while ensuring that the communication Quality of Service (QoS) for each user is maintained. To address the above nonconvex, high-dimensional problems, a direct alternative minimization, along with an indirect fully digital approximation, is proposed. This method decomposes the original problems into distinct subproblems, enabling effective solutions for each subproblem. Simulation results demonstrate that the proposed wideband near-field ISAC framework can achieve sensing and communication performance close to that of fully digital precoders, given an appropriate communication Signal-to-Noise Ratio (SNR) setting and transmit antenna grouping.
2025,
14(4):
829-841.
Because the Terahertz (THz) band is capable of achieving terabit-per-second communication rates and high-precision sensing, THz Integrated Sensing And Communication (ISAC) has become a key technology for future wireless systems. We propose a THz ISAC framework based on a delay-Doppler waveform, i.e., the Orthogonal Delay-Doppler Multiplexing (ODDM) modulation. A more general off-grid ODDM modulation input/output relationship is derived to eliminate the assumption that channel path delays and Doppler frequency shifts are integer multiples of their resolutions. For ODDM symbol detection, a time-domain channel equalizer based on the conjugate gradient method is proposed to optimize the computational complexity. Compared with orthogonal frequency division multiplexing, ODDM demonstrates higher Doppler robustness against the Doppler effect. A sensing estimation algorithm is designed to achieve high-precision estimates with low complexity. The results show that the multi-target estimation accuracy approaches Cramér-Rao Lower Bounds (CRLB).
2025,
14(4):
842-853.
With the emergence of the low-altitude economy, the communication and detection issues of Unmanned Aerial Vehicles (UAVs) have gained considerable attention. This paper investigates sensing reference signal design for Integrated Sensing And Communication (ISAC) in Orthogonal Frequency Division Multiplexing (OFDM) systems aimed at detecting long-range, high-speed UAVs. To address the ambiguity problem in long-range and high-speed UAV detection, traditional reference signal designs require densely arranged reference signals, leading to significant resource overhead. In addition, long-range detection based on OFDM waveforms faces challenges from Inter-Symbol Interference (ISI). To address these issues, this paper first proposes a reference signal pattern that supports long-range detection and resists ISI, achieving the maximum unambiguous detection range of the system with reduced resource overhead. Then, to address the challenge of high-speed detection, the paper incorporates range-rate into the Chinese Remainder Theorem-based method. Through the proper configuration of sensing reference signals and the cancellation of ghost targets, this approach significantly increases the unambiguous detection velocity while minimizing resource usage and avoiding the generation of ghost targets. The effectiveness of the proposed methods is validated through simulations. Simulation results show that compared with the traditional sensing reference signal design, our proposed scheme can reduce 72% overhead of reference signals for long-range and high-speed UAV detections.
2025,
14(4):
854-866.
This study explores the use of one-bit Digital-to-Analog Converters (DAC) to mitigate the challenges of high hardware costs and excessive power consumption in large-scale Multiple-Input Multiple-Output (MIMO) communication and radar systems. The present study focuses on the design of one-bit transmit waveforms for dual-functional radar and communication systems. Under preset communication Quality of Service (QoS) constraints, the objective was to minimize the integral sidelobe-to-mainlobe ratio of the radar transmit beampattern. This should help enhance the power concentration of the transmitted beampattern and improve the performance of the beampattern synthesis. To address the limited Degrees of Freedom (DoF) caused by one-bit quantization, this study employs symbol-level precoding technology and then fully utilizes the DoFs in spatial and temporal domains to assist waveform design based on the principle of Constructive Interference (CI). To address the nonconvex fractional quadratic objective function and the multiple nonconvex discrete constraints inherent in the proposed waveform design problem, this study introduces an algorithm that combines the Dinkelbach transform with the Alternating Direction Method of Multipliers (ADMM). This approach effectively tackles the NP-hard problem. The numerical results demonstrate that the designed waveform significantly reduces the required DAC resolution and achieves excellent radar beampattern performance while satisfying the QoS requirements of downlink multiuser communications.
2025,
14(4):
867-895.
Efficient Radio-Frequency (RF) stealth is crucial for Dual-Function Radar-Communication (DFRC) systems that detect radar stealth and con vert communication transmission. However, traditional beamforming schemes based on phased arrays and Multiple-Input Multiple-Output (MIMO) systems lack the ability to control the radiation energy in the range dimension, resulting in the facile interception of integrated transmission signals by enemy-owned passive detection systems. To address this issue, a joint transmit-receive beamforming design for Frequency Diversity Array MIMO (FDA-MIMO) DFRC systems is designed herein to achieve RF stealth. First, an integrated transmission signal model based on orthogonal waveform generation, frequency diversity modulation, and weighted transmission beamforming is constructed. The two-dimensional expression of the distance angle between the radar equivalent transmission beam pattern and the communication transmission channel is obtained through matched filtering and reception beamforming. Second, with communication information embedding and communication reachable rate as constraints, a joint optimization model for FDA-MIMO radar communication integrated transmission and reception beams for RF stealth is established. The model aims to simultaneously minimize the equivalent transmission beam power at the radar target and maximize the output signal-to-noise ratio. Finally, a joint optimization algorithm based on Weighted Mean-Square Error Minimization (WMMSE) and the Consensus Alternating Direction Method of Multiplier (C-ADMM) is proposed. Closed form expressions for each variable are derived and combined with convex optimization algorithms to achieve low-complexity solutions. The simulation results show that radar detection and communication transmission using the proposed method form a “point-to-point” pattern on the two-dimensional plane of range and angle, exhibiting good RF stealth capability. Simultaneously, this method can provide high clutter and interference suppression performance as well as a low communication bit error rate.
2025,
14(4):
896-914.
To address the low data rate issue in the design of Dual-Function Radar-Communication (DFRC) waveforms with radar detection as the primary function, this paper proposes an information modulation method for multiple sub-pulse structure waveforms called Sub-pulse Hybrid Modulation (SHM). The proposed SHM method utilizes time-, spectral-, and polarization-domain features from inter-subpulse and intra-subpulse sources to convey information. The DFRC waveform design problem is formulated based on minimizing cross- and auto-correlation Peak Sidelobe Levels (PSL), while considering constant envelope and SHM constraints. To tackle the resulting nonconvex and nondeterministic polynomial-hard optimization problem, the Spectral Majorization Minimization (SMM) algorithm is employed to monotonically decrease the objective function value. Furthermore, this paper explores an echo processing method that makes the Doppler frequency at the first zero point of the zero-delay intercept of the fuzzy function \begin{document}$ L - 1 $\end{document} times higher than that of the conventional waveform, where L is the number of sub-pulses. This enhancement ensures high Doppler tolerance for the DFRC waveform and enables effective detection of high-speed targets.
2025,
14(4):
915-927.
When radar and communication systems share the same frequency spectrum on the same platform, mutual interference may occur. In addition, mainlobe deceptive interferences pose a serious threat to radar target detection. To address these issues, we devise a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar and MIMO communication spectral coexistence system and propose a radar-centric joint transceiver design scheme. In this respect, the radar transmission waveform, radar receive filter, and communication transmission codebook are optimized to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) of the radar system, thereby enhancing the target detection probability while ensuring MIMO communication throughput. During the optimization process, the Alternating Optimization (AO) strategy is employed to decompose the problem into multiple subproblems, which are solved in an iterative way. Specifically, the radar receive filter is obtained using the Lagrange multiplier method. In addition, the communication transmission codebook is approximated using an inequality theorem, and the radar transmission waveform is optimized using Taylor expansion and relaxation algorithms. Simulation results reveal that this joint design method can effectively improve the SINR of the radar system while ensuring communication throughput, thereby considerably enhancing the performance of the FDA-MIMO radar and MIMO communication spectral coexistence system under mainlobe jamming conditions.
2025,
14(4):
928-949.
The integration technology of satellite communications and spaceborne Synthetic Aperture Radar (SAR) remote sensing aims to combine communication and remote sensing functionalities, enabling simultaneous data transmission and remote sensing imaging to meet the demands of efficient, covert, and secure information transfer, enhancing system multifunctionality. However, due to significant differences between the waveform characteristics, transceiver design, and signal processing algorithms of the technologies, integrating communication and remote sensing in spaceborne systems presents numerous challenges. This study proposes a passive wireless communication system based on information metasurface technology, combined with SAR echo modulation methods, to innovatively achieve the deep integration of ground-to-space communication and spaceborne SAR remote sensing. The system precisely modulates the scattering parameters of SAR echoes to enable passive wireless communication without affecting the quality of SAR imaging. Additionally, by leveraging electromagnetic backscatter properties instead of active transmission mechanisms, the system effectively ensures the electromagnetic concealment and information security of the communication link. A series of scene simulation experiments and spaceborne SAR data experiments were performed, which validated the system’s feasibility and effectiveness. Results indicate that while maintaining compatibility with traditional SAR waveform structures, the proposed system successfully achieved the synchronous operation of ground-to-space data transmission and spaceborne SAR imaging. The core objective of this research was to promote the deep integration of spaceborne SAR remote sensing systems and wireless communication technologies, aiming for the efficient utilization of spectrum resources and exploring the application of information metasurface technology in integration of communication and remote sensing systems, providing new research perspectives and technological potential in this field.
2025,
14(4):
950-960.
With the widespread application of Wi-Fi sensing technology in intelligent health monitoring, constructing high-quality perception datasets has become a key challenge. Particularly in monitoring abnormal behaviors, such as falls, traditional methods rely on repeated human experiments, which not only poses safety risks but also raises ethical concerns. To address these issues, this paper proposes a time-domain digital coding metasurface-assisted data acquisition method. By simulating the Doppler effect and micro-Doppler characteristics of the human body, the time-domain digital coding metasurface can effectively replace human experiments and assist in constructing Wi-Fi sensing datasets. To verify the feasibility of this method, we develop a time-domain digital coding metasurface with 0°–360° full-phase modulation capability. Experimental results show that the signals generated by the metasurface retain the motion characteristics of the human body, complement real samples, reduce the complexity of data collection, and finally improve the monitoring accuracy of the classification model significantly. This method provides an innovative and feasible solution for data acquisition for Wi-Fi sensing technology.
2025,
14(4):
961-973.
The widespread application of wireless communication devices in emerging scenarios (e.g., Vehicle-to-Everything, Low Earth Orbit Satellites) has gradually pushed communication frequencies toward higher bands, resulting in an increasingly prominent overlap with radar frequency bands. A Dual-Functional Radar-Communication (DFRC) system, with its joint signal processing capabilities and low-power characteristics, is regarded as effective in alleviating spectrum congestion. Unlike traditional antenna array architectures, Holographic Metasurface Antennas (HMAs) embed closely arranged metamaterial units, enabling the flexible configuration of each unit’s state to regulate frequency responses. This facilitates controllable and energy-efficient beamforming, offering potential for application in DFRC systems. Considering an HMA-based DFRC system that performs target sensing in a cluttered environment while providing communication services to multiple single-antenna users, this paper formulates an optimization problem to maximize the weighted sum of communication spectral efficiency and radar mutual information, subject to constraints on the transmission power and HMA frequency response. It jointly optimizes the involved digital precoder, HMA weight matrix, and receive filter to realize an HMA-based DFRC beamforming design. To tackle this nonconvex optimization challenge, we propose an alternating optimization algorithm based on fractional programming. This algorithm first employs fractional programming techniques to transform the original problem into more manageable subproblems, which are then alternately solved using methods such as Lagrangian dual decomposition and manifold optimization. Simulation results show that the beamforming design with the HMA array architecture achieves a flexible tradeoff between communication spectral efficiency and radar mutual information performance, approaching the performance of a fully digital array architecture.
2025,
14(4):
974-993.
This paper proposes an intelligent framework based on a cell-free network architecture, called HRT-Net. HRT-Net is designed to enhance multi-station collaborative sensing problems for Dual-Functional Radar-Communication (DFRC) systems, offering accurate and resource-efficient target location estimation. First, the sensing area is divided into sub-regions and a lightweight region selection network employing depthwise separable convolution; this approach coarsely identifies the target’s sub-region, reducing computational demands and enabling extensive area coverage. To tackle interstation data disparity, we propose a channel-wise unidimensional attention mechanism. This mechanism aggregates multi-station sensing data effectively, enhancing feature extraction and representation by generating attention weight maps that refine the original features. Finally, we design a target localization network featuring multi-scale and multi-residual connections. This network extracts comprehensive, deep features and achieves multi-level feature fusion, allowing for reliable mapping of data to the target coordinates. Extensive simulations and real-world experiments validate the effectiveness and robustness of our scheme. The results show that compared with the existing methods, HRT-Net achieves centimeter-level target localization with low computational complexity and minimal storage overhead.
2025,
14(4):
994-1004.
Beamforming enhances the received signal power by transmitting signals in specific directions. However, in high-speed and dynamic vehicular network scenarios, frequent channel state updates and beam adjustments impose substantial system overhead. Furthermore, real-time alignment between the beam and user location becomes challenging, leading to potential misalignment that undermines communication stability. Obstructions and channel fading in complex road environments further constrain the effectiveness of beamforming. To address these challenges, this study proposes a multimodal feature fusion beamforming method based on a convolutional neural network and an attention mechanism model to achieve sensor-assisted high-reliability communication. Data heterogeneity is solved by customizing data conversion and standardization strategies for radar and lidar data collected by sensors. Three-dimensional convolutional residual blocks are employed to extract multimodal features, while the cross-attention mechanism integrates integrate these features for beamforming. Experimental results show that the proposed method achieves an average Top-3 accuracy of nearly 90% in high-speed environments, which is substantially improved compared with the single-modal beamforming scheme.
2025,
14(4):
1005-1018.
To address the physical layer security challenges in low-altitude Unmanned Aerial Vehicle (UAV) communications, this paper proposes an Integrated Sensing And Communication (ISAC) scheme. For the proposed ISAC scheme, an online optimization framework for UAV trajectory and communication resource allocation is developed using Deep Reinforcement Learning (DRL). In the proposed scheme, artificial noise transmitted by a communication UAV is reused to simultaneously sense and jam a potential eavesdropping UAV, thereby enhancing secure communications for ground users. By estimating and predicting the state of the eavesdropping UAV, the trajectory and resource allocation design problem is reformulated as a Markov decision process. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, the optimal framework is learned over time, dynamically optimizing the communication UAV’s trajectory and resource allocation to maximize long-term sensing and secure communication performance. Simulation results demonstrate that the proposed scheme achieves a superior trade-off between sensing and security without degrading sensing performance and outperforms baseline methods in terms of secure communication performance. This validates the performance gains achieved through sensing and online trajectory design, as well as the potential and superior performance of applying DRL to the integrated design of sensing, communication, and trajectory.
2025,
14(4):
1019-1045.
Integrated Sensing And Communications (ISAC), a key technology for 6G networks, has attracted extensive attention from both academia and industry. Leveraging the widespread deployment of communication infrastructures, the integration of sensing functions into communication systems to achieve ISAC networks has emerged as a research focus. To this end, the signal design for communication-centric ISAC systems should be addressed first. Two main technical routes are considered for communication-centric signal design: (1) pilot-based sensing signal design and (2) data-based ISAC signal design. This paper provides an in-depth and systematic overview of signal design for the aforementioned technical routes. First, a comprehensive review of the existing literature on pilot-based signal design for sensing is presented. Then, the data-based ISAC signal design is analyzed. Finally, future research topics on the ISAC signal design are proposed.
2025,
14(4):
1046-1070.
Dual Function Radar and Communication (DFRC)-integrated electronic equipment platform, which combines detection and communication functions, effectively addresses issues such as platform limitations, resource constraints, and electromagnetic compatibility by sharing hardware platforms and transmitting waveforms. Therefore, it has become a research hotspot in recent years. The DFRC technology, centered on detection functionality and incorporating limited communication capabilities, has remarkable application prospects in typical detection scenarios, such as early warning and surveillance and tracking guidance under future combat conditions. This paper focuses on using the signal design method to optimize radar detection performance by effectively adjusting the trade-off between detection and communication in multi-domain resource utilization by guaranteeing a minimum communication performance. First, the performance measurement criteria of DFRC systems were summarized. Then, the paper provides a comprehensive introduction to the DFRC signal design methods under typical detection scenarios and a thorough analysis of the problems and current solutions of each signal design method. Finally, a summary and future research directions are outlined.
2025,
14(4):
1071-1091.
Joint radar communication leverages resource-sharing mechanisms to improve system spectrum utilization and achieve lightweight design. It has wide applications in air traffic control, healthcare monitoring, and autonomous vehicles. Traditional joint radar communication algorithms often rely on precise mathematical modeling and channel estimation and cannot adapt to dynamic and complex environments that are difficult to describe. Artificial Intelligence (AI), with its powerful learning ability, automatically learns features from large amounts of data without the need for explicit modeling, thereby promoting the deep fusion of radar communication. This article provides a systematic review of the research on AI-driven joint radar communication. Specifically, the model and challenges of the joint radar communication system are first elaborated. On this basis, the latest research progress on AI-driven joint radar communication is summarized from two aspects: radar communication coexistence and dual-functional radar communication. Finally, the article is summarized, and the potential technical challenges and future research directions in this field are described.
2025,
14(4):
1092-1114.
Compared to ground-based external radiation source radar, satellite signal-based external radiation source radar (i.e., satellite signal external radiation source radar) offers advantages such as global, all-time, and all-weather coverage, which can compensate for the limitations of ground-based external radiation source radar in terms of maritime coverage. In contrast to medium and high-altitude satellite signals, Low-Earth Orbit (LEO) communication satellite signals have advantages such as strong reception power and a large number of satellites, which can provide substantial detection range and accuracy for passive detection of maritime targets. In response to future development needs, this paper provides a detailed discussion of the research status and application prospects of satellite signal external radiation source radar, and presents a feasibility analysis for constructing a low-earth orbit communication satellite signal external radiation source radar system using Iridium and Starlink, two types of LEO communication satellite systems, which integrates high and low frequencies with both wide and narrow bandwidths. Based on this, the paper summarizes the technical challenges and potential solutions in the development of low-earth orbit communication satellite signal external radiation source radar systems. The aforementioned research can serve as an important reference for wide-area external radiation source radar detection.