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.

Wi-Fi device-free tracking enables the inference of user behaviors without physical contact, which is crucial for intelligent indoor location-based services. Nevertheless, the practical implementation of current tracking systems is constrained by several critical limitations: 1) The low-quality sensing signals in complex scenarios lead to increased tracking errors; 2) Existing methods inadequately adjust to dynamic environments, necessitating additional data collection or retraining processes. To address these challenges, this paper introduces EOC-Tracking, a device-free Wi-Fi tracking system that dynamically incorporates environmental information. Our key innovation involves leveraging obstacles to correct illogical users' trajectories and facilitate adjustment to varying environments. This significantly improves the accuracy of the follow-up in complex and changing environments. The EOC-Tracking system is built upon three fundamental design principles: 1) A lightweight dual-branch neural network architecture that effectively fuses environmental data with Wi-Fi signal characteristics; 2) An autonomous map updating mechanism that facilitates real-time adaptation to environmental layout modifications without human intervention; 3) A sophisticated data-driven, phased training paradigm that optimizes the model's ability to learn and apply obstacle constraints. We implement EOC-Tracking using commercial Wi-Fi devices and deploy it on low-power embedded systems such as the MCU. Experimental results demonstrate that EOC-Tracking can reduce tracking errors by at most 49.48% compared to datadriven methods and 62.21% compared to model-based methods in various complex scenarios.

This paper identifies the importance of the safe use of fonts in web and document security. We find multiple attack surfaces that can be exploited by an adversary using malicious fonts. We conduct a comprehensive evaluation of Portable Document Format (PDF) documents collected from the real world to investigate how an attacker can bypass PDF signatures. We further evaluate the potential security threats that an attacker can bring to web-based emails. Our study shows that various security issues may be caused by the inappropriate use of fonts, which are nonethelessly overlooked in the past years. As such, guidelines promoting the secure use of fonts could be beneficial in reinforcing the security measures for digital documents and web pages.

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully- and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze both the sensing signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB) for estimating the target’s direction-of-arrival (DoA) with joint transmit and reflective beamforming optimization. First, we characterize the maximum sensing SNR when the BS-IRS channel follows line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elementsNequipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally toN2andN4for the semi- and fully-passive IRSs, respectively. Then, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. It is shown that whenNgrows, the minimum CRB decreases inversely proportionally toN4andN6for the semi- and fully-passive IRS, respectively. Finally, numerical results are presented to corroborate our analysis across general channel conditions. It is shown that the fully-passive IRS outperforms the semi-passive counterpart whenNexceeds a certain threshold due to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss.

Along with AIGC shines in CV and NLP, its potential in the wireless domain has also emerged in recent years. Yet, existing RF-oriented generative solutions are ill-suited for generating high-quality, time-series RF data due to limited representation capabilities. In this work, inspired by the stellar achievements of the diffusion model in CV and NLP, we adapt it to the RF domain and propose RF-Diffusion. To accommodate the unique characteristics of RF signals, we first introduce a novel Time-Frequency Diffusion theory to enhance the original diffusion model, enabling it to tap into the information within the time, frequency, and complex-valued domains of RF signals. On this basis, we propose a Hierarchical Diffusion Transformer to translate the theory into a practical generative DNN through elaborated design spanning network architecture, functional block, and complex-valued operator, making RF-Diffusion a versatile solution to generate diverse, high-quality, and time-series RF data. Performance comparison with three prevalent generative models demonstrates the RF-Diffusion's superior performance in synthesizing Wi-Fi and FMCW signals. We also showcase the versatility of RF-Diffusion in boosting Wi-Fi sensing systems and performing channel estimation in 5G networks.

Depth estimation is crucial for numerous applications, including autonomous driving, robotic navigation and aug-mented reality. Existing solutions based on LiDAR and mm Wave technologies are constrained by high deployment costs, while those utilizing monocular vision suffer from limited accuracy. To address these challenges, this paper proposes WiViD, a diffusion-based depth estimation system that leverages commercial Wi-Fi and vision. Diffusion models, with their ability to iteratively refine predictions, offer significant advantages in producing accurate and detailed estimations. We introduce a Multimodal Conditional Diffusion (MMCD) mechanism and design two encoding modules: the Complex-Valued CSI Encoder (CCE) and the Residual Image Encoder (RIE). These components fully exploit the spatio-temporal information inherent in Wi-Fi CSI and enable the effective fusion of Wi-Fi CSI and RGB image data, which results in high-precision and robust depth estimation. Experimental results in real-world scenarios demonstrate that WiViD out-performs state-of-the-art (SOTA) monocular methods, reducing the Absolute Relative Error (ARE) by 67.2 %, highlighting the advantages of WiViD in terms of accuracy and reliability.

The channel state information (CSI) has been extensively studied in the literature to facilitate authentication in wireless networks. The less focused is a systematic attack model to evaluate CSI-based authentication. Existing studies generally adopt either a random attack model that existing designs are resilient to or a specific-knowledge model that assumes certain inside knowledge for the attacker. This paper proposes a new, realistic attack model against CSI-based authentication. In this model, an attacker Eve tries to actively guess a user Alice’s CSI, and precode her signals to impersonate Alice to the verifier Bob who uses CSI to authenticate users. To make the CSI guessing effective and low-cost, we use theoretical analysis and CSI dataset validation to show that there is no need to guess CSI values in all signal propagation paths. Specifically, Eve can adopt a Dominant Path Construction (DomPathCon) strategy that only focuses on guessing the CSI values on the first few paths with the highest channel response amplitude (called dominant paths). Comprehensive experimental results show that DomPathCon is effective and achieves up to 61% attack success rates under different wireless network settings, which exposes new limitations of CSI-based authentication. We also propose designs to mitigate the adverse impact of DomPathCon.

Future wireless systems are supposed to provide high-resolution sensing services via communication signals. Under this background, multiband sensing has recently become a promising technology due to that it can improve the sensing performance by jointly utilizing multiple non-contiguous frequency bands at a low cost. However, few studies have investigated the fundamental limits of multiband sensing, especially in the presence of phase distortion factors. In this paper, we investigate the fundamental limits of multiband sensing in terms of time delay. We derive a closed-form expression of the Cramér-Ran bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called the statistical resolution limit (SRL) is employed to investigate the fundamental limits of delay resolution. The fundamental limits of delay estimation error are also investigated based on the CRB and Ziv-Zakai bound (ZZB). Based on the above derived fundamental limits, numerical results are presented to provide key insights on the performance limits of the multiband sensing.

Multiband fusion enhances delay estimation by jointly utilizing signals from multiple noncontiguous frequency bands. However, in the multiband signal model, there are many local optimums in the associated likelihood function due to the existence of high-frequency component and phase distortion factors, posing challenges for high-accuracy parameter estimation. To address this, we propose a two-stage scheme equipped with different signal models derived from the original model, where the first-stage coarse estimation is performed using a weighted root MUSIC algorithm to narrow down the search range for the subsequent stage, and the second-stage refined estimation utilizes a Bayesian approach to avoid convergence to bad suboptimal solutions. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike conventional particle-based VBI (PVBI) that optimizes only particle probability and incurs exponential per-iteration complexity with particle count, our more flexible SPVBI algorithm optimizes both the position and probability of each particle. Additionally, it utilizes block SSCA to significantly improve sampling efficiency by averaging over iterations, making it suitable for high-dimensional problems. Extensive simulations demonstrate the superiority of our proposed algorithm over various baseline methods.

Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition. This is because of the large experimental data sets and the black-box nature of deep neural networks. This article presents SDP3, a Simulation-Driven Performance Predictor and oPtimizer, which consists of SDP3 data simulator, SDP3 performance predictor and SDP3 performance optimizer. Specifically, the SDP3 data simulator generates vivid wireless sensing data sets in a virtual environment, the SDP3 performance predictor predicts the sensing performance based on the curve fitting method, and the SDP3 performance optimizer investigates the sensing and communication performance tradeoff analytically. It is shown that the simulated sensing data set matches the experimental data set very well in the motion recognition accuracy. By leveraging SDP3, it is found that the achievable region of recognition accuracy and communication throughput consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the desired balanced performance for ISAC systems lies in the third one.

Multiband technology has recently received incremental attention for its ability to jointly utilize multiple noncontiguous frequency bands to achieve high-resolution delay estimation. In multiband scenarios, numerous signal processing algorithms for delay estimation have been proposed, while research on the fundamental limits remains under explored. In this article, we focus on the analysis of fundamental limits and the optimization of multiband delay estimation in orthogonal frequency division multiplexing (OFDM) systems. We derive a closed-form expression of the Cramer-Rao bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called statistical resolution limit (SRL) that provides a resolution performance bound is employed to research the fundamental limits of delay resolution. The fundamental limits of the delay estimation error are also investigated using the performance bounds CRB and Ziv-Zakai bound (ZZB). Based on these derived performance bounds, numerical results have been presented to analyse the effect of frequency band apertures and phase distortions on the fundamental limits of the multiband delay estimation error. Inspired by the analysis of fundamental limits, we formulate an optimization problem to find the optimal system configuration in multiband systems with the objective of minimizing the delay SRL. To solve this nonconvex constrained problem, we propose an efficient alternating optimization (AO)-based algorithm that iteratively optimizes the variables using the successive convex approximation (SCA) and 1-D search. Simulation results demonstrate the effectiveness of the proposed algorithm and give useful insights for the multiband system design.

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in dense urban areas with a high success rate. Furthermore, we find that this vulnerability can also lead to severe consequences that hurt user privacy, including the leakage of sensitive information like precise locations, daily activities, and demographics. Ultimately, we discuss the potential countermeasures that may be used to mitigate this vulnerability and location spoofing attack.

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate one sensing target and communicate with a multi-antenna communication user (CU) simultaneously. We consider two sensing target models, namely the point and extended targets, respectively. For the point target case, the BS estimates the target angle and the reflection coefficient as unknown parameters, and we adopt the Cramér-Rao bound (CRB) for angle estimation as the sensing performance metric. For the extended target case, the BS estimates the complete target response matrix, and we consider three different sensing performance metrics including the trace, the maximum eigenvalue, and the determinant of the CRB matrix for target response matrix estimation. For each of the four scenarios with different CRB measures, we investigate the fundamental tradeoff between the estimation CRB for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. In particular, we formulate a new MIMO rate maximization problem for each scenario, by optimizing the transmit covariance matrix at the BS, subject to a different form of maximum CRB constraint and its maximum transmit power constraint. For these problems, we obtain the optimal transmit covariance solutions in semi-closed forms by using advanced convex optimization techniques. For the point target case, the optimal solution is obtained by diagonalizing acomposite channel matrixvia singular value decomposition (SVD) together with water-filling-like power allocation over these decomposed subchannels. For the three scenarios in the extended target case, the optimal solutions are obtained by diagonalizing thecommunication channelvia SVD, together with proper power allocation over two orthogonal sets of subchannels, one for both communication and sensing, and the other for dedicated sensing only. Finally, numerical results show the C-R region achieved by the optimal design in each scenario, which significantly outperforms that by other benchmark schemes such as time switching.

Recently, channel state information (CSI) extracted from Wi-Fi signals has enabled a variety of Wi-Fi sensing applications beyond communications. However, the extraction of CSI relies on specific Wi-Fi devices, which severely limits the CSI-based sensing applications. In this paper, we exploit the beamforming matrix, a compressed version of CSI that is fed back from the beamformee to the beamformer without encryption, for Wi-Fi sensing. In order to recover more channel information from the beamforming matrix for sensing, a QR decomposition (QRD)-based beamforming feedback scheme is proposed in this paper. We analyze the effectiveness of the proposed QRD-based scheme in terms of sensing and communication performance. Simulation results show that the Doppler frequency shift, time of flight, angle of departure, as well as the amplitude information can be recovered using the proposed scheme and at the same time, the spectral efficiency of the proposed QRD-based scheme is comparable to that of the existing SVD-based scheme. We implement the proposed scheme on IEEE 802.11ac/ax based Wi-Fi devices. The experimental results on human respiration and finger tracking demonstrate that the proposed scheme works well for practical Wi-Fi sensing.

Integrated sensing and communication (ISAC) is considered one of the key technologies for next-generation wireless communication. To achieve satisfactory communication and sensing performance simultaneously, it is necessary to maximize compatibility with the existing communication waveform. In this paper, we mainly investigate the ISAC constellation design based on communication waveforms (random waveforms). Firstly, we derive the modulated waveform with constant modulus has a smaller side lobe of periodic auto-correlation function (PACF), i.e., the modulated waveform with constant modulus is more suitable for sensing. To improve the sensing performance of the modulated waveform with non-constant modulus, we propose a ISAC constellation design method based on PCS, which designs the power spectrum of the modulated waveform by adjusting the probabilities of constellation points, thereby reducing the side lobe of PACF. In addition, we design a joint optimization problem between the weighted variance of normalized energy and the communication information entropy (CIE) to obtain the tradeoff between communication and sensing. Finally, we simulate the communication performance and sensing performance of the reshaped waveform, and obtain some interesting conclusions. The simulation results show that, for the modulated waveform with non-constant modulus, the proposed method can reduce the side lobe of PACF, and increase the probability of detection (Pd), along with a minimal loss for CIE and a tiny growth for bit error rate (BER). This work is helpful for the theoretical exploration and system design for ISAC.

Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communication in OFDM systems, where a unified ISAC waveform is exploited to perform both tasks. In particular, we define the Capacity-Bayesian Cramer Rao Bound (BCRB) region in the asymptotical case when the number of subcarriers is large. Specifically, we show that the asymptotical optimal input distribution that achieves the Pareto boundary point of the Capacity-BCRB region is Gaussian and the entire Pareto boundary can be obtained by solving a convex power allocation problem. Moreover, we characterize the structure of the sensing-optimal power allocation in the asymptotical case. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights.

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communication (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one extended target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assist the communication from the BS to the CUs, but also enable the BS's NLoS target sensing based on the echo signals from the BS-IRS-target-IRS-BS link. To provide full degrees of freedom for sensing, we suppose that the BS sends additional dedicated sensing signals combined with the information signals. Accordingly, we consider two types of CU receivers, namely Type-I and Type-II receivers, which do not have and have the capability of cancelling the interference from the sensing signals, respectively. Under this setup, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the IRS to minimize the Cramer-Rao bound (CRB) for estimating the target response matrix with respect to the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs and the maximum transmit power constraint at the BS. We present efficient algorithms to solve the highly non-convex SINR-constrained CRB minimization problems, by using the techniques of alternating optimization and semi-definite relaxation. Numerical results show that the proposed design achieves lower estimation CRB than other benchmark schemes, and the sensing signal interference pre-cancellation is beneficial when the number of CUs is greater than one.

Integrated sensing and communications (ISAC) is recognized as a key enabling technology for future wireless networks. To shed light on the fundamental performance limits of ISAC systems, this paper studies the deterministic-random tradeoff between sensing and communications (S&C) from a rate-distortion perspective under vector Gaussian channels. We model the ISAC signal as a random matrix that carries information, whose realization is perfectly known to the sensing receiver, but is unknown to the communication receiver. We characterize the sensing mutual information conditioned on the random ISAC signal, and show that it provides a universal lower bound for distortion metrics of sensing. Furthermore, we prove that the distortion lower bound is minimized if the sample covariance matrix of the ISAC signal is deterministic. We then offer our understanding of the main results by interpreting wireless sensing as non-cooperative source-channel coding, and reveal the deterministic-random tradeoff of S&C for ISAC systems. Finally, we provide sufficient conditions for the achievability of the distortion bound by analyzing specific examples.

This paper introduces a cooperative sensing framework designed for integrated sensing and communication cellular networks. The framework comprises one base station (BS) functioning as the sensing transmitter, while several nearby BSs act as sensing receivers. The primary objective is to facilitate cooperative target localization by enabling each receiver to share specific information with a fusion center (FC) over a limited capacity backhaul link. To achieve this goal, we propose an advanced cooperative sensing design that enhances the communication process between the receivers and the FC. Each receiver independently estimates the time delay and the reflecting coefficient associated with the reflected path from the target. Subsequently, each receiver transmits the estimated values and the received signal samples centered around the estimated time delay to the FC. To efficiently quantize the signal samples, a Karhunen-Loève Transform coding scheme is employed. Furthermore, an optimization problem is formulated to allocate backhaul resources for quantizing different samples, improving target localization. Numerical results validate the effectiveness of our proposed advanced design and demonstrate its superiority over a baseline design, where only the locally estimated values are transmitted from each receiver to the FC.

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.

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in dense urban areas with a high success rate. Furthermore, we find that this vulnerability can also lead to severe consequences that hurt user privacy, including the leakage of sensitive information like precise locations, daily activities, and demographics. Ultimately, we discuss the potential countermeasures that may be used to mitigate this vulnerability and location spoofing attack.

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate an extended target and communicate with a multi-antenna communication user (CU) at the same time. We investigate the fundamental tradeoff between the estimation Cramér-Rao bound (CRB) for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. Towards this end, we formulate a new MIMO rate maximization problem by optimizing the transmit covariance matrix at the BS, subject to a new form of maximum CRB constraint together with a maximum transmit power constraint. We derive the optimal transmit covariance solution in a semi-closed form, by first implementing the singular-value decomposition (SVD) to diagonalize the communication channel and then properly allocating the transmit power over these subchannels for communication and other orthogonal subchannels (if any) for dedicated sensing. It is shown that the optimal transmit covariance is of full rank, which unifies the conventional rate maximization design with water-filling power allocation and the CRB minimization design with isotropic transmission. Numerical results are provided to validate the performance achieved by our proposed optimal design, in comparison with other benchmark schemes.

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.

With the emerging paradigm of Internet of Things, high-accuracy localization has been an ever-present key issue, and relative position information is getting growing attention in cooperative tasks. In this paper, we propose an efficient relative localization algorithm for three-dimensional anchor-free networks with limited communication range. Specifically, we first design the condition number-based geometry selection criteria. Then we develop a scheme to establish a well-conditioned reference coordinate system for the network. Furthermore, an iterative relative localization algorithm is proposed, in which the reliability of the agents is evaluated for dynamic virtual anchor extension. The numerical results validate that the performance gain of the proposed relative localization scheme over existing algorithms is significant.

This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS with a uniform linear array (ULA) is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing potential targets at areas with LoS links blocked. We consider that the BS transmits combined information and sensing signals for ISAC. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS and the reflective beamforming at the IRS, to maximize the IRS’s minimum beampattern gain towards the desired sensing angles, subject to the minimum signal-to-noise ratio (SNR) requirement at the CU and the maximum transmit power constraint at the BS. Although the formulated SNR-constrained beampattern gain maximization problem is non-convex and difficult to solve, we present an efficient algorithm to obtain a high-quality solution by using the techniques of alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed joint beamforming design achieves improved sensing performance while ensuring the communication requirement as compared to benchmarks without such joint optimization. It is also shown that the use of dedicated sensing beams is beneficial in enhancing the performance for IRS-assisted ISAC.

With the arrival of the Internet of Things era, IoT devices and the services built on them make our lives more convenient and also raise public concerns on their vulnerability to attacks. Recent literature advocates physical-layer solutions to help IoT devices detect attacks instead of using sophisticated cryptographic methods. However, there is still no satisfying solutions for IoT devices with a single antenna and sparse traffic. Thus, we introduce SecurePilot to fill this gap. SecurePilot is an unsupervised and plug-and-play solution which works without an attacker’s knowledge in advance. It leverages the strengths of two orthogonal physical-layer information, propagation signatures and device signatures embedded in pilot signals to enable effective attack detection. It could work on single-antenna IoT devices with sparse traffic and also work compatibly with communication protocols. The experimental results show that SecurePilot can successfully detect 99.6% of attacks, triggering false alarms on 3.1% of legitimate traffic in a typical office environment.

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.

Driving a gradual integration of the physical and digital worlds is perceived to become a reality in the 6G era, from vehicles to drones, from surveillance facilities in cities to agricultural tools in the countryside. Jointly motivated by recent advances in communication and signal processing, radio sensing functionality can be integrated into a 6G radio access network (RAN) in a low-cost and fast manner. That is, future networks have the ability to “see” the physical world through imaging and measuring the surrounding environment, which enables advanced location-aware services, ranging from the physical to application layers. In essence, a radio emission could simultaneously convey communication data from the transmitter to the receiver and deliver environmental information from the scattered echoes. Therefore, sensing and communication (S&C) functionalities are possible to be co-designed to utilize resources efficiently and to assist each other for mutual benefits. This type of research is typically referred to as integrated sensing and communication (ISAC).

This is Part II of the double-part Special Issue (SI) on Integrated Sensing and Communication (ISAC). This SI aims at bringing together contributions from both academia and industry to highlight the recent progress of ISAC, where sensing and communication (S$\$ $C) functionalities are jointly designed to utilize wireless/hardware resources efficiently and to assist each other for mutual benefits. The 32 accepted articles of this SI are arranged into six groups, namely, 1) Fundamental Performance Bounds and Optimization, 2) Time-Frequency Signal Processing, 3) Spatial Signal Processing, 4) Networking and Resource Allocation, 5) ISAC With Emerging Communications Technologies, and 6) ISAC Applications. We kindly refer readers to Part I of this SI for a comprehensive overview written by the Guest Editorial Team, which provides both a bird’s eye view and technical details regarding state-of-the-art ISAC innovations. The contributions made by the papers in Part II are summarized as follows, which correspond to paper groups 4), 5), and 6).

Radar sensing will be integrated into the 6G communication system to support various applications. In this integrated sensing and communication system, a radar target may also be a communication channel scatterer. In this case, the radar and communication channels exhibit certain joint burst sparsity. We propose a two-stage joint pilot optimization, target detection and channel estimation scheme to exploit such joint burst sparsity and pilot beamforming gain to enhance detection/estimation performance. In Stage 1, the base station (BS) sends downlink pilots (DP) for initial target search, and the user sends uplink pilots (UP) for channel estimation. Then the BS performs joint target detection and channel estimation. In Stage 2, the BS exploits the prior information obtained in Stage 1 to optimize the DP signal to further refine the performance. A Turbo Sparse Bayesian inference algorithm is proposed for joint target detection and channel estimation in both stages. The pilot optimization problem in Stage 2 is a semi-definite programming with rank-1 constraints. By replacing the rank-1 constraint with a tight and smooth approximation, we propose an efficient pilot optimization algorithm based on the majorization-minimization (MM) method. Simulations verify the advantages of the proposed scheme.

This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simultaneously perform downlink multiuser communication and radar target sensing. Under this setup, we minimize the radar sensing beampattern matching errors, subject to the communication users' minimum signal-to-interference-plus-noise ratio (SINR) requirements and the BS's transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the a-priori known dedicated radar signals, respectively. Under both Type-I and Type-II receivers, the nonconvex beampattern matching problems are globally optimally solved via applying the semidefinite relaxation (SDR) technique. It is shown that at the optimality, dedicated radar signals are not required with Type-I receivers under some specific conditions, while dedicated radar signals are always needed to enhance the performance with Type-II receivers. Numerical results show that by exploiting the capability of canceling the interference caused by the radar signals, the case with Type-II receivers results in better sensing performance in terms of beampattern matching error than that with Type-I receivers and other conventional designs.

This paper studies an intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system. In this system, a multi-antenna access point (AP) uses transmit beamforming to send both information and energy signals to a set of receivers each for information decoding (ID) or energy harvesting (EH), and a dedicatedly deployed IRS properly controls its reflecting phase shifts to form passive reflection beams for facilitating both ID and EH at receivers. Under this setup, we jointly optimize the (active) information and energy transmit beamforming at the AP together with the (passive) reflective beamforming at the IRS, to maximize the minimum power received at all EH receivers, subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at ID receivers, and the maximum transmit power constraint at the AP. Although the formulated SINR-constrained min-energy maximization problem is highly non-convex, we present an efficient algorithm to obtain a high-quality solution by using the techniques of alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed IRS-assisted SWIPT system with both information and energy signals achieves significant performance gains over benchmark schemes without IRS deployed and/or without dedicated energy signals used.

As the low frequency band has become more and more crowded, millimeter-wave (mmWave) has attracted significant attention. The IEEE has released the 802.11ad standard to satisfy the demand of ultra-high-speed communication. It adopts beamforming technology that can generate directional beams to compensate for high path loss. In the association beamforming training (A-BFT) phase of BF training, a station (STA) randomly selects an A-BFT slot to contend for training opportunity. Due to the limited number of A-BFT slots, the A-BFT phase suffers high probability of collisions in dense user scenarios, resulting in inefficient training performance. Based on the evaluation of the IEEE 802.11ad standard and 802.11ay draft in dense user scenarios of mmWave wireless networks, we propose an enhanced A-BFT beam training and random access mechanism, including the separated A-BFT (SA-BFT) and secondary backoff A-BFT (SBA-BFT). The SA-BFT can provide more A-BFT slots and divide the A-BFT slots into two regions by defining a new E-A-BFT Length field compared with the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also greatly reduce the collision probability in dense user scenarios. The SBA-BFT performs secondary backoff with very small overhead of transmission opportunities within one A-BFT slot, which not only further reduces collision probability, but also improves the A-BFT slots utilization. Furthermore, we propose a 3-D Markov model to analyze the performance of the SBA-BFT. The analytical and simulation results show that both the SA-BFT and the SBA-BFT can significantly improve BF training efficiency, which is beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.