Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.

Accurate beam prediction is essential for maintaining reliable links and high spectral efficiency in dynamic low-altitude wireless networks. However, existing approaches often fail to capture the deep correlations across heterogeneous sensing modalities, limiting their adaptability in complex three-dimensional environments. To overcome these challenges, we propose a multi-modal predictive beamforming method based on a cross-attention fusion mechanism that jointly leverages visual and structured sensor data. The proposed model utilizes a Convolutional Neural Network (CNN) to learn multi-scale spatial feature hierarchies from visual images and a Transformer encoder to capture cross-dimensional dependencies within sensor data. Then, a cross-attention fusion module is introduced to integrate complementary information between the two modalities, generating a unified and discriminative representation for accurate beam prediction. Through experimental evaluations conducted on a real-world dataset, our method reaches 79.7% Top-1 accuracy and 99.3% Top-3 accuracy, surpassing the 3D ResNet-Transformer baseline by 4.4%-23.2% across Top-1 to Top-5 metrics. These results verify that multi-modal cross-attention fusion is effective for intelligent beam selection in dynamic low-altitude wireless networks.

Combining wireless sensing and edge intelligence, edge perception networks enable intelligent data collection and processing at the network edge. However, traditional sample partition based horizontal federated edge learning (HFEEL) struggles to effectively fuse complementary multi-view information from distributed devices. To address this limitation, we propose a vertical federated edge learning (VFEEL) framework tailored for feature-partitioned sensing data. In this paper, we consider an integrated sensing, communication, and computation (ISCC)-enabled edge perception network, where multiple edge devices utilize wireless signals to sense environmental information for updating their local models, and the edge server aggregates feature embeddings via over-the-air computation (AirComp) for global model training. First, we analyze the convergence behavior of the ISCC-enabled VFEEL in terms of the loss function degradation in the presence of wireless sensing noise and aggregation distortions during AirComp. Then, to accelerate convergence, we aim to optimize the batch size, sensing power, and transmission power control at edge devices as well as the denoising factors at the edge server under limited network constraints on overall energy consumption and per-round latency. Due to the tight coupling of variables, the problem is non-convex. To address this problem, we design an alternating optimization-based algorithm to efficiently obtain a high-quality solution. Numerical results are conducted based on a human motion recognition task to verify that the proposed ISCC-enabled VFEEL algorithm achieves higher accuracy compared with other benchmarking schemes including ISCC-enabled HFEEL approach.

Semantic communication is increasingly recognized as a promising paradigm for enhancing the communication capabilities of wireless systems in the 6G era. Existing deep learning (DL)-based semantic methods typically enhance system robustness through module-centric strategies, where additional components are integrated into the model. Due to the significant computational overhead, these strategies are often unsuitable for resource-limited systems, such as the emerging low-altitude wireless networks (LAWNs). Moreover, most existing methods are optimized for fixed channel models and lack architectural adaptability across diverse environments, leading to repeated retraining and increased maintenance complexity. To address these challenges, we propose a novel Dual-Branch Architecture (DBA) for semantic communication. DBA employs a training-only auxiliary decoder branch to provide noise-free supervision for the main decoder, thereby enhancing robustness without adding deployment or inference overhead. Specifically, we introduce a contrastive learning mechanism to align the outputs of the noisy and noise-free branches, reinforcing semantic consistency. Building on this, we further propose the Extensible Multi-Channel Architecture (EMCA), a multi-branch design that incorporates multiple decoder branches optimized for different channel models and jointly trains them with a shared encoder and auxiliary branch, improving scalability without duplicating model parameters. Simulation results demonstrate that DBA and EMCA consistently outperform existing baselines in terms of semantic fidelity across additive white Gaussian noise (AWGN), Rayleigh, and Rician fading channels, without incurring any additional inference cost. Additionally, experiments conducted using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP) further validate the robustness and practicality of the proposed methods in practical environmental applications.

This paper investigates an intelligent reflecting surface (IRS) aided wireless federated learning (FL) system, where an access point (AP) coordinates multiple edge devices to train a machine leaning model without sharing their own raw data. During the training process, we exploit the joint channel recon figuration via IRS and resource allocation design to reduce the latency of a FL task. Particularly, we propose three transmission protocols for assisting the local model uploading from multiple devices to an AP, namely IRS aided time division multiple access (I-TDMA), IRS aided frequency division multiple access (I-FDMA), and IRS aided non-orthogonal multiple access (I NOMA), to investigate the impact of IRS on the multiple access for FL. Under the three protocols, we minimize the per-round latency subject to a given training loss by jointly optimizing the device scheduling, IRS phase-shifts, and communication computation resource allocation. For the associated problem under I-TDMA, an efficient algorithm is proposed to solve it optimally by exploiting its intrinsic structure, whereas the high quality solutions of the problems under I-FDMA and I-NOMA are obtained by invoking a successive convex approximation (SCA) based approach. Then, we further develop a theoretical framework for the performance comparison of the proposed three transmission protocols. Sufficient conditions for ensuring that I-TDMA outperforms I-NOMA and those of its opposite are unveiled, which is fundamentally different from that NOMA always outperforms TDMA in the system without IRS. Simulation results validate our theoretical findings and also demonstrate the usefulness of IRS for enhancing the fundamental tradeoff between the learning latency and learning accuracy.

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air computation (AirComp) as a transformative paradigm for edge intelligence. To enhance the efficiency and scalability of AirComp systems, this paper proposes a comprehensive dual-approach framework that systematically transitions from traditional mathematical optimization to deep reinforcement learning (DRL) for resource allocation under execution uncertainty. Specifically, we establish a rigorous system model capturing execution uncertainty via Gamma-distributed computational workloads, resulting in challenging nonlinear optimization problems involving complex Gamma functions. For single-user scenarios, we design advanced block coordinate descent (BCD) and majorization-maximization (MM) algorithms, which yield semi-closed-form solutions with provable performance guarantees. However, conventional optimization approaches become computationally intractable in dynamic multi-user environments due to inter-user interference and resource contention. To this end, we introduce a Deep Q-Network (DQN)-based DRL framework capable of adaptively learning optimal policies through environment interaction. Our dual methodology effectively bridges analytical tractability with adaptive intelligence, leveraging optimization for foundational insight and learning for real-time adaptability. Extensive numerical results corroborate the performance gains achieved via increased edge server density and validate the superiority of our optimization-to-learning paradigm in next-generation AirComp systems.

Privacy security constitutes a critical challenge in low-altitude wireless communications. Motivated by the application requirements for stereoscopic coverage and multi-domain collaboration, this paper investigates a friendly jamming-assisted air-ground cooperative covert transmission scheme. In the considered system, an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) serves as a network hub. It relays confidential signals from an aerial hovering platform to ground users while cooperating with terrestrial jammer to realize environment-independent directional jamming. Benefiting from the UAV's relaying functionality, this architecture can significantly enhance the flexibility of the jamming mechanism and the security of the jamming node. With the objective of maximizing the UAV's energy efficiency associated with effective throughput, we formulate a joint optimization problem under strict covertness constraints. To solve this problem, we propose an algorithm that integrates semidefinite relaxation (SDR), the Dinkelbach method, and Gaussian randomization within a double deep Q-network (DDQN) framework. The UAV trajectory, onboard resource, user scheduling and RIS parameters are jointly optimized to simultaneously ensure the communication covertness and transmission performance. Numerical simulation results validate the superiority of the proposed scheme compared to benchmark solutions.

This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates multiple edge devices to wirelessly sense the objects and use the sensing data to collaboratively train a machine learning model for recognition tasks. In this system, over-the-air computation (AirComp) is employed to enable one-shot model aggregation from edge devices. Under this setup, we analyze the convergence behavior of the ISCC-enabled Air-FEEL in terms of the loss function degradation, by particularly taking into account the wireless sensing noise during the training data acquisition and the AirComp distortions during the over-the-air model aggregation. The result theoretically shows that sensing, communication, and computation compete for network resources to jointly decide the convergence rate. Based on the analysis, we design the ISCC parameters under the target of maximizing the loss function degradation while ensuring the latency and energy budgets in each round. The challenge lies on the tightly coupled processes of sensing, communication, and computation among different devices. To tackle the challenge, we derive a low-complexity ISCC algorithm by alternately optimizing the batch size control and the network resource allocation. It is found that for each device, less sensing power should be consumed if a larger batch of data samples is obtained and vice versa. Besides, with a given batch size, the optimal computation speed of one device is the minimum one that satisfies the latency constraint. Numerical results based on a human motion recognition task verify the theoretical convergence analysis and show that the proposed ISCC algorithm well coordinates the batch size control and resource allocation among sensing, communication, and computation to enhance the learning performance.

Over-the-air computation (AirComp) exploits the waveform superposition of wireless channels for fast data aggregation from multiple devices. The implementation of AirComp requires amplitude alignment among devices, which requires better channel conditions. Meanwhile, movable antenna (MA) is an emerging method to create better channel states via local antenna movement. To fully utilize the channel gain obtained by adjusting the positions of MA, we consider a MA-enabled AirComp system equipped with one-dimensional MA at transmitter to aggregate wireless data from a large number of devices. We aim to minimize the mean squared error (MSE) by jointly optimizing the antenna position vectors (APV), transmit power, and denoising factor at devices. To address this highly non-convex problem, an alternating optimization (AO) based algorithm is adopted by decomposing it into two sub-problems for power control and APV optimization, respectively. Specifically, we obtain a semi-closed form solution of transmit power control and denoising factor under any given antenna position, and then use second-order Taylor expansion to derive a more tractable MSE counterpart for APV optimization under successive convex approximation (SCA) technique. Experimental results show that, compared with other benchmark schemes, our proposed scheme demonstrates better performance.

Movable antennas (MAs) have emerged as a promising enhancement for integrated sensing and communication (ISAC) by dynamically adjusting antenna positions to significantly improve channel quality and overall ISAC system performance. In this paper, we investigate an MA-enabled ISAC system, where an ISAC base station (BS) equipped with a one-dimensional MA array needs to communicate with devices and detect potential targets at the same time. In particular, we aim to maximize the downlink common (minimum) throughput among all devices by jointly optimizing the transmit beamforming and antenna position vector (APV) at BS, while ensuring beampattern gain constraints in specified directions for sensing tasks and maximum transmit power constraint. Note that the formulated problem is highly non-convex and hard to be solved. To tackle with this problem, we adopt an alternating optimization (AO) based algorithm by decomposing the original problem into two subproblems. In the first subproblem, we construct a surrogate function via second-order polynomial expansion to optimize APV under successive convex approximation (SCA) technique. In the second subproblem, we use the bisection method to obtain feasible transmit beamforming under any given antenna position. The numerical results demonstrate that our proposed scheme not only improves the communication performance of communication devices but also ensures the required sensing performance.

Future 6G Integrated Sensing and Communication (ISAC) networks are expected to reuse data payload signals for both communication and sensing. However, the inherent randomness of these signals can degrade ranging accuracy. To address this challenge, this paper studies power allocation (PA) strategies for CP-OFDM-based ISAC systems operating under random signaling, with the goal of reducing the sidelobe levels in the periodic auto-correlation function (P-ACF) of the ISAC signal. Specifically, we first derive closed-form expressions for the average squared P-ACF, and then formulate an optimization problem that minimizes the expected integrated sidelobe level (EISL) under arbitrary constellation mappings. We rigorously prove that, across all constellations, a uniform PA scheme yields the lowest ranging sidelobe levels, both in terms of the EISL and at each individual lag. Additionally, we extend our analysis to scenarios involving frequency-domain zero-padding. In such cases, we show that uniform PA no longer guarantees optimal sidelobe suppression. To address this, we propose a projected gradient descent (PGD) algorithm to find a locally optimal PA scheme that minimizes the EISL. Finally, our theoretical results are substantiated by extensive simulations, which confirm the effectiveness of the proposed PA methods in suppressing the ranging sidelobe levels of random OFDM signals.

A compact antenna array is developed for W-band vehicle mounted millimeter-wave radar, also suitable for Internet of Things (IoT) and intelligent vehicular transportation systems (IVTSs). The proposed array provides a flat shoulder shaped (FSS) radiation pattern, obtained using a beamforming algorithm, enabling medium- and long-range radar detection and information transmission between wireless devices. The beamforming algorithm determines the excitation amplitudes and phases of each subarray. Power dividers are designed using a scheme of equal/unequal power division to meet the requirements of amplitude. And phase shifters are used to create required phases. To obtain a compact design, a substrate integrated waveguide (SIW) feeding structure are employed to integrate power dividers, phase shifters, and waveguide transitions into a single part. Quadratic recursive exhaustive search in combination with full-wave simulation is used to optimize the design. Further improvements are made to achieve a more compact feeding structure. The realized array provides a stable FSS radiation pattern in the H-plane. In addition, its maximal gain appears in the boresight direction, which is usually a challenge for patch element array. The realized gain is 22 dBi, and the ripple in the shoulder range is less than 2.3 dB, which is better than other designs. The feeding structure is only 14 mm$\times 16$mm large, showing a minimized design. The demonstrated antenna presents itself to be an excellent hardware candidate for IoT and IVTS.

We propose a robust beamforming design methodology for integrated sensing and communications (ISACs) beamform, where the beamforming design is investigated under the sensing optimal beamforming designed to overcome the channel uncertainty that arises from the communication system. Under the assumption that the channel state information (CSI) error is elliptically bounded, we study the robust ISAC beamforming design problem with the minimization of the Cramér-Rao bound (CRB) under the signal-to-noise ratio (SINR) threshold constraint. We consider the long-range and near-range cases separately and categorize them into point-target and extended-target for processing. In the point target scenario, we address the problem through distributed optimization using the S-procedure and solve it with the semidefinite relaxation (SDR) method. Meanwhile, in the extended target scenario, we transform the infinite constraints of the robust ISAC design problem into a finite set, employing linear matrix inequalities (LMIs) for equivalent representation. Under specific conditions, we illustrate that the SDR problem in this scenario can yield a rank-1 solution. Simulation results verify the effectiveness of the proposed CRB optimizationmin method and prove its application value in the next generation of Internet of Things devices.

The rapid advancement of Internet of Things (IoT) services and the evolution toward the sixth generation (6 G) have positioned unmanned aerial vehicles (UAVs) as critical enablers of low-altitude wireless networks (LAWNs). This work investigates the co-design of integrated sensing, communication, and control ($\mathbf {SC^{2}}$) for multi-UAV cooperative systems with finite blocklength (FBL) transmission. In particular, the UAVs continuously monitor the state of the field robots and transmit their observations to the robot controller to ensure stable control while cooperating to localize an unknown sensing target (ST). To this end, a weighted optimization problem is first formulated by jointly considering the control and localization performance in terms of the linear quadratic regulator (LQR) cost and the determinant of the Fisher information matrix (FIM), respectively. The resultant problem, optimizing resource allocations, the UAVs' deployment positions, and multi-user scheduling, is non-convex. To circumvent this challenge, we first derive a closed-form expression of the LQR cost with respect to other variables. Subsequently, the non-convex optimization problem is decomposed into a series of sub-problems by leveraging the alternating optimization (AO) approach, in which the difference of convex functions (DC) programming and projected gradient descent (PGD) method are employed to obtain an efficient near-optimal solution. Furthermore, the convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Extensive simulation results are presented to validate the effectiveness of our proposed approach compared to the benchmark schemes and reveal the trade-off between control and sensing performance.

The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, playing a crucial role in enhancing the rate and spectral efficiency of wireless networks. As ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region where the spherical wavefront propagates. Near-field beam training requires information on both angle and distance, which inevitably leads to a significant increase in the beam training overhead. To address this challenge, we propose a near-field beam training method based on deep learning. Specifically, we employ a convolutional neural network (CNN) to efficiently extract channel characteristics from historical data by strategically selecting padding and kernel sizes. The negative value of the user average achievable rate is utilized as the loss function to optimize the beamformer, maximizing the achievable rate in multi-user networks without relying on predefined beam codebooks. Once deployed, the model requires only pre-estimated channel state information (CSI) to compute the optimal beamforming vector. Simulation results demonstrate that the proposed scheme achieves more stable beamforming gains and substantially outperforms traditional beam training approaches. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.

Existing communication signal-based sensing systems mainly rely on the orthogonal frequency division multiplexing (OFDM) technique due to its remarkable communication performance. However, extracting Doppler shifts from the received signal is not straightforward for OFDM and usually requires additional operations. The recently emerging orthogonal time frequency space (OTFS) modulation, which employs the Delay-Doppler (DD) domain for data transmission, can reveal the physical wireless propagation environments and provide the DD information directly. This paper investigates the moving multi-target sensing problem based on OTFS signaling. In particular, we attempt to tackle sensing and data association tasks concurrently by using the time delay (TD) and Doppler information from OTFS channel estimation. To this end, we formulate a mixed-integer optimization problem and approximate it as a convex problem. Simulation results has demonstrated the effectiveness of the proposed method.

In this paper, we investigate the robust waveform design problem for integrated sensing and communications (ISAC) in the presence of imperfect communcation channel state information (CSI). Specifically, the estimation error obtained through the minimum mean squared error (MMSE) criterion is used to represent sensing performance. Subsequently, under the premise of minimizing the estimation error, constraints on signal-to-interference-plus-noise ratio (SINR) outage probability and power budget are introduced. The non-convex optimization problem is then addressed using the semidefinite relaxation (SDR) and sphere bounding method. Simulation results demonstrate a enhancement in both sensing and communication performance with the proposed robust waveform design, validating the effectiveness and robustness of the proposed approach.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems’ rate and spectral efficiency. However, as ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region. This inevitably leads to a significant increase in the overhead of beam training, requiring two-dimensional beam searching in both the angle and the distance domain. To address this problem, we propose a learning-based codebook-free near-field beamforming method. We strategically select padding and kernel size of convolutional neural network to efficiently extract complex channel state information features. We optimize the beamformers to maximize achievable rates in a multi-user network without predefined beam codebooks. Our solution requires only pre-estimated channel state information for optimal beamforming vector derivation during deployment. Simulation results demonstrate stable beamforming gain compared to baseline schemes, and the deep learning approach substantially reduces near-field beam training overhead.

In this paper, we introduce a novel communication-assisted sensing (CAS) framework that explores the potential coordination gains offered by the integrated sensing and communication technique. The CAS system endows users with beyond-line-of-the-sight sensing capabilities, supported by a dual-functional base station that enables simultaneous sensing and communication. To delve into the system's fundamental limits, we characterize the information-theoretic framework of the CAS system in terms of rate-distortion theory. We reveal the achievable overall distortion between the target's state and the reconstructions at the end-user, referred to as the sensing quality of service, within a special case where the distortion metric is separable for sensing and communication processes. As a case study, we employ a typical application to demonstrate distortion minimization under the ISAC signaling strategy, showcasing the potential of CAS in enhancing sensing capabilities.

We propose a robust beamforming design methodology for integrated sensing and communications beamforms. The beamforming design aims to address channel uncertainties in the communication system by optimizing the sensing beamform. Assuming the Channel State Information (CSI) error is elliptically bounded, we investigate the robust integrated sensing and communication (ISAC) beamforming design problem, focusing on minimizing the Cramér-Rao bound (CRB) under a signal-to-noise ratio (SINR) threshold constraint. The problem is addressed through distributed optimization using the S-procedure and solved with the SDR method. Simulation results verify the effectiveness of the proposed CRB_min method.

In this paper, we study the parameter estimation performance for monostatic downlink integrated sensing and communications (ISAC) systems. In particular, we analyze the mean squared error (MSE) lower bound for target sensing in the downlink ISAC system that reveals the suboptimality in re-using the conventional communication waveform for sensing. To realize a practical dual-functional waveform, we propose a waveform augmentation strategy that imposes an extra signal structure, namely the degrees-of-freedom (DoF) completion method. The proposed approach is capable of improving the parameter estimation performance of the ISAC system and achieving the derived MSE lower bound. To improve the performance of the proposed strategy, we formulate an MSE minimization problem to design the ISAC precoder, subject to the communication users' signal-interference-plus-noise-ratio (SINR) constraints. Despite the non-convexity of the waveform design problem, we obtain its globally optimal solution via semi-definite relaxation (SDR) and the proposed constructive method. Simulation results validate the proposed DoF completion technology could achieve the derived MSE lower bound and the effectiveness of the MSE-based ISAC waveform design.

Semantic communication (SemCom) has emerged as a promising technology to improve the spectrum efficiency of next-generation wireless networks, by extracting meaningful content from the data and transmitting relevant semantic information only. However, the existing research usually overlooks the limited computing and storage resources on the mobile devices, which may make it unaffordable to implement resource-demanding deep learning (DL)-based semantic encoders/decoders. Moreover, besides the end-to-end SemCom framework, cooperative SemCom has not been well studied in the existing works, which can further enhance the communication performance. To address these issues, we propose a new architecture in this article, called semantic relay (SemRelay), which acts as an edge server to provide DL-enabled SemCom (DeepSC) services for two categories of edge users, called semantic users (SemUsers) with rich computing resources and conventional users (ConUsers) with limited resources. Two new transmission protocols are proposed for enabling text transmissions from the base station to the SemUsers and ConUsers, respectively, via the SemRelay (edge server). Moreover, an optimization problem is formulated to jointly design the SemRelay transmit power allocation and system bandwidth allocation to maximize the weighted sum-rate of all the users. Although this problem is nonconvex and hence difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution by applying the block coordinate descent and successive convex approximation techniques. Finally, the numerical results demonstrate the effectiveness of our proposed algorithm and the superior performance of the proposed SemRelay as compared to the traditional decode-and-forward relays, especially in the small bandwidth regime.

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, physical-layer system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S&C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution physical-layer ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.

The formation control of multi-agent systems has increasingly drawn attention for fulfilling numerous emerging applications and services. To achieve high-accuracy formation, the location awareness of all agents becomes an essential requirement. In this paper, we address the problem of network localization and formation control in a cooperative system with asynchronous agents. In particular, we formulate the joint localization and synchronization of agents as a statistical inference problem. The underlying probabilistic model is represented by a factor graph from which a message-passing algorithm is designed that computes approximations of the marginals of unknown variables, i.e. agents’ locations and clock offsets. Due to the Euclidean-norm operator involved in their computation no parametric closed-form expressions of the messages exist. As a compromise, implemented message-passing methods therefore resort to approximations of these messages. Conventional methods rely either on a first-order Taylor expansion of the norm operation or on non-parametric representations, e.g. by means particle filters (PFs), to compute such approximations. However, the former approach suffers from poor performance while the latter one experiences high complexity. The proposed message-passing algorithm in this paper is parametric. Specifically, it passes Gaussian messages that can be essentially obtained by suitably augmenting the factor graph and applying on it a hybrid method for combining belief propagation and variational message passing. Subsequently, the agents can exploit the estimated locations for determining the control policy. Two types of control policy are designed based on the optimization of a generalized cost function. We show that the proposed scheme enjoys a reduced complexity for multi-agent localization while achieving the desired formation with excellent accuracy.

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramér-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.

As a typical label-limited task, it is significant and valuable to explore networks that enable to utilize labeled and unlabeled samples simultaneously for synthetic aperture radar (SAR) image scene classification. Graph convolutional network (GCN) is a powerful semisupervised learning paradigm that helps to capture the topological relationships of scenes in SAR images. While the performance is not satisfactory when existing GCNs are directly used for SAR image scene classification with limited labels, because few methods to characterize the nodes and edges for SAR images. To tackle these issues, we propose a contrastive learning-based dual dynamic GCN (DDGCN) for SAR image scene classification. Specifically, we design a novel contrastive loss to capture the structures of views and scenes, and develop a clustering-based contrastive self-supervised learning model for mapping SAR images from pixel space to high-level embedding space, which facilitates the subsequent node representation and message passing in GCNs. Afterward, we propose a multiple features and parameter sharing dual network framework called DDGCN. One network is a dynamic GCN to keep the local consistency and nonlocal dependency of the same scene with the help of a node attention module and a dynamic correlation matrix learning algorithm. The other is a multiscale and multidirectional fully connected network (FCN) to enlarge the discrepancies between different scenes. Finally, the features obtained by the two branches are fused for classification. A series of experiments on synthetic and real SAR images demonstrate that the proposed method achieves consistently better classification performance than the existing methods.

In this paper, we consider the energy-efficient waveform design for integrated sensing and communications systems, simultaneously performing multi-user communications and point-like/extended target sensing. We propose a performance metric to measure sensing-centric energy efficiency (EE) for the first time, namely sensing-centric EE. We formulate a problem to optimize sensing-centric EE with power budget, signal-to-interference-and-noise ratio (SINR) constraints for communication and a Cramér-Rao bound (CRB) constraint for sensing. For the point-like target case, we give the first-order approximations for the non-convex formulations and develop an effective iterative algorithm to handle the nonconvexity. For the extended target case, we show that the considered problem can be relaxed into semidefinite programming and the optimum can be reconstructed. Simulation results demonstrate significant performance gains on sensing-centric EE over the benchmarks.

To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for largescale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.

In this paper, we propose an efficient channel estimation algorithm for orthogonal time frequency space (OTFS) systems in the presence of fractional Doppler. The proposed algorithm first employs the well-known threshold-based estimator to obtain the effective channel response. With the effective channel matrix in hand, we then utilize the linear system to recover the Doppler shifts and channel gains of different resolvable paths. The interference between different paths is also considered. Our simulation results verify that, by selecting appropriate samples in the effective channel matrix, the Doppler shifts and channel gains can be estimated robustly even in poor signal-to-noise ratio (SNR) conditions.

Integrated sensing and communication (ISAC) has received increasing attention as a potential technology to alleviate spectrum shortage. This paper studies the power-saving problem based on power assignment and subcarrier allocation in ISAC. Specifically, we propose a joint design method to jointly optimize the subcarrier and transmit power allocated to sensing services and communication services respectively. To ensure the quality of sensing service (SS) and communication service (CS), the minimum total power required by the system is obtained by optimizing the allocation of subcarriers and transmission power. Since the formulated problem is nonconvex, which is generally difficult to solve effectively. Inspired by the classical subcarrier allocation algorithm based on channel gain information, we decompose the formulated problem into two convex optimization subproblems that are easy to solve. Numerical simulation results show that the proposed algorithm can effectively improve the power-saving performance of the system compared with the existing algorithms.

The digital twin (DT), which effectively represents the actual real-world physical system or process, has reshaped the classic manufacturing, construction, as well as healthcare industry. As for realizing DT, both sensing and communication functionalities are demanded, which fully builds the connectivity between the physical world and the digital world. We first conducted a survey on the current situation of DT combined with communication and sensing. Inspired from this survey and the current development of communication and sensing, in this paper, we attempt to study the communication annd sensing technologies of physical layer in DT, to reduce the hardware and spectrum overhead. First, we studied the degree of freedom (DoF) problem in general communication and sensing system, and contribute to the DoF definition in the sensing system. Then, in order to improve the spectrum efficiency in DT system, we proposed an iterative optimization framework to address the coexistence of communication and sensing, and some examples are provided. Finally, in order to pursue a better integration gain, we proposed a new waveform design method based on DoF completion. The proposed optimization method can achieve the mean square error (MSE) lower bound. Simulation results demonstrate the effectiveness of various problems in the above scenarios.

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.

Integrated Sensing and Communication (ISAC) is recognized as a promising technology for the next-generation wireless networks, which provides significant performance gains over individual sensing and communications (S&C) systems via the shared use of wireless resources. The characterization of the S&C performance tradeoff is at the core of the theoretical foundation of ISAC. In this paper, we consider a point-to-point (P2P) ISAC model under vector Gaussian channels, and propose to use the Cramér-Rao bound (CRB)-rate region as a basic tool for depicting the fundamental S&C tradeoff. In particular, we consider the scenario where a unified ISAC waveform is emitted from a dual-functional ISAC transmitter (Tx), which simultaneously communicates information to a communication receiver (Rx) and senses targets with the help of a sensing Rx. In order to perform both S&C tasks, the ISAC waveform is required to be random to convey communication information, with realizations being perfectly known at both the ISAC Tx and the sensing Rx as a reference sensing signal as in typical radar systems. In this context, we treat the ISAC waveform as a random but known nuisance parameter in the sensing signal model, and define a Miller-Chang type CRB for the analysis of the sensing performance. As the main contribution of this paper, we characterize the S&C performance at the two corner points of the CRB-rate region, namely,$P_{\mathrm{ SC}}$indicating the maximum achievable communication rate constrained by the minimum CRB, and$P_{\mathrm{ CS}}$indicating the minimum achievable CRB constrained by the maximum communication rate. In particular, we derive the high-SNR communication capacity at$P_{\mathrm{ SC}}$, and provide lower and upper bounds for the sensing CRB at$P_{\mathrm{ CS}}$. We show that these two points can be achieved by the conventional Gaussian signalling and a novel strategy relying on the uniform distribution over the set of semi-unitary matrices, i.e., the Stiefel manifold, respectively. Based on the above-mentioned analysis, we provide an outer bound and various inner bounds for the achievable CRB-rate regions. Our main results reveal a two-fold tradeoff in ISAC systems, consisting of the subspace tradeoff (ST) and the deterministic-random tradeoff (DRT) that depend on the resource allocation and data modulation schemes employed for S&C, respectively. Within this framework, we examine the state-of-the-art ISAC signalling strategies and study a number of illustrative examples, which are validated through numerical simulations.

In the upcoming next-generation (5G-Advanced and 6G) wireless networks, sensing as a service will play a more important role than ever before. Recently, the concept of perceptive network is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. This type of technology is typically referred to as Integrated Sensing and Communications (ISAC). In this paper, we propose the concept of sensing quality of service (QoS) in terms of diverse applications. Specifically, the probability of detection, the Crámer-Rao bound (CRB) for parameter estimation and the posterior CRB for moving target indication are employed to measure the sensing QoS for detection, localization, and tracking, respectively. Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services. The proposed schemes can flexibly allocate the limited power and bandwidth resources according to both S&C QoSs. Finally, we study the performance trade-off between S&C services in different resource allocation schemes by numerical simulations.

Integrated Sensing and Communication (ISAC) is recognized as a promising technology for the next-generation wireless networks. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communications (S&C), where a unified ISAC waveform is exploited to perform dual-functional tasks. In particular, we define the Cramér-Rao bound (CRB)-rate region to characterize the S&C tradeoff, and propose a pentagon inner bound of the region. We show that the two corner points of the CRB-rate region can be achieved by the conventional Gaussian waveform and a novel strategy corresponding to the uniform distribution over the Stiefel manifold, respectively. Moreover, we also offer our insights into transmission approaches achieving the boundary of the CRB-rate region, namely the Shannon-Fisher information flow.

Nowadays, a possible approach to designing a commercial-attractive sensing solution is integrating sensing capability into widely deployed communication systems, e.g., the force coming fifth-generation (5G) new radio (NR), by slightly modifying the standard. To this end, in this paper, we firstly investigate the possibility of re-using the NR waveform for sensing by reviewing current NR frame structure. Then, the self-ambiguity and cross-ambiguity functions are analyzed to exploit the NR waveform performance limitations. Several synchronizations and reference NR signal structures are considered for both downlink and uplink NR transmissions. Finally, the simulated NR frame and its self- and cross-ambiguity simulation results demonstrate the performance limitations.

This paper investigates the problem of fairness in Dual-Function radar-communications system (DFRC) which exploits orthogonal frequency division multiplexing (OFDM) waveforms for performing radar and communication operations simultaneously. A novel iterative algorithm are proposed to maximize the fairness among different communication users (CUs) with the constraint on radar user’s (RU) perfermance. In our considered system, the optimization problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. In order to make this thorny problem easy to deal with, we propose an approximate algorithm based on BSUM theory to obtain the feasible solution of the original problem in polynomial time. It is shown, using simulation results, that the proposed optimization strategy outperform other strategies in term of fairness.

In this paper, we consider an energy efficient waveform design in integrated sensing and communication (ISAC) systems. The transmitted waveform simultaneously serves multiple communication users and estimates the parameters of a moving target. In order to improve its energy efficiency (EE) while guaranteeing target estimation performance, we maximize the EE of the emitted dual-use waveform, under a Cramér-Rao bound (CRB) constraint. However, the considered optimization problem is a fractional function that is highly non-convex. Thus, we firstly adopt fractional programming based on Dinkelbach’ method and then, solve the sub-problem by leveraging semi-definite relaxation (SDR). Numerical results demonstrate superior performance than the benchmark and show the trade-off between EE and CRB.

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks.

Scatter targets of complex land covers in polarimetric synthetic aperture radar (PolSAR) images are often randomly oriented and cause randomly fluctuating echoes, which brings a challenge to PolSAR image classification. Therefore, many existing methods have alleviated this problem through orientation compensation. However, there are still two obstacles that limit the improvement of classification accuracy. On the one hand, generally, these methods process PolSAR images with fixed polarization rotation angles, which is experience-dependent and inflexible. On the other hand, for the different land covers of a PolSAR image, the existing methods do not consider these rotation angles separately. For the first obstacle, we design a group of convolution kernels called polarization rotation kernels (PRKs) and utilize them to build the polarimetric convolutional neural network (CNN) (PolCNN). The PolCNN is the base network of our final model, and it can learn polarization rotation angles adaptively. For the second obstacle, we extend the PolCNN into a multipath structure, the final model polarimetric multipath CNN (PolMPCNN). The polarization rotation angles of different land covers are directly related to the networks of different paths within the PolMPCNN. Furthermore, we also put forward the two-scale sampling and the stagewise training algorithm in order that our PolMPCNN can fit different scales of PolSAR targets and pays more attention to difficult training samples. Experiments on real PolSAR images show that the proposed model achieves the best classification results with an extremely low sampling rate of 0.1%.

Data-based image classification methods, such as convolutional neural networks (CNNs), have achieved state-of-the-art performance. They usually leverage thousands of labeled samples to train the networks but ignore some prior knowledge. However, labeled samples are difficult to be obtained for synthetic aperture radar (SAR) images. Model-based methods are adept at utilizing the prior information of data, while they have to introduce some restrictions or assumptions during the realization of models. Consequently, to develop the advantages of both methods and improve their disadvantages, we propose a hybrid network by coupling the data-based with model-based methods for SAR image scene classification in this article. First, to fully use the prior information of SAR images and large amounts of unlabeled samples, we improve the$G^{0}$-based variational Bayesian inference model (GVBI) and construct a$G^{0}$-based convolutional variational auto-encoder (GCVAE) for unsupervised learning of the distributional characteristics of SAR images. After that, we further extend the GCVAE by combining it with CNN, resulting in a stronger hybrid network to classify SAR images with a few labeled samples. In addition, considering the abundant structural information is crucial for SAR image classification, we design a sketch fitter and two structural constraints on both pixel and sketch spaces to assist the hybrid network to improve its classification performance. Finally, we evaluate the performance of our method on real-SAR images, and the experimental results demonstrate that the proposed framework outperforms related methods on classification while reducing the manual annotation substantially.

To improve detection performance, this paper proposes a non-cooperative unmanned aerial vehicle (UAV) detection strategy based on multichannel detection of remote signal with energy detector (ED). More specifically, the sampling point of remote signal on each subchannel adaptively varies with environmental signal-to-noise (SNR) within constrained scope. The decision on the presence or absence of remote signal is made at the fusion center (FC) based on the majority voting rule. Both theoretical derivation and simulation experiments validate the effectiveness of the proposed scheme.

The dual-function radar communication system develops rapidly with the integration of sensing function and communication function, the combination of vehicle tracking and positioning and vehicle communication leads to a more efficient vehicle networking system in the future. This paper proposes a beam tracking prediction scheme for intergraded sensing and communications (ISAC) aided vehicle to infrastructure communications. In detail, we focus on the beam misalignment problem between roadside units (RSU) and high dynamic passing vehicles. To solve this problem, we propose a particle filter-based predictive beamforming method that can predict the motion parameters of vehicles by using transmitted ISAC signals and received the vehicle echoes. The simulation results show that the proposed particle filter algorithm can reduce the overhead and predict the vehicle motion parameters and the vehicle's angle relative to the RSU when the vehicle is moving.

Wireless sensing can enable human identification by quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric system, WirelessID, that explores the human fine-grained behavior and body physical signatures embedded in channel state information by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to identification performance. Thus, to extract robust features, we introduce an attention mechanism into our system. Particularly, commercial Wi-Fi devices are used for prototyping WirelessID in a laboratory with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

With powerful feature representations, convolutional neural networks (CNNs) have produced tremendous achievements in image classification tasks and, typically, entail millions of labeled samples to train massive parameters. However, the sample labeling of synthetic aperture radar (SAR) images is extremely difficult, especially pixelwise labels, and has, sometimes, required field trips to accomplish labeling. Moreover, the inherent speckle noise may weaken the ability of networks to extract effective features from SAR images. In this article, we address these issues by labeling a few patchwise samples and propose Ridgelet-Nets with speckle reduction regularization for SAR image scene classification by combining deep learning with multiscale geometric analysis and statistical modeling of SAR images. First, we design Ridgelet-Nets with convolutional kernels constructed by ridgelet filters to reduce the training parameters and learn more discriminative features. Then, we embed speckle reduction regularization in the Ridgelet-Nets to restrain the influence of speckle noise and smooth the classification maps, in which the prior information of SAR image statistical modeling is introduced. Finally, we propose an adaptive SAR image scene classification framework based on an extended hierarchical visual semantic model, considering the differences in the structures and spatial relationships of different regions in the SAR images, particularly large-scale and complex scenes. Experimental results on real SAR images demonstrate that the proposed framework can achieve preferable classification performance using very limited labeled samples.