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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, an...
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... of the sensing applications that can be enabled by PMN are illustrated in Fig. 1. They may be classified as several major areas, such as smart transportation, smart city, smart home, industrial IoT, environmental sensing, and sensingassisted communications. More specific examples of these applications are listed in Table III. Detailed discussions on some of these applications are available from [5]. ...
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... potential solution is to introduce the concept of antenna grouping and virtual subarrays [114], [140]. By dividing existing antennas into two or more groups/virtual subarrays, we can designate tasks of C&S and optimize the design across groups of antennas. There could be overlap between different groups of antennas, as shown in Fig. 10. Virtual subarray introduces beamforming capability. Using orthogonal signals across virtual subarrays, we can maintain the orthogonality desired by MIMO radar, in order to achieve a larger aperture of an equivalent virtual array. Using overlapped antennas across neighbouring virtual subarrays can increase the spatial degree of freedom ...
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... array structure as shown in Fig. 10 reminds us of the hybrid antenna array that has been widely studied in mmWave communications. Considering the benefits of antenna grouping for both C&S, using hybrid antenna arrays [59], [143] will be an attractive low-cost option. This is particularly true for mmWave systems where propagation loss is high and beamforming gain is ...
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... better to be removed from the signals sent to the sensing parameter estimator. In [57], the inner bounds of the impact of clutter on the performance of JCAS is evaluated. It is shown that clutter originating from motion objects can significantly degrade the inner bounds of the performance. There may be two ways of clutter suppression, as shown in Fig. 11: doing suppression after or before estimating the sensing parameters. The former does not introduce signal distortion for sensing parameter estimation, and the latter can reduce the unknown sensing parameters to be estimated. High-end military/domestic radar can simultaneously detect and track hundreds of objects, and the capability is ...
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... these techniques have higher complexity than classical channel estimation algorithms. Since the required sensing rate is typically in the order of milliseconds to seconds, such high computational complexity is affordable at BSs. Comparison of these techniques for sensing parameter estimation in PMNs is summarized in Table XVI and illustrated in Fig. 12 in terms of the overall performance and complexity. Details of the research are elaborated below, together with additional techniques for sensing in clustered channels and resolution of sensing ...
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... discussed in Section IV, there is typically no clock-level synchronization between a sensing receiver and the transmitter in PMNs, particularly in uplink sensing. In this case, there exist both timing and carrier frequency offsets in the received signals. The timing offset is illustrated in Fig. 13. Both of them, as shown in (6), are typically time-varying due to oscillator stability. In communications, timing offset can be absorbed into channel estimation and CFO can be estimated and compensated. Their residuals become sufficiently small and can be ignored. Differently, in sensing they cause measurement ambiguity and accuracy ...
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... on the various approaches developed for WiFi sensing, we can deduce the procedures of applying pattern analysis to mobile signals, as shown in Fig. 14. They typically involve four steps: signal collection, signal preprocessing, feature extraction, and recognition and classification. In the signal collection step, the signals are collected at the receiver according to the desired rate. In the signal preprocessing step, the collected signals may be stripped, cleaned and compressed. ...
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... for generating initial beamforming and updating it when either the BS or the UE moves, using downlink and uplink sensing [89]. In particular, the multibeam scheme in [29], [132] introduces protocols and algorithms to enable communication and sensing in different directions at the same time even with an analog antenna array, as shown in Fig. 15. This makes it possible for a JCAS transmitter or a receiver to scan the surrounding environment and update the propagation map, while maintaining the communications. The basic idea of the multibeam scheme is to generate beamforming with multiple sub-beams, consisting of fixed sub-beams primarily for communications and packet-varying ...
Citations
... Communication data can be embedded in waveforms by modulating the phase in PC-FMCW [89] and PMCW [15], or by coding the waveform in OFDM [90,91]. According to [92], JRC system designs can be broadly categorized into three types: ...
Commercial automotive radar systems for advanced driver assistance systems (ADASs) have relied on frequency-modulated continuous wave (FMCW) waveforms for years due to their low-cost hardware, simple signal processing, and established academic and industrial expertise. However, FMCW systems face challenges, including limited unambiguous velocity, restricted multiplexing of transmit signals, and susceptibility to interference. This work introduces a unified automotive radar
signal model and reviews alternative modulation schemes such as phase-coded frequency-modulated continuous wave (PC-FMCW), phase-modulated continuous wave (PMCW), orthogonal frequencydivision multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), and orthogonal time frequency space (OTFS). These schemes are assessed against key technological and economic criteria and compared with FMCW, highlighting their respective strengths and limitations.
... In this context, a more focused effort in 6G is directed towards joint communication and sensing (JCAS), where both systems can coexist using the same spectrum and, in some cases, the same hardware [2]. This evolution of wireless systems from communication-only networks to dual-functional networks finds applications in the Internet of Things (IoT), vehicle-to-vehicle (V2V), non-terrestrial networks (NTN), nomadic networks, and more [3]. ...
This paper investigates the use of intelligent reflecting surfaces (IRS) to assist cellular communications and radar sensing operations in a communications and sensing setup. The IRS dynamically allocates reflecting elements to simultaneously localize a target and assist a user's communication. To achieve this, we propose a novel optimization framework that jointly addresses beamforming design and IRS element allocation. Specifically, we formulate a Weighted Minimum Mean Square Error (WMMSE)-based approach that iteratively optimizes the transmit and receive beamforming vectors, IRS phase shifts, and element allocation. The allocation mechanism adaptively balances the number of IRS elements dedicated to communication and sensing subsystems by leveraging the signal-to-noise-plus-interference-ratio (SINR) between the two. The proposed solution ensures efficient resource utilization while maintaining performance trade-offs. Numerical results demonstrate significant improvements in both communication and sensing SINRs under varying system parameters.
... For this reason, integrated sensing and communication (ISAC) technology is seen as a promising approach that has received extensive attention from academia and industry [2,3]. Integrating communication and sensing functionalities bring performance gains in hardware, antenna, and spectral efciency [4]. Meanwhile, ultrareliable and low-latency communication (URLLC) is usually required to support timesensitive applications by enabling high reliability and low latency signal transmission [5]. ...
... , e jϕ M ) ∈ C M×M be RIS phase shift matrix, where ϕ m ∈ [0, 2π), m ∈ M represents the phase shift of RIS refecting element m. Te signal received at user k can be expressed as 4 International Journal of Intelligent Systems ...
Aiming to address the security and timeliness challenges in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with finite blocklength (FBL), this paper jointly investigates the communication security, sensing security, and information freshness performance of the system in the presence of communicating eavesdropper and sensing eavesdropper. Specifically, based on statistical channel state information (CSI), approximate closed-form expressions for secrecy throughput, average age of information (AoI), and channel parameter estimation errors are derived and analyzed to characterize the performance of communication security, information freshness, and sensing security. The asymptotic analyses between secrecy throughput and blocklength, number of antennas, and number of RIS reflecting elements are established. Furthermore, an optimization problem for maximizing sum secrecy throughput is established under the timeliness, sensing security, transmit power, and RIS unit modulus constraints. To handle the intractable stochastic nonconvex problem, a joint alternating optimization method based on noncooperative game and stochastic successive convex approximation (NCG-SSCA) is proposed by jointly designing RIS phase shift, transmit beamforming vector, sensing signal covariance, and blocklength. Simulation results validate our theoretical derivations and conclusions in the performance analysis. It is also shown that compared with SSCA and stochastic gradient descent (SGD) methods, the NCG-SSCA method proposed in this paper achieves an increase in sum secrecy throughput by 10.4% and 16.3% with faster convergence speed.
... Simultaneously, communication and sensing systems are increasingly converging their frequency bands in response to the rising demand for fast communication and high-resolution sensing [2], which has led to competition for spectrum. Therefore, integrated sensing and communication (ISAC) aiming to combine both functions within a single system to improve spectrum and energy efficiency is considered promising to enable 6G wireless communications [3]. ...
The recently proposed multi-chirp waveform, affine frequency division multiplexing (AFDM), is regarded as a prospective candidate for integrated sensing and communication (ISAC) due to its robust performance in high-mobility scenarios and full diversity achievement in doubly dispersive channels. However, the insufficient Doppler resolution caused by limited transmission duration can reduce the accuracy of parameter estimation. In this paper, we propose a new off-grid target parameter estimation scheme to jointly estimate the range and velocity of the targets for AFDM-ISAC system, where the off-grid Doppler components are incorporated to enhance estimation accuracy. Specifically, we form the sensing model as an off-grid sparse signal recovery problem relying on the virtual delay and Doppler grids defined in the discrete affine Fourier (DAF) domain, where the off-grid components are regarded as hyper-parameters for estimation. We also employ the expectation-maximization (EM) technique via a sparse Bayesian learning (SBL) framework to update hyper-parameters iteratively. Simulation results indicate that our proposed off-grid algorithm outperforms existing algorithms in sensing performance and is highly robust to the AFDM-ISAC high-mobility scenario.
... In [20], an overview of converged 6G communication, localization, and sensing systems is presented. In [21], an in-depth review of systems and technologies related to joint communication and radar sensing in mobile networks is given. In [22], a thorough review of ISAC channel modeling approaches is presented. ...
... Note that the definition is conditioned on the existence of K 0 UEs in a given coverage area. The definition in (21) has been employed in [59]. • Sensing Outage Probability: The sensing outage probability is defined as the probability that a target cannot be successfully detected by the BS. ...
One of the most promising technologies for next-generation wireless networks is integrated communication and sensing (ISAC). It is considered a key enabler for applications that require both enhanced communication and accurate sensing capabilities. Examples of such applications include smart environments, augmented and virtual reality, or the internet of things, where the capabilities of intelligent sensing and broadband communications are vital. Therefore, ISAC has attracted the research interest of both academia and industry, and many investigations have been carried out over the past decade. The articles in the literature include system models, performance evaluation, and optimization studies of several ISAC alternative designs. Stochastic geometry is the study and analysis of random spatial patterns, and as such, stochastic geometry tools have been considered for the performance evaluation of wireless networks with different types of nodes. In this paper, we aim to provide a comprehensive survey of current research progress in performance evaluation of ISAC systems using stochastic geometry tools. The survey covers terrestrial, aerial, and vehicular networks, where the random spatial location of the corresponding network elements and propagation scatterers and/or blockages is treated with various point processes. The paper starts with a short tutorial on ISAC technology, stochastic geometry tools, and metrics used in performance evaluation of communication and sensing. Then, the technical components of the system models utilized in the surveyed papers are discussed. Subsequently, we present the key results of the literature in all types of networks using three levels of integration: sensing-assisted communication, communication-assisted sensing, and joint sensing and communication. Finally, future research challenges and promising directions are discussed.
... I Ntegrated sensing and communication (ISAC) has emerged as a key technology for next-generation wireless networks, enabling the joint delivery of sensing and communication services through shared hardware and spectrum [1]- [3]. By optimizing infrastructure, frequency spectrum, and power allocation of ISAC systems, it fosters sustainable development of future wireless networks [4]- [6]. This has attracted significant attention, driving advancements in signaling design [7], waveform selection [8], resource allocation [9], and physical layer security [10]. ...
... The key idea is that the true target range and Doppler parameters in L → and L → remain identical, while the effects of TO and CFO manifest symmetrically in opposite directions as shown in Fig. 2(b) and 2(c). By analyzing the sensing channel in (6), which consists of the true target channel and the offset between two nodes, the following proposition holds for reciprocal bistatic links: Proposition 1. The TO and CFO between node and node in the reciprocal bistatic sensing channels H , and H , satisfy the offset reciprocity as ...
A distributed integrated sensing and communication (D-ISAC) system offers significant cooperative gains for both sensing and communication performance. These gains, however, can only be fully realized when the distributed nodes are perfectly synchronized, which is a challenge that remains largely unaddressed in current ISAC research. In this paper, we propose an over-the-air time-frequency synchronization framework for the D-ISAC system, leveraging the reciprocity of bistatic sensing channels. This approach overcomes the impractical dependency of traditional methods on a direct line-of-sight (LoS) link, enabling the estimation of time offset (TO) and carrier frequency offset (CFO) between two ISAC nodes even in non-LoS (NLOS) scenarios. To achieve this, we introduce a bistatic signal matching (BSM) technique with delay-Doppler decoupling, which exploits offset reciprocity (OR) in bistatic observations. This method compresses multiple sensing links into a single offset for estimation. We further present off-grid super-resolution estimators for TO and CFO, including the maximum likelihood estimator (MLE) and the matrix pencil (MP) method, combined with BSM processing. These estimators provide accurate offset estimation compared to spectral cross-correlation techniques. Also, we extend the pairwise synchronization leveraging OR between two nodes to the synchronization of N multiple distributed nodes, referred to as centralized pairwise synchronization. We analyze the Cramer-Rao bounds (CRBs) for TO and CFO estimates and evaluate the impact of D-ISAC synchronization on the bottom-line target localization performance. Simulation results validate the effectiveness of the proposed algorithm, confirm the theoretical analysis, and demonstrate that the proposed synchronization approach can recover up to 96% of the bottom-line target localization performance of the fully-synchronous D-ISAC.
... Consequently, the integration of communication and sensing plays a vital role in improving the efficiency of wireless resources. The integrated sensing and communication (ISAC) system is expected to provide wireless communication and radar sensing functions, which promotes the realization of new intelligent applications and services in the future sixthgeneration (6G) communication systems, such as unmanned aerial vehicle communication and sensing, vehicle networking, and smart cities [2]. ...
In this letter, we investigate a coordinated multiple point (CoMP)-aided integrated sensing and communication (ISAC) system that supports multiple users and targets. Multiple base stations (BSs) employ a coordinated power allocation strategy to serve their associated single-antenna communication users (CUs) while utilizing the echo signals for joint radar target (RT) detection. The probability of detection (PoD) of the CoMP-ISAC system is then proposed for assessing the sensing performance. To maximize the sum rate while ensuring the PoD for each RT and adhering to the total transmit power budget across all BSs, we introduce an efficient power allocation strategy. Finally, simulation results are provided to validate the analytical findings, demonstrating that the proposed power allocation scheme effectively enhances the sum rate while satisfying the sensing requirements.
... Maximizing (23) and (24) for multiple-target joint parameter estimation remains challenging due to its high dimensionality and strong nonlinearity. Nevertheless, with the proposed discrete-signal-based MLE problem, existing estimation schemes, such as compressed sensing and relaxationbased approaches can be leveraged for low-complexity and accuracy estimations [53]. A detailed discussion of this aspect is beyond the scope of this paper. ...
This paper investigates joint location and velocity estimation, along with their fundamental performance bounds analysis, in a cell-free multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system. First, unlike existing studies that derive likelihood functions for target parameter estimation using continuous received signals, we formulate the maximum likelihood estimation (MLE) for radar sensing based on discrete received signals at a given sampling rate. Second, leveraging the proposed MLEs, we derive closed-form Cramer-Rao lower bounds (CRLBs) for joint location and velocity estimation in both single-target and multiple-target scenarios. Third, to enhance computational efficiency, we propose approximate CRLBs and conduct an in-depth accuracy analysis. Additionally, we thoroughly examine the impact of sampling rate, squared effective bandwidth, and time width on CRLB performance. For multiple-target scenarios, the concepts of safety distance and safety velocity are introduced to characterize conditions under which the CRLBs for multiple targets converge to their single target counterparts. Finally, extensive simulations are conducted to verify the accuracy of the proposed CRLBs and the theoretical results using state-of-the-art waveforms, namely orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM).
... Recently, the idea of Integrated Sensing and Communication (ISAC) has emerged, garnering considerable attention in the research community [2]. ISAC aims to combine the functionalities of communication and sensing systems, leveraging scarce spectral resources to create costefficient, intelligent, and user-friendly wireless networks with unprecedented possibilities [3]. Nevertheless, establishing a reliable ISAC service poses serious challenges, particularly in unfavorable electromagnetic (EM) propagation environments, operating at millimeter-wave (mmWave) and terahertz (THz) frequency bands, which offer abundant spectral resources [4]. ...
The advance towards 6G networks comes with the promise of unprecedented performance in sensing and communication capabilities. The feat of achieving those, while satisfying the ever-growing demands placed on wireless networks, promises revolutionary advancements in sensing and communication technologies. As 6G aims to cater to the growing demands of wireless network users, the implementation of intelligent and efficient solutions becomes essential. In particular, reconfigurable intelligent surfaces (RISs), also known as Smart Surfaces, are envisioned as a transformative technology for future 6G networks. The performance of RISs when used to augment existing devices is nevertheless largely affected by their precise location. Suboptimal deployments are also costly to correct, negating their low-cost benefits. This paper investigates the topic of optimal RISs diffusion, taking into account the improvement they provide both for the sensing and communication capabilities of the infrastructure while working with other antennas and sensors. We develop a combined metric that takes into account the properties and location of the individual devices to compute the performance of the entire infrastructure. We then use it as a foundation to build a reinforcement learning architecture that solves the RIS deployment problem. Since our metric measures the surface where given localization thresholds are achieved and the communication coverage of the area of interest, the novel framework we provide is able to seamlessly balance sensing and communication, showing its performance gain against reference solutions, where it achieves simultaneously almost the reference performance for communication and the reference performance for localization.
... The sixth generation (6G) of communication networks is envisioned to support various applications that require both Ahmed precise location information and high data rates simultaneously such as autonomous driving, healthcare monitoring, precision agriculture, and the internet of things (IoT) [1]. This has motivated the exploration of integrated sensing and communication (ISAC), where sensing and communication are performed simultaneously using shared resources [2]- [4]. However, ISAC introduces tradeoffs between sensing and communication performance. ...
... In the first step, we choose random W, Φ, and u that satisfy the constraints (27b), (27e), and (27f), respectively, which is a straightforward task. Similarly, we start with an initial R. Next, we focus on refining this initialization to satisfy the constraints (27c) and (27d) 4 . ...
... Algorithm 3: Joint beamforming design for communication, detection and protection against adversarial detectors in RIS-aided ISAC Input: τ = 0 , ϵ > 0 1 Find an initial feasible point W (0) , Φ (0) , u (0) and R (0) as described in Section IV-C; 2 repeat 3 τ ← τ + 1; 4 Compute W (τ ) and Φ (τ ) using Algorithm 1; 5 Compute u (τ ) and R (τ ) , and update A, G r , h r,k , c r,ℓ , c a,ℓ , e r , e a and Φ using Algorithm 2; ...
Integrated sensing and communication (ISAC) has been identified as a promising technology for the sixth generation (6G) of communication networks. Target privacy in ISAC is essential to ensure that only legitimate sensors can detect the target while keeping it hidden from malicious ones. In this paper, we consider a downlink reconfigurable intelligent surface (RIS)-assisted ISAC system capable of protecting a sensing region against an adversarial detector. The RIS consists of both reflecting and sensing elements, adaptively changing the element assignment based on system needs. To achieve this, we minimize the maximum sensing signal-to-interference-plus-noise-ratio (SINR) at the adversarial detector within sample points in the sensing region, by optimizing the transmit beamformer at the base station, the RIS phase shift matrix, the received beamformer at the RIS, and the division between reflecting and absorptive elements at the RIS, where the latter function as sensing elements. At the same time, the system is designed to maintain a minimum sensing SINR at each monitored location, as well as minimum communication SINR for each user. To solve this challenging optimization problem, we develop an alternating optimization approach combined with a successive convex approximation based method tailored for each subproblem. Our results show that the proposed approach achieves a 25 dB reduction in the maximum sensing SINR at the adversarial detector compared to scenarios without sensing area protection. Also, the optimal RIS element assignment can further improve sensing protection by 3 dB over RISs with fixed element configuration.
