Orthogonal frequency division multiplexing (OFDM) is one of the most widely adopted waveforms for integrated sensing and communication (ISAC) systems, owing to its high spectral efficiency and compatibility with modern communication standards. This paper investigates the sensing performance of OFDM-based ISAC for multi-target delay (range) estimation under specific radar receiver processing schemes. An estimation-theoretic framework is developed to characterize sensing performance with random communication payloads. We establish the fundamental limit of delay estimation accuracy by deriving the closed-form expression of the mean-square error (MSE) achieved using matched filtering (MF) and reciprocal filtering (RF) receivers. The results show that, in multi-target scenarios, the impact of signal constellations on the delay estimation MSE differs across receivers: MF performance depends on the fourth-order moment of the zero-mean, unit-power constellation in the presence of multiple targets, whereas RF performance depends on its inverse second-order moment, irrespective of the number of targets. Building on this analysis, we present a ISAC constellation design under specific receiver architecture that brings a receiver-dependent flexible trade-off between sensing and communication in OFDM-ISAC systems. The theoretical findings are validated through simulations and proof-of-concept experiments, and also the sensing and communication performance trade-off is experimentally shown with the proposed constellation design.

The emergence of non-contact biosensing technologies is transforming the Healthcare Internet-of-Things (IoT) by enabling continuous and unobtrusive monitoring of cardiovascular function, critical for early disease detection and personalized care. Millimeter-wave radar, known for its fine spatial resolution and motion sensitivity, offers a compelling platform for remote cardiac monitoring. However, practical deployment remains challenging due to susceptibility to motion artifacts, respiratory interference, and physiological variability. In this work, we present a novel radar-acoustic cardiac monitoring framework that addresses these challenges by exploiting the spectral separation between high-frequency heart sounds (20-80 Hz) and the lower-frequency thoracic movements (0.7-1.5 Hz). This natural spectral isolation facilitates the suppression of motion artifacts and respiratory interference, enhancing the robustness of cardiac signal extraction. Building on the presented insights, we develop an active motion cancellation technique to effectively estimate and compensate for band-limited multiplicative motion interference even under complex body motions. This method enables seamless signal extraction while preserving the integrity of heart sound signals. Simulations and experimental validations demonstrate significant improvements in heart rate estimation and pulse waveform recovery, even under realistic motion scenarios. This privacy-preserving, non-contact, and clothing-penetrating sensing framework presents a promising avenue for seamless integration of cardiac monitoring into IoT ecosystems, enabling real-time health analytics in free-living environments.

Integrated sensing and communications (ISAC), which are recognized as a key enabler for sixth generation (6G), have brought new opportunities for intelligent, sustainable, and connected wireless networks. Multiple-input–multiple-output (MIMO) transceiver technology lies at the core of this paradigm, providing the degrees of freedom required for simultaneous data transmission and accurate radar sensing. The tight integration of sensing and communication (S&C) introduces unique security vulnerabilities that extend beyond conventional physical-layer security (PLS). In particular, high-power transmissions directed at sensing targets may empower adversarial eavesdroppers, whereas passive interception of ISAC echoes can reveal sensitive information such as target locations and mobility patterns. This article presents an overview of recent advances in MIMO ISAC transceiver design, considering transmitter perspectives, receiver architectures, and full-duplex implementations. We examine MIMO transceiver designs under unique security threats specific to ISAC and highlight emerging countermeasures, including secure signaling design, interference exploitation, and transceiver optimization under adversarial conditions. Finally, we discuss challenges and research opportunities for developing secure ISAC systems in next-generation wireless networks.

The deployment of integrated sensing and communication (ISAC) in wireless networks brings along unprecedented vulnerabilities to authorized passive sensing, necessitating the development of secure sensing solutions. Unlike traditional wireless communication, where data security can be enhanced through data encryption, sensing security is more challenging to achieve. This is because sensing parameters are embedded within the target-reflected signal leaked to unauthorized passive radar sensing eavesdroppers (Eve), implying that they can silently extract sensory information without prior knowledge of the information data. To overcome this limitation, we propose a novel sensing-secure ISAC framework that ensures secure target detection and estimation for the legitimate system, while obfuscating unauthorized sensing without requiring any prior knowledge of Eve. Specifically, by introducing artificial imperfections into the ambiguity function (AF) of ISAC signals, we introduce artificial ghost targets into Eve’s range profile which increase its range estimation ambiguity. In contrast, the legitimate sensing receiver (Alice) can suppress these AF artifacts using mismatched filtering, albeit at the expense of signal-to-noise ratio (SNR) loss. Specifically, employing an OFDM signal, a structured subcarrier power allocation scheme is designed to shape the secure autocorrelation function (ACF), inserting periodic peaks to mislead Eve’s range estimation and degrade target detection performance. To quantify the sensing security level, we introduce peak sidelobe level (PSL) and integrated sidelobe level (ISL) as key performance metrics. Additionally, we analyze the three-way trade-offs between communication, legitimate sensing, and sensing security, highlighting the impact of the proposed sensing-secure ISAC signaling on system performance. Furthermore, we formulate a convex optimization problem to maximize ISAC performance while guaranteeing a certain sensing security level. Numerical results validate the effectiveness of the proposed sensing-secure ISAC signaling, demonstrating its ability to degrade Eve’s target estimation while preserving Alice’s performance.

The ultimate goal of enabling sensing through the cellular network is to obtain coordinated sensing of an unprecedented scale, through distributed integrated sensing and communication (D-ISAC). This, however, introduces challenges related to synchronization and demands new transmission methodologies. In this paper, we propose a transmit signal design framework for noncoherent D-ISAC systems, where multiple ISAC nodes cooperatively perform sensing and communication without requiring phase-level synchronization. The proposed framework employing orthogonal frequency division multiplexing (OFDM) jointly designs downlink coordinated multi-point (CoMP) communication and multi-input multi-output (MIMO) radar waveforms. This leverages both collocated and distributed MIMO radars to estimate angle-of-arrival (AOA) and time-of-flight (TOF) from all possible multi-static measurements for target localization. To this end, we use the target localization Cramér-Rao bound (CRB) as the sensing performance metric and the signal-to-interference-plus-noise ratio (SINR) as the communication performance metric. Then, an optimization problem is formulated to minimize the localization CRB while maintaining a minimum SINR requirement for each communication user. Particularly, we present three distinct transmit signal design approaches, including unconstrained, orthogonal, and beamforming designs, which reveal trade-offs between ISAC performance and computational complexity. Unlike single-node ISAC systems, the proposed D-ISAC designs involve per-subcarrier sensing signal optimization to enable accurate TOF estimation, which contributes to the target localization performance. Numerical simulations demonstrate the effectiveness of the proposed designs in achieving flexible ISAC trade-offs and efficient D-ISAC signal transmission.

Integrated sensing and communication (ISAC) networks strive to deliver both high-precision target localization and high-throughput data services across the entire coverage area. In this work, we examine the fundamental trade-off between sensing and communication from the perspective of base station (BS) deployment. Furthermore, we conceive a design that simultaneously maximizes the target localization coverage, while guaranteeing the desired communication performance. In contrast to existing schemes optimized for a single target, an effective network-level approach has to ensure consistent localization accuracy throughout the entire service area. While employing time-of-flight (ToF) based localization, we first analyze the deployment problem from a localization-performance coverage perspective, aiming for minimizing the area Cramér-Rao Lower Bound (A-CRLB) to ensure uniformly high positioning accuracy across the service area. We prove that for a fixed number of BSs, uniformly scaling the service area by a factor$\kappa $increases the optimal A-CRLB in proportion to$\kappa ^{2 \beta }$, where$\beta $is the BS-to-target pathloss exponent. Based on this, we derive an approximate scaling law that links the achievable A-CRLB across the area of interest to the dimensionality of the sensing area. We also show that cooperative BSs extend the coverage but yield marginal A-CRLB improvement as the dimensionality of the sensing area grows. By exploiting the invariance properties discovered with respect to the displacement, rotation, and symmetric projection deformation, we derive a deployment-invariant structure for conceiving a low-complexity framework for ISAC network deployment. We then formulate the joint sensing-communication optimization problem and present a Majorization-Minimization algorithm for designing high-quality deployment solutions. Extensive simulations demonstrate that our framework significantly enhances sensing coverage, while maintaining the desired communication throughput.

This work studies a cooperative architecture for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate the allocation of antennas-to-base stations (BSs) as a means to optimize antenna densities and explore the range between massive MIMO and cell-free typologies, and their effects on cooperative sensing and cooperative communication performance. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)-based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, to comprehensively assess their effects on ISAC network performance. In networks with multiple ISAC nodes following a Poisson point process, the Cramér-Rao lower bound (CRLB) for time of flight (TOF)-based methods decreases with the square of the logarithm of the number of nodes, for angle of arrival (AOA)-based methods with the logarithm, and for hybrid methods as a mix of both. In terms of communication performance, we derive a tractable expression for the communication data rate under various cooperative region sizes. The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.

We propose a cooperative integrated sensing and communication (ISAC) architecture that integrates coordinated multi-point (CoMP) communication with multi-static sensing. This study investigates how the allocation of a fixed total number of antennas among base stations (BSs) affects sensing and communication performance, and optimizes both the antenna topology and the number of BSs. To this end, we formulate an antenna topology optimization problem to balance the advantages of centralized and distributed antennas. Specifically, centralized antennas enhance beamforming and coherent processing in massive multiple-input and multiple-output (MIMO) systems, while distributed antennas improve spatial diversity and reduce access distances in cell-free setups. For sensing, we evaluate two localization methods, angle-of-arrival (AOA)-based and time-of-flight (TOF)-based localizations, to analyze how their scaling laws affect overall network performance. We analyze and demonstrate that the synchronization errors in our proposed architecture are negligible, thereby providing theoretical support for system implementation. In terms of communication, our results indicate that higher path loss exponents favour distributed configurations, while lower exponents benefit centralized setups. Simulations confirm that our cooperative scheme outperforms non-cooperative approaches, surpassing purely centralized or distributed strategies.

A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas in the massive multiple input multiple output (MIMO) fashion, which enhance beamforming and coherent processing, against those of geographically distributed antennas towards cell-free transmission, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)- based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks havingNISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follows a ln2Nscaling law (explicitly, the Cramér-Rao lower bound (CRLB) reduces with ln2N). The AOA-based methods follow a lnNscaling law, while the hybrid methods scale asaln2N+blnN, whereaandbrepresent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. Specifically, when converting AOA measurements to the target location, the localization error introduced during this conversion is inversely proportional to the distance between the BS and the target, leading to a more significant reduction in accuracy as the number of transceivers increases. In contrast, TOF-based localization avoids such distance dependent errors in the conversion process. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain. Simulations confirm that cooperative transmission and sensing in ISAC networks can effectively improve non-cooperative sensing and communication performance The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.

This paper presents a novel motion-tolerant heart sound (HS) detection approach using a millimeter-wave (mmWave) Frequency-Modulated Continuous Wave (FMCW) radar sensor. Recent works demonstrate, in ideal stationary scenarios, radar-based HS detection from chest surface vibrations due to sound pressure waves. However, similar to the known motion distortion problem for radar-based vital signs detection, we have observed that HS measurement is severely affected by other motion artifacts as the HS-induced skin vibration is extremely small (a few micrometers) compared to body motion. Therefore, we develop a phase-tracking-based motion cancellation technique to mitigate the effect of body motions and thus radar-based HS detection is feasible. Simulation and measurement results validate the motion-tolerant radar-based HS detection in the presence of random body motions (RBM).

This paper presents a signal processing method to detect human cough signals with a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. Tiny vibrations induced by coughing can be extracted by using the phase demodulation technique of the FMCW radar. A body motion artifact cancellation (BMAC) technique is exploited to suppress motion artifacts, which can easily overwhelm and distort the small vibrations. This allows measuring the vibration frequency of the cough signal even when large-scale body motions are spontaneously involved. Numerical simulations are conducted to evaluate detection probability and accuracy of the cough signal with the proposed method, including the analysis for effects of FMCW chirp nonlinearity. The proposed techniques are also verified through experiments with a 60-GHz FMCW radar.

Radar sensors are emerging as solutions for perceiving surroundings and estimating ego-motion in extreme weather conditions. Unfortunately, radar measurements are noisy and suffer from mutual interference, which degrades the performance of feature extraction and matching, triggering imprecise matching pairs, which are referred to as outliers. To tackle the effect of outliers on radar odometry,$a$novel outlier-robust method called ORORA is proposed, which is an abbreviation of Outlier-RObust RAdar odometry. To this end, a novel decoupling-based method is proposed, which consists of graduated non-convexity (GNC)-based rotation estimation and anisotropic component-wise translation estimation (A-COTE). Furthermore, our method leverages the anisotropic characteristics of radar measurements, each of whose uncertainty along the azimuthal direction is somewhat larger than that along the radial direction. As verified in the public dataset, it was demonstrated that our proposed method yields robust ego-motion estimation performance compared with other state-of-the-art methods. Our code is available at https://github.com/url-kaist/outlier-robust-radar-odometry.