This paper investigates the fundamental information-theoretic limits for the control and sensing of noiseless linear dynamical systems subject to a broad class of nonlinear observations. We analyze the interactions between the control and sensing components by characterizing the minimum information flow required for stability. Specifically, we derive necessary conditions for mean-square observability and stabilizability, demonstrating that the average directed information rate from the state to the observations must exceed the intrinsic expansion rate of the unstable dynamics. Furthermore, to address the challenges posed by non-Gaussian distributions inherent to nonlinear observation channels, we establish sufficient conditions by imposing regularity assumptions, specifically log-concavity, on the system's probabilistic components. We show that under these conditions, the divergence of differential entropy implies the convergence of the estimation error, thereby closing the gap between information-theoretic bounds and estimation performance. By establishing these results, we unveil the fundamental performance limits imposed by the sensing layer, extending classical data-rate constraints to the more challenging regime of nonlinear observation models.

In this paper, we analyze the periodic transmission masks for MASked Modulation (MASM) in half-duplex integrated sensing and communication (ISAC), and derive their closed-form expected range-Doppler response $\mathbb{E}\{r(k,l,ν)\}$. We show that range sidelobes ($k\neq l$) are Doppler-invariant, extending the range-sidelobe optimality to the 2-D setting. For the range mainlobe ($k=l$), periodic masking yields sparse Doppler sidelobes: Cyclic difference sets (CDSs) (in particular Singer CDSs) are minimax-optimal in a moderately dynamic regime, while in a highly dynamic regime the Doppler-sidelobe energy is a concave function of the mask autocorrelation, revealing an inevitable tradeoff with mainlobe fluctuation.

In this paper, we investigate the problem of predictive beamforming design for tracking vehicles in an integrated sensing and communication (ISAC)-based near-field vehicle-toinfrastructure (V2I) system. The waveform design in near-field scenarios requires the joint consideration of both range and angle dimensions, posing new challenges to conventional beamforming and tracking strategies. To address this issue, we propose a predictive beamforming framework leveraging a large language model (LLM)-based neural network (LNN), which exploits historical channel state information (CSI) to facilitate accurate future beamforming decisions. Cramér–Rao bounds (CRBs) for angle and distance estimation, along with the achievable sum-rate, are applied as key metrics to evaluate the sensing and communication performance of the V2I system, respectively. Capitalizing on the derived performance metrics, we formulate the optimization problems aiming either to maximize the sum-rate subject to CRB constraints or to minimize the CRB while ensuring a required communication rate, thereby accommodating different design requirements. Moreover, to effectively capture the stochastic nature of vehicle driving behavior, the performance metrics are further expressed in expectation form over the distribution of possible driving states. Consequently, a data-driven optimization approach based on the LNN is adopted to handle the resulting intractable analytical expressions, and the underlying LNN is trained with task-specific loss functions. During the training process, low-rank adaptation (LoRA) is incorporated to fine-tune the pre-trained LLM, which significantly reduces the number of trainable parameters. Simulation results demonstrate that the proposed framework accurately predicts future vehicle kinematic parameters and effectively optimizes the power allocation across transmit links. As a result, it achieves superior and robust performance in both communication and sensing tasks, highlighting its potential as a vital solution for next-generation near-field V2I systems.

Integrated sensing and communications (ISAC) promises new use cases for mobile communication systems by reusing the communication signal for radar-like sensing. However, sensing and communications (S&C) impose conflicting requirements on the modulation format, resulting in a trade-off between their corresponding performance. This paper investigates constellation shaping as a means to simultaneously improve S&C performance in orthogonal frequency division multiplexing (OFDM)-based ISAC systems. We begin by deriving how the transmit symbols affect detection performance and derive theoretical lower and upper bounds on the maximum achievable information rate under a given sensing constraint. Using an autoencoder-based optimization, we investigate geometric, probabilistic, and joint constellation shaping, where joint shaping combines both approaches, employing both optimal maximum a-posteriori decoding and practical bit-metric decoding. Our results show that constellation shaping enables a flexible trade-off between S&C, can approach the derived upper bound, and significantly outperforms conventional modulation formats. Motivated by its practical implementation feasibility, we review probabilistic amplitude shaping (PAS) and propose a generalization tailored to ISAC. For this generalization, we propose a low-complexity log-likelihood ratio computation with negligible rate loss. We demonstrate that combining conventional and generalized PAS enables a flexible and low-complexity trade-off between S&C, closely approaching the performance of joint constellation shaping.

Integrated sensing and communication (ISAC) has emerged as a key enabler for future wireless networks by unifying communication and sensing functionalities within a shared framework. However, achieving the coordination gains of these two functionalities critically depends on accurate estimation of the sensing targets and communication channels, while their joint estimation remains challenging. To address this, this paper proposes a Bayesian tensor decomposition (BTD) approach for joint channel estimation and target sensing in multiple-input multiple-output (MIMO)-ISAC systems, where parts of sensing targets also act as communication scatterers. Specifically, we develop space-frequency domain received signal models for target sensing and channel estimation and formulate them as canonical polyadic decomposition (CPD) problems under the tensor decomposition framework. This formulation reveals the common multilinear structure and the partially shared physical parameters between sensing and communication, which underpins the ensuing joint estimation task. To solve these problems, we propose a dual-module Vandermonde structure-assisted BTD (V-BTD) algorithm that incorporates propagation-induced Vandermonde structure constraints within a Bayesian framework to enable effective sensing-communication collaboration while maintaining problem feasibility. In this algorithm, Module A estimates the factor matrices via unstructured BTD with Gaussian priors, whereas Module B exploits the Vandermonde structure to recover the underlying physical parameters using generalized von Mises priors. The dual-module design alternates between an unstructured tensor decomposition step and a structure-aware parameter recovery step, yielding a favorable trade-off between inference exactness and computational tractability. With the flexible prior models in the BTD framework, the proposed algorithm supports both uninformative and informative settings, thereby allowing sensing-derived information to be incorporated for communication channel estimation to further improve estimation accuracy. Simulation results demonstrate that the proposed method significantly outperforms the benchmarks, highlighting its superiority for advanced ISAC systems.

Integrated sensing and communication (ISAC) has gained traction in academia and industry. Recently, multipath components (MPCs), as a type of spatial resource, have the potential to improve the sensing performance in ISAC systems, especially in richly scattering environments. In this paper, we propose to leverage MPC and Khatri-Rao space-time (KRST) code within a single ISAC system to realize high-accuracy sensing for multiple dynamic targets and multi-user communication. Specifically, we propose a novel MPC-enhanced sensing processing scheme with symbol-level fusion, referred to as the “SL-MSP” scheme, to achieve high-accuracy localization of multiple dynamic targets and empower the single ISAC system with a new capability of absolute velocity estimation for multiple targets with a single sensing attempt. Furthermore, the KRST code is applied to flexibly balance communication and sensing performance in richly scattering environments. To evaluate the contribution of MPCs, the closed-form Cramér-Rao lower bounds (CRLBs) for location and absolute velocity estimation are derived. Simulation results illustrate that the proposed SL-MSP scheme is more robust and accurate in localization and absolute velocity estimation compared with the existing state-of-the-art schemes.

This paper proposes a cooperative cell-free integrated sensing and communication network (CoCF-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contributions of this work are three-fold. First, we introduce a CoCF-ISACNet with energy-efficient HBF architecture. To show the benefits of proposed CoCF-ISACNet, we propose to jointly design the HBF to maximize the network communication capacity while satisfying the constraint of beampattern similarity for radar sensing, which results in a highly dimensional and non-convex problem. Second, to facilitate the joint design, we propose a novel distributed optimization framework called Proximal grAdieNt Decentralized Alternating direction method of multipliers (PANDA). Third, we further adopt the proposed PANDA framework to solve the joint HBF design problem for the CoCF-ISACNet. By using the proposed PANDA framework, all access points (APs) optimize the HBF in parallel, where each AP only requires local channel state information and limited message exchange among the APs. Such framework reduces significantly the computational complexity and thus has pronounced benefits in practical scenarios. Simulation results verify the effectiveness of the proposed algorithm compared with the conventional centralized algorithm and show the remarkable performance improvement of radar sensing and communication by deploying CoCF-ISACNet.

This paper investigates transceiver design optimization strategies for frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar-communication coexistence (RCC) systems, focusing on both radar-centric and communication-centric modes. Specifically, the former formulates the design problem to maximize the signal-to-interference-plus-noise ratio (SINR) in mainlobe deceptive jammer scenarios, whereas the latter aims to maximize the communication rate while simultaneously satisfying a predefined radar SINR constraint. In this framework, practical constraints pertaining to the radar’s transmitted waveform, communication codebook, frequency increment, and receive filter are taken into account. To address the resultant non-convex and NP-hard optimization problems, a maximum block improvement (MBI) approach is employed, where the variables are alternately examined, which are achieved either by leveraging closed-form expressions and hidden convexities or by resorting to the minorization-maximization (MM) approach, while keeping the remaining parameters fixed. The convergence performance of the devised algorithm is thoroughly examined, alongside their computational complexity analyses. Numerical results are provided to validate the efficacy of our approach against mainlobe deceptive jammers, demonstrating superior SINR and communication rate performance compared to existing optimization strategies and benchmark system frameworks.

Integrated Sensing and Communication (ISAC) has emerged as a promising solution in addressing the challenges of high-mobility scenarios in 5 G NR Vehicle-to-Infrastructure (V2I) communications. This paper proposes a novel sensing-assisted handover framework that leverages ISAC capabilities to enable precise beamforming and proactive handover decisions. Two sensing-enabled handover triggering algorithms are developed: a distance-based scheme that utilizes estimated spatial positioning, and a probability-based approach that predicts vehicle maneuvers using interacting multiple model extended Kalman filter (IMM-EKF) tracking. The proposed methods eliminate the need for uplink feedback and beam sweeping, thus significantly reducing signaling overhead and handover interruption time. A sensing-assisted NR frame structure and corresponding protocol design are also introduced to support rapid synchronization and access under vehicular mobility. Extensive link-level simulations using real-world map data demonstrate that the proposed framework reduces the average handover interruption time by over 50%, achieves lower handover rates, and enhances overall communication performance.

Wireless sensing enables contactless and accurate recognition of human activities and physiological states by using electromagnetic signals. As a promising enabler for sixth-generation (6 G) multi-functional networks, orthogonal time frequency space (OTFS) modulation exhibits strong resilience to high Doppler shifts in high-mobility environments, while also supporting precise human sensing in low-mobility scenarios. In this work, we propose a novel two-dimensional (2D) delay-Doppler motion profiling framework based on the OTFS waveform to extract distinctive features of human activities. To enhance recognition performance, a fractional-Doppler enhancement network is integrated with a convolutional neural network (CNN)-aided encoder-only Transformer architecture. Extensive experiments are conducted to assess the cross-domain generalization capability of the proposed OTFSensi system. Compared with existing classification models based on CNN, gated recurrent unit (GRU), and long short-term memory (LSTM) networks, OTFSensi demonstrates substantial improvements in adaptability across diverse environments and observation angles. Furthermore, a comparative analysis with various radio frequency (RF) sensing technologies confirms the superior classification performance achieved by OTFSensi.

Integrated sensing and communications (ISAC) is considered an innovative technology in sixth-generation (6G) wireless networks, where utilizing orthogonal frequency division multiplexing (OFDM) communication signals for sensing provides a cost-effective solution for implementing ISAC. However, the sensing performance of matched and mismatched filtering schemes can be significantly deteriorated due to the signaling randomness induced by finite-alphabet modulations with non-constant modulus, such as quadrature amplitude modulation (QAM) constellations. Therefore, improving sensing performance without significantly compromising communication capability (i.e., maintaining randomness), remains a challenging task. To that end, we propose a unified probabilistic constellation shaping (PCS) framework that is compatible with both matched and mismatched filtering schemes, by maximizing the communication rate while imposing constraints on mean square error (MSE) of sensing channel state information (CSI), power, and probability distribution. Specifically, the MSE of sensing CSI is leveraged to optimize sensing capability, which is illustrated to be a more comprehensive metric compared to the output SNR after filtering (SNRout) and integrated sidelobes ratio (ISLR). Additionally, the internal relationships among these three sensing metrics are explicitly analyzed. Building upon this, we further reveal that the normalized MSE can be interpreted as a penalty function version of the dynamic range, which is usually exploited to evaluate the behavior of delay-Doppler profiles. Finally, both simulations and field measurements validate the efficiency of proposed PCS approach in achieving a flexible S&C trade-off, as well as its credibility in enhancing 6G wireless transmission in real-world scenarios.

We are interested in the mobile target state detection (TSD) based on multi-input-multi-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) communication signals. Yet, communication-based TSD is of challenge, due to complex problem structures and communication symbol randomness. To address this challenge, we exploit structured features of low-speed and narrow-band systems to decouple the target state parameters and random communication symbols, and then use coherent detection to equalize random symbols. As such, a simplified sensing model holding an explicit space-time-frequency-domain correlation structure with respect to target direction angle, radial speed and relative distance, respectively, is obtained. Then, an efficient MIMO OFDM waveform-based TSD method extracting space-time-frequency correlation features is devised. It is verified by simulations that the proposed TSD method outperforms state-of-the-art baselines, due to the above problem-specific algorithm design. In addition, we establish the closed-form boundaries of the maximum detectable speed and maximum detectable range for MIMO OFDM-based TSD, which are essentially subject to the limited coherent time and bandwidth, respectively. The impact of system parameters (e.g., signal bandwidth, subcarrier spacing and carrier frequency) on the detection capability boundaries is analysed to gain insights into the fundamental limits of MIMO OFDM communication-based TSD. This work does not only build a technical foundation for sensing-assisted communication design, but also provide a unified framework for understanding the potentials of MIMO OFDM communication-based sensing.

In this paper, we investigate delay-Doppler (DD) sensing in a communication-centric integrated sensing and communication (ISAC) framework based on Zak transform-based orthogonal time frequency space (Zak-OTFS) modulation. Specifically, we consider target sensing with communication waveforms and propose a novel backpropagation (BP) algorithm for multi-target DD parameter estimation. We formulate the radar sensing task as a maximum likelihood parameter estimation problem, which is highly non-convex. By exploiting the structural analogy between parameter estimation and neural network training, the BP algorithm treats the DD parameters as tunable network weights and efficiently computes their gradients via the chain rule, enabling accurate and parallelized estimation. To facilitate the algorithm implementation, a successive interference cancellation method based on DD domain twisted convolution is developed to obtain coarse DD estimates. Furthermore, a constant false alarm rate based dynamic merging strategy is introduced to adaptively estimate the number of targets during the BP process. Comprehensive theoretical analyses are conducted, including the derivation of the Cramér–Rao bound (CRB) for Zak-OTFS systems and performance evaluation under various challenging sensing scenarios. Simulation results demonstrate that the proposed algorithm achieves high estimation accuracy and validates the theoretical analysis.

In reconfigurable intelligent surface (RIS)-assisted communication systems, the amplitudes and phases of reflected electromagnetic waves are modified to enhance signal power and provide additional communication paths, particularly in scenarios lacking a line-of-sight (LOS) propagation path. However, conventional RIS-aided communication systems reflect all incident signals indiscriminately, without distinguishing between desired signals and interference. In this paper, we propose a novel RIS-assisted communication system capable of selectively reflecting only the desired signal by performing interference suppression directly at the RIS. By exploiting the degree of freedom (DOF) in spatial domain of RIS, the design of RIS coefficients is formulated as a minimum variance distortionless response (MVDR) problem, where the optimal RIS coefficients are obtained by estimating the interference subspace and the desired signal. Then, we propose a two-step iterative approach based on atomic norm minimization (ANM) to estimate the subspace and desired signal simultaneously. The resulting ANM problem is solved with alternative optimization by decomposing it into two subproblem. Each subproblem is formulated as semidefinite programming (SDP) problem and efficiently solved using the alternating direction method of multipliers (ADMM). Theoretical analysis is conducted to verify the estimations are consistent and bounded. Simulation results validate its effectiveness and superiority over existing methods.

This paper proposes a promising framework of multi-functional service incorporating sensing targets (STs), information receivers (IRs), and energy receivers (ERs) in an active reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transfer (ISCPT) system. In the proposed system, we aim to maximize the weighted sum of the received radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the transmit beamforming at the multi-functional base station (MFBS), the coefficients of active RIS, and the radar receive filter coefficients. Meanwhile, the constraints of the SINR of IRs, energy harvesting (EH) requirements of ERs, the power budget for the MFBS and active RIS, and the amplification gain should be satisfied. To guarantee the generality of formulated problems, we further incorporate the self-interference effects of echo signals, multi-target echo interference, simultaneous detection of multiple STs, and a nonlinear EH model into the generalized system model. Due to the presence of echo interference and multi-target echo interference, the MFBS transmits the dedicated sensing signal with the communication to enhance the sensing performance. The formulated problem is tackled by developing an efficient alternating optimization (AO) algorithm combined with fractional programming (FP) and majorization-minimization (MM) techniques. Finally, the numerical results reveal the impact of system parameters on the sensing performance, the trade-off relationship between multiple functionalities, and the deployment strategy of RIS. The main findings are as follows: 1) Active RIS is remarkably superior to passive RIS for ISCPT systems, especially for closer to the receivers with a 40 dB performance gain. 2) Comparatively, the radar sensing SINR is more sensitive to the number of active RIS units, while the SINR of IRs is more sensitive to the number of antennas at the base station. These results demonstrate that the proposed system holds the potential for practical deployment.

This paper investigates the ambiguity function (AF) of the emerging affine frequency division multiplexing (AFDM) waveform for random integrated sensing and communication (ISAC) signaling under a pulse shaping regime. Specifically, we first derive the closed-form expression of the average squared discrete period AF (DPAF) for AFDM waveform without pulse shaping, revealing that the AF depends on the parameterc1and the kurtosis of random communication data, while being independent of the parameterc2. As a step further, we conduct a comprehensive analysis on the DPAFs of various waveforms, including AFDM, orthogonal frequency division multiplexing (OFDM) and orthogonal chirp-division multiplexing (OCDM). Our results indicate that all three waveforms exhibit the same number of regular depressions in the sidelobes of their DPAFs, which incurs performance loss for detecting and estimating weak targets. However, the AFDM waveform can flexibly control the positions of depressions by adjusting the parameterc1, which motivates a novel design approach of the AFDM parameters to mitigate the adverse impact of depressions of the strong target on the weak target. Furthermore, the closed-form expressions of the average squared DPAFs for pulse-shaped AFDM, OFDM and OCDM waveforms are derived, which demonstrates that the pulse shaping filter generates the shaped mainlobe along the delay axis and the rapid roll-off sidelobes along the Doppler axis. Numerical results verify the effectiveness of our theoretical analysis and proposed design methodology for the AFDM waveform.

Motion state sensing is crucial in wireless communication systems employing Integrated Sensing and Communication (ISAC), with accurate target velocity estimation being central to its effectiveness. While waveform design for ISAC signals can enhance the sensing capability, most existing studies focus on single-target scenarios, with limited discussion on multi-target scenarios. This paper proposes a novel waveform design approach aiming for enhancing the multi-target sensing performance, under the prior knowledge that the Doppler difference between two hardly resolvable targets lies in a certain interval. In particular, we use the Ziv-Zakai Bound (ZZB) of Doppler difference to design the waveform for multi-target sensing, which effectively captures the ambiguity phenomenon. We first derive the expression of the Doppler difference ZZB, which reveals the connection between ZZB and the widely-used ambiguity function. Based on this expression, we propose an SNR-adaptive pulse modulation strategy that significantly improves the velocity estimation accuracy for multi-target scenarios. Numerical results demonstrate that the Doppler difference ZZB effectively reflects the multi-target Doppler frequency estimation performance of maximum aposterioriestimators.

Integrated sensing and communications (ISAC) is one of the key enabling technologies in future sixth-generation (6G) networks. Current ISAC systems predominantly rely on deterministic pilot signals within the signal frame to accomplish sensing tasks. However, these pilot signals typically occupy only a small portion, e.g., 3% to 25%, of the time-frequency resources. To enhance the system utility, a promising solution is to repurpose the extensive random data payload signals for sensing tasks. In this paper, we analyze the ISAC performance of a multi-antenna system where both deterministic pilot and random data symbols are employed for sensing tasks. By capitalizing on random matrix theory (RMT), we first derive a semi-closed-form asymptotic expression of the ergodic linear minimum mean square error (ELMMSE), which evaluates the average sensing error of ISAC systems involving random data payload signals. Then, we formulate an ISAC precoding optimization problem to minimize the ELMMSE, which is solved via a specifically tailored successive convex approximation (SAC) algorithm. To provide system insights, we further derive a closed-form expression for the asymptotic ELMMSE at high signal-to-noise ratios (SNRs). Our analysis reveals that, compared with conventional sensing implemented by deterministic signals, the sensing performance degradation induced by random signals is critically determined by the ratio of the transmit antenna size to the data symbol length. Based on this result, the ISAC precoding optimization problem at high SNRs is transformed into a convex optimization problem that can be efficiently solved. Simulation results validate the accuracy of the derived asymptotic expressions of ELMMSE and the performance of the proposed precoding schemes. Particularly, by leveraging data payload signals for sensing tasks, the sensing error is reduced by up to 5.6 dB compared to conventional pilot-based sensing.

Affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) are two promising advanced waveforms proposed for reliable communications in high-mobility scenarios. In this paper, we introduce a simple transmit diversity technique, termed cyclic delay-Doppler shift (CDDS), for these two advanced waveforms to achieve ultra-reliable communications in doubly selective channels (DSCs). Two simple CDDS schemes, named modulation-domain CDDS (MD-CDDS) and time-domain CDDS (TD-CDDS), are proposed, which perform CDDS in advance at the transmitter before and after the modulation, respectively. We demonstrate that both of the two proposed CDDS schemes can be implemented efficiently and flexibly by multiplying the transmit vector with a well-designed precoding matrix, which is nothing but a sparse phase-compensated permutation matrix. Moreover, we theoretically and numerically prove that CDDS can provide MIMO-AFDM and MIMO-OTFS with optimal transmit diversity gain when a proper CDDS step is adopted. Compared to the conventional transmit diversity techniques, the proposed CDDS scheme enjoys the advantages of lower channel estimation overhead, implementation complexity, and signal processing latency, making it particularly suitable for ultra-reliable communications in high-mobility scenarios.

Beamforming is a key technology for achieving integrated sensing and communication (ISAC). However, most existing works focus on mono-static sensing, which has limited sensing accuracy and strong self-interference. To address these issues, this paper investigates hybrid beamforming (HBF) design for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) ISAC system with multi-static cooperative localization. Specifically, one access point (AP) simultaneously forms communication beams to serve multiple user equipments (UEs) and a sensing beam towards one target, and other multiple distributed APs perform cooperative localization on the target by estimating the angle-of-arrivals (AOAs) of received echo signals. First, to characterize the target localization accuracy, we derive the squared position error bound (SPEB) of AOA-based multi-static cooperative localization. Then, two HBF optimization problems are formulated to investigate the performance tradeoff between sensing and communication. For the sensing-centric design, we aim to minimize the SPEB of target localization while ensuring the signal-to-interference-plus-noise ratio (SINR) requirements of individual UEs. To tackle this nonconvex problem, we propose a semidefinite relaxation (SDR)-based alternating optimization algorithm. For the communication-centric design, a fractional programming (FP)-based alternating optimization algorithm is proposed for solving the communication sum-rate maximization problem under the sensing SPEB constraint. Simulation results demonstrate that the proposed two HBF algorithms can achieve localization accuracy and sum-rate performance close to fully-digital beamforming counterparts and outperform other baseline schemes.

High-precision ranging plays a crucial role in future 6G Integrated Sensing and Communication (ISAC) systems. To improve the ranging performance while maximizing the resource utilization efficiency, future 6G ISAC networks have to reuse data payload signals for both communication and sensing, whose inherent randomness may deteriorate the ranging performance. To address this issue, this paper investigates the power allocation (PA) design for an OFDM-based ISAC system under random signaling, aiming to reduce the ranging sidelobe level of both periodic and aperiodic auto-correlation functions (P-ACF and A-ACF) of the ISAC signal. Towards that end, we first derive the closed-form expressions of the average squared P-ACF and A-ACF, and then propose to minimize the expectation of the integrated sidelobe level (EISL) under arbitrary constellation mapping. We then rigorously prove that the uniform PA scheme achieves the global minimum of the EISL for both P-ACF and A-ACF. As a step further, we show that this scheme also minimizes the P-ACF sidelobe level at every lag. Moreover, we extend our analysis to the P-ACF case with frequency-domain zero-padding, which is a typical approach to improve the ranging resolution. We reveal that there exists a tradeoff between sidelobe level and mainlobe width, and employ the Dinkelbach’s method to seek a globally optimal PA scheme that reduces the EISL. Finally, we validate our theoretical findings through extensive simulation results, confirming the effectiveness of the proposed PA methods in reducing the ranging sidelobe level for random OFDM signals.

This paper investigates the sensing potential of affine frequency division multiplexing (AFDM) in high-mobility integrated sensing and communication (ISAC) from the perspective of radar waveforms. We introduce an innovative parameter selection criterion that establishes a precise mathematical equivalence between AFDM subcarriers and Nyquist-sampled frequency-modulated continuous-wave (FMCW). This connection not only provides a clear physical insight into AFDM's sensing mechanism but also enables a direct mapping from the DAFT index to delay-Doppler (DD) parameters of wireless channels. Building on this, we develop a novel input-output model in a DD-parameterized DAFT (DD-DAFT) domain for AFDM, which explicitly reveals the inherent DD coupling effect arising from the chirp-channel interaction. Subsequently, we design two matched-filtering sensing algorithms. The first is performed in the time-frequency domain with low complexity, while the second is operated in the DD-DAFT domain to precisely resolve the DD coupling. Simulations show that our algorithms achieve effective pilot-free sensing and demonstrate a fundamental trade-off between sensing performance, communication overhead, and computational complexity. The proposed AFDM outperforms classical AFDM and other variants in most scenarios.

This paper investigates an integrated sensing and communication (ISAC) system employing delay-Doppler (DD) signaling. The sensing performance of both random and deterministic signaling schemes is evaluated based on the expected squared ambiguity function (AF), for which closed-form expressions are derived by leveraging the Zak transform-based orthogonal time-frequency space (Zak-OTFS) modulation framework. Our analysis highlights a key difference between the two signaling types: DD domain ISAC (DD-ISAC) with deterministic signaling yields a roughly periodic AF with prominent peaks and low sidelobes between adjacent peaks, whereas DD-ISAC with random signaling using a Quadrature Phase-Shift Keying (QPSK) constellation exhibits low sidelobe values periodically without prominent peaks. Furthermore, we demonstrate that DD-ISAC enables a flexible trade-off between delay and Doppler sidelobe levels by adjusting the number of delay and Doppler bins. The analytical findings are explicitly validated through numerical simulations.

This paper exploits the integrated sensing and communication (ISAC) technology in unmanned aerial vehicle (UAV) networks, where multiple UAVs collaboratively form a virtual antenna array (VAA) within a pre-determined area, to effectively operate as a multi-antenna system for communication and sensing (C&S) services. Since the VAA is an extremely-large antenna array, the near-field characteristics must be considered in C&S channels. By optimizing UAV positions, we construct an enhanced VAA configuration and subsequently design the corresponding beamforming, thereby improving sensing performance while guaranteeing communication requirements. A penalty-based iterative algorithm is exploited to address the resulting optimization problem. Simulation results demonstrate that the optimized VAA with beamforming significantly enhances target localization accuracy compared to conventional schemes, validating the effectiveness of the ISAC implementation in UAV networks.

Accurate velocity sensing is crucial in Integrated Sensing and Communication (ISAC) systems, while most studies focus on single-target cases with limited attention to multi-target scenarios. This paper proposes a novel waveform design approach that enhances multi-target sensing performance by leveraging the Ziv-Zakai Bound (ZZB) of Doppler difference, effectively capturing the ambiguity phenomenon under the prior knowledge that the Doppler difference between two hardly resolvable targets lies in a certain interval. We first derive the ZZB for Doppler difference, which reveals the connection between ZZB and the widely-used ambiguity function. Based on this, an SNR-adaptive pulse modulation strategy is developed to enhance multi-target sensing accuracy. Numerical results confirm that the proposed Doppler difference ZZB effectively captures the estimation performance of maximum a posteriori estimators in multi-target scenarios.

Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced environmental sensing. Although passive sensing-based SLAM techniques can estimate user locations and environmental reflections simultaneously, their effectiveness is often constrained by assumptions of specular reflections and oversimplified map representations. To overcome these limitations, this work employs a mmWave 5G NR system for active sensing, enabling it to function similarly to a laser scanner for point cloud generation. Specifically, point clouds are extracted from the power delay profile estimated from each beam direction using a binary search approach. To ensure accuracy, hardware delays are calibrated with multiple predefined target points. Pose variations of the terminal are then estimated from point cloud data gathered along continuous trajectory viewpoints using point cloud registration algorithms. Loop closure detection and pose graph optimization are subsequently applied to refine the sensing results, achieving precise terminal localization and detailed radio map reconstruction. The system is implemented and validated through both simulations and experiments, confirming the effectiveness of the proposed approach.

This paper addresses the resource allocation optimization problem in an uplink OFDM-based integrated sensing and communications (ISAC) network, where several multiple access points (APs) communicate with a base station (BS) and collaborate on target localization simultaneously. The goal is to minimize the Cramer-Rao Lower Bound (CRLB) for target position estimation while ensuring that communication quality of service (QoS) requirements. This is achieved by assigning orthogonal subcarriers to the APs and optimizing power allocation across these subcarriers. Given the complexity of the mixed-integer optimization problem, we propose an efficient alternating optimization algorithm incorporating successive convex approximation (SCA) method to iteratively obtain near-optimal solutions. Numerical simulations show that the proposed algorithm significantly improves the sensing performance compared to conventional uniform power allocation and communicationonly power allocation schemes. These findings underscore the effectiveness of the proposed approach in achieving a balance between communication and sensing objectives in ISAC systems.

This paper investigates the problem of computing capacity-cost ($\mathbf{C}-\mathbf{C}$) functions for continuous channels. Motivated by the Kullback-Leibler divergence (KLD) proximal reformulation of the classical Blahut-Arimoto (BA) algorithm, the Wasserstein distance is introduced to the proximal term for the continuous case, resulting in an iterative algorithm related to the Wasserstein gradient descent. Practical implementation involves moving particles along the negative gradient direction of the objective function's first variation in the Wasserstein space and approximating integrals by the importance sampling (IS) technique. Such formulation is also applied to the rate-distortion (R-D) function for continuous source spaces and thus provides a unified computation framework for both problems.

Given the challenges of high mobility and frequent handovers in 5G NR's Vehicle-to-Infrastructure (V2I) communications, the Integrated Sensing and Communications (ISAC) technique, now recognized in IMT-2030 as a usage scenario, is introduced to boost mobile network efficiency. Leveraging this emerging paradigm, our study delves into a V2I network within the 5G NR context, proposing a sensing-assisted handover mechanism and protocol that integrates ISAC to refine the intercell handover process. This mechanism uses precise beamforming and kinematic parameter estimation to reduce signaling and interruption time in inter-cell handover process. This approach enables a distance-based handover triggering mechanism that is both proactive and information-rich, allowing the serving gNB to inform the target gNB of the vehicle's location preemptively. Numerical results from link-level simulations at a crossroad scenario demonstrate that this sensing-assisted handover mechanism enables faster triggering and reduces the interruption time by 76.46%, while enhancing communication performance.

In this work, we study the transmit sequence design for integrated sensing and communications (ISAC) using random single-carrier communication signals. Particularly, we focus on the sensing-centric ISAC, where a family of communication codewords is optimized to yield a good sensing performance. To this end, we formulate the problem of finding the optimal communication codewords by minimizing the integrated sidelobe of the ambiguity function under the transmit power constraint. Specifically, two optimization methods are developed to solve such a problem, whose suitability with different communication shaping pulses is also highlighted. We unveil that the considered problem has non-unique optimum that can be exploited to obtain a family of communication codewords with optimized sensing performance. Furthermore, the communication performance of the derived codewords is evaluated based on both the Euclidean distance and the pairwise error probability (PEP) over multipath fading channels. Our numerical results confirm the superiority of the optimized codewords and the effectiveness of the proposed optimization methods.

This paper aims at computing the capacity-distortion-cost (CDC) function for continuous memoryless channels, which is defined as the supremum of the mutual information between channel input and output, constrained by an input cost and an expected distortion of estimating channel state. Solving the optimization problem is challenging because the input distribution does not lie in a finite-dimensional Euclidean space and the optimal estimation function has no closed form in general. We propose to adopt the Wasserstein proximal point method and parametric models such as neural networks (NNs) to update the input distribution and estimation function alternately. To implement it in practice, the importance sampling (IS) technique is used to calculate integrals numerically, and the Wasserstein gradient descent is approximated by pushing forward particles. The algorithm is then applied to an integrated sensing and communications (ISAC) system, validating theoretical results at minimum and maximum distortion as well as the randomdeterministic trade-off.

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.

The physical cell identity (PCI) is a critical parameter in wireless networks, enabling user equipment (UE) to uniquely identify cells and mitigate inter-cell interference during communication. However, the proliferation of base stations in modern networks has complicated the proper assignment of PCIs, as it requires avoiding multiple types of PCI conflicts, such as PCI collision, mod-3 collision, and confusion. Existing methods generally focus on addressing single PCI conflicts and involve reassigning PCIs for all cells, which can lead to significant data exchange overhead in large-scale networks. To address these challenges, this paper tackles the PCI planning problem by introducing a PCI reassignment budget as a constraint to minimize overall network interference. A penalty-based double-loop PCI planning algorithm is proposed, where the outer loop optimizes penalty parameters derived from the constraints, and the inner loop combines a block coordinate descent (BCD) approach with a proposed concave-convex procedure to optimize PCI assignments. Simulation results demonstrate the effectiveness of the proposed method in significantly reducing network interference compared to existing approaches.

The rapid advancement of Internet of Things (IoT) networks has positioned unmanned aerial vehicles (UAV s) as critical enablers of next-generation wireless communication technologies. This paper focuses on orthogonal time frequency space (OTFS) modulation-assisted wireless control in UAV networks with finite blocklength (FBL) transmission. In particular, we in-vestigate the optimal power allocation that maximizes the fairness of control performance in terms of linear quadratic regulator (LQR) cost, subject to rate-LQR cost bounds and maximum available power budget constraints. To address the optimization problem, we first analyze the concave-convex property of the FBL rate function, followed by developing an efficient successive convex approximation (SCA)-based algorithm to obtain a sub-optimal solution. The convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Simulation results validate the effectiveness of the proposed approach, offering promising insights for UAV-enabled wireless control systems.

Spectrum competition and hardware complexity inherent in communication and radar systems can be alleviated by a dual-function radar communication (DFRC) systems. However, enhancing detection capabilities for maneuvering or weak targets remains a significant challenge, as traditional radar signal accumulation algorithms are not directly applicable to DFRC systems. This article proposes a novel method that integrates signal accumulation and parameter estimation for target detection in DFRC systems. The approach employs an orthogonal frequency division multiplexing (OFDM) waveform within a multiple-input-multiple-output (MIMO) framework, enabling the detection of high-speed or weak targets through a computationally efficient signal accumulation process. The proposed method comprises three key steps: first, the designed signals combined with the multiple signal classification (MUSIC) algorithm for target angle estimation in the spatial domain. Second, a two-step signal accumulation process is introduced, which separately extracts range and velocity information for weak or high-speed targets. Third, acceleration is derived based on velocity information and accumulation time. We present explicit expressions and detailed analyses of various performance metrics, including accumulation output response for high-speed targets, multiple target scenarios, and cases with low-signal-to-noise ratio (SNR). Additionally, Cramér-Rao bounds (CRBs) for azimuth, range cell, and velocity cell estimation in DFRC MIMO-OFDM systems are derived. Simulation results validate the proposed method, demonstrating its superior target detection performance compared to existing techniques.

Exploring the mutual benefit and reciprocity of sensing and communication (S&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations.

In this paper, we propose a novel scheme to estimate the six-dimensional (6D) motion parameters of the dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (BS) and the 6D motion parameters of the dynamic target. Then, we employ the array signal processing methods to estimate the horizontal angle, pitch angle, distance, and virtual velocity of the dynamic target. Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target’s radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities. Simulation results demonstrate the effectiveness of the proposed 6D motion parameters estimation scheme, which also confirms a new finding that one single BS with a massive MIMO array is capable of estimating the horizontal angular velocity and pitch angular velocity of the dynamic target.

Sensing performance is typically evaluated by classical radar metrics, such as Cramér-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the performance metric for sensing and communication, where sensing mutual information (SMI) was proposed as a sensing performance metric withdeterministicsignals. However, the communication need in ISAC systems necessitates the transmission ofrandomsignals for sensing applications, whereas an explicit evaluation for the SMI with random signals is not yet available in the literature. This paper aims to fill the research gap and investigate the unification of sensing and communication performance metrics. For that purpose, we first derive the explicit expression for the SMI with random signals utilizing random matrix theory. On top of that, we further build up the connections between SMI and traditional sensing metrics, such as ergodic minimum mean square error (EMMSE), ergodic linear minimum mean square error (ELMMSE), and ergodic Bayesian Cram´er- Rao bound (EBCRB). Such connections open up the opportunity to unify sensing and communication performance metrics, which facilitates the analysis and design for ISAC systems. Finally, SMI is utilized to optimize the precoder for both sensing-only and ISAC applications. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed precoding design.

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC):What is the optimal communication-centric ISAC waveform for ranging?Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

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.

Orthogonal Time-Frequency Space (OTFS) modulation is an emerging technique that characterizes wireless channels and transmits information in the delay-Doppler domain. This work focuses on estimating fundamental sensing parameters, i.e., the delay and Doppler shifts of individual propagation paths, which serve as critical enablers for downstream positioning techniques, such as time-difference-of-arrival (TDOA)-based localization. Specifically, we propose a parameter-inherited (PI) channel estimation method that integrates sparse Bayesian learning (SBL) with unitary approximate message passing (UAMP), achieving low computational complexity and high estimation robustness. To accelerate the convergence of the UAMP-based iterative estimation, we explore the strategy of initializing parameters by inheriting prior estimates from adjacent OTFS transmission blocks. Furthermore, the overall computational burden is significantly reduced by employing large-scale matrix operations via two-dimensional fast Fourier transform (2D FFT). The proposed algorithms are implemented and evaluated on a software-defined radio (SDR)-based ISAC platform. Experimental results demonstrate that the proposed dual-functional system outperforms existing benchmarks in both communication quality and sensing parameter accuracy.

Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems.

This paper proposes an integrated sensing and communications (ISAC) system based on affine frequency division multiplexing (AFDM) waveform. To this end, a metric set is designed according to not only the maximum tolerable delay/Doppler, but also the weighted spectral efficiency as well as the outage/error probability of sensing and communications. This enables the analytical investigation of the performance trade-offs of AFDM-ISAC system using the derived analytical relation among metrics and AFDM waveform parameters. Moreover, by revealing that delay and the integral/fractional parts of normalized Doppler can be decoupled in the affine Fourier transform-Doppler domain, an efficient estimation method is proposed for our AFDM-ISAC system, whose unambiguous Doppler can break through the limitation of subcarrier spacing. Theoretical analyses and numerical results verify that our proposed AFDM-ISAC system may significantly enlarge unambiguous delay/Doppler while possessing good spectral efficiency and peak-to-sidelobe level ratio in high-mobility scenarios.

Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next-generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization problem takes into account the maximum available transmission power budget and the required sensing performance. Unlike previous studies that focused on ISAC in the time-frequency (TF) domain, we devise the precoder from the perspective of the delay-Doppler (DD) domain by exploiting the equivalent DD domain channel. The DD domain channel generally tends to be sparse and quasi-static, which is conducive to a low-complexity ISAC system design. To address the non-convex optimization design problem, we resort to optimizing the lower bound of the derived average BER by adopting Jensen’s inequality. Subsequently, the formulated problem is decoupled into two independent sub-problems via singular value decomposition (SVD) methodology. We then theoretically analyze the feasibility conditions of the proposed problem and present a low-complexity iterative solution via leveraging the Lagrangian duality approach. Simulation results verify the effectiveness of our proposed precoder compared to the benchmark schemes and reveal the interplay between sensing and communication for dual-functional precoder design, indicating a trade-off where transmission efficiency is sacrificed for increasing transmission reliability and sensing accuracy.

In low-altitude economy (LAE), the widespread use of various types of unmanned aerial vehicles (UAVs) could provide convenience and enhance efficiency. However, the existence of unauthorized or illegal UAVs would pose significant challenges to urban privacy and security. In this paper, we propose a networked integrated sensing and communications (ISAC) based UAV tracking and handover scheme towards LAE. We define avirtual sensing cell (VSC)where oneprimary base station (PBS)transmits sensing signals, while both the PBS and twosecondary base stations (SBS)receive echoes. Since the echoes contain the clutter of static environment, each base station (BS) would first filter out the clutter and then estimate the UAV’s horizontal angle, elevation angle, distance, and radial velocity with the multiple signal classification (MUSIC) algorithm. Next, we employ the centralized extended Kalman filter (EKF) to fuse the estimations from the three BSs and leverage the one-step prediction results of the EKF to distinguish and track multiple UAVs. When the UAV flies within the coverage of a VSC, we design aPBS handoverstrategy to select the optimal BS from three BSs as the new PBS in real-time. Moreover, we propose aVSC handoverstrategy to track the UAV continuously when it flies from one VSC to another. Simulation results demonstrate the effectiveness of the proposed scheme and provide valuable reference for UAV tracking and handover in LAE.

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.

This paper explores a novel integrated localization and communication (ILAC) system using the affine Fourier transform multicarrier (AFT-MC) waveform. Specifically, we consider a multiple-input multiple-output (MIMO) AFT-MC system with ILAC and derive a continuous delay and Doppler shift channel matrix model. Based on the derived signal model, we develop a two-step algorithm with low complexity for estimating channel parameters. Furthermore, we derive the Cramér-Rao lower bound (CRLB) of location estimation as the fundamental limit of localization. Finally, we provide some insights about the AFT-MC parameters by explaining the impact of the parameters on localization performance. Simulation results demonstrate that the AFT-MC waveform is able to provide significant localization performance improvement compared to orthogonal frequency division multiplexing (OFDM) while achieving the CRLB of location estimation.

Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. In this paper, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), accounting for the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the optimized sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations.

In this paper, we propose a multi-input multi-output beamforming transmit optimization model for joint radar sensing and multi-user communications, where the design of the beamformers is formulated as an optimization problem whose objective is a weighted combination of the sum rate and the Cramér-Rao bound, subject to the transmit power budget constraint. Obtaining a global solution for the formulated problem is a challenging task, because the sum rate maximization problem itself (even without considering the sensing metric) is known to be NP-hard. In this paper, we propose an efficient global branch-and-bound algorithm for solving the formulated problem based on the McCormick envelope relaxation and the semidefinite relaxation technique. The proposed algorithm is guaranteed to find the global solution for the considered problem, and thus serves as an important benchmark for performance evaluation of the existing local or suboptimal algorithms for solving the same problem.

Integrated sensing and communications (ISAC) has been recognized as a key component in the envisioned 6G communication systems. Understanding the fundamental performance tradeoff between sensing and communication functionalities is essential for designing practical cost-efficient ISAC systems. In this paper, we aim for augmenting the current understanding of the deterministic-random tradeoff (DRT) between sensing and communication, by analyzing the sensing-optimal operating point of the fundamental capacity-distortion region. We show that the DRT exists for generic sensing performance metrics that are in general not convex/concave in the ISAC waveform. Especially, we elaborate on a representative non-convex performance metric, namely the detection probability for target detection tasks.

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal- to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the SMI and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The accuracy of the theoretical analysis and the effectiveness of the proposed precoder design method are validated by simulation results.

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.

This paper proposes a novel design algorithm for dual-function radar communication (DFRC) Orthogonal Frequency-Division Multiplexing (OFDM) waveform. The algorithm achieves a tradeoff between detection performance and communication bit error rate (BER) performance. Firstly, the algorithm modulates communication information on the phase of the subcarrier coefficient of OFDM waveform using M-phase-shift keying (MPSK) schemes such as Binary-PSK (BPSK). Subsequently, the waveform minimises the weighted integrated sidelobe level (WISL) of the transmit waveform and the receive mismatch filter while ensuring the BER. Additionally, constraints are placed on constant amplitude, mainlobe energy and signal-to-noise ratio (SNR) loss. To address the non-convex optimization issues arising from algorithm design, a Weight Alternating Direction Method of Penalty (W-ADPM) network-based approach simultaneously optimises the transmit waveform and receives mismatched filters. The simulation experiments demonstrate that the proposed algorithm has better convergence performance for the proposed waveform compared to the Alternating Direction Method of Multipliers (ADMM) algorithm. Besides, compared to traditional matched filters, the jointly transmitted and received mismatched filters proposed in this paper provide better WISL cross-correlation performance while ensuring the BER.

IoT-based wireless sensor networks (WSNs) face significant energy constraints, which can be alleviated by wireless power transfer (WPT) technology. Integrating WPT with WSNs creates wireless rechargeable sensor networks (WRSNs), where optimizing charging efficiency and scheduling is critical. This paper introduces an ISAC-facilitated optimal on-demand mobile charging scheme for IoT-based WRSNs (IOMSN) with three key components. First, it presents an ISAC-assisted prioritized charging queue, incorporating four attributes with probability distributions: residual energy, traffic load, MCV travel time, and direction angle. Second, it provides ISAC-driven estimations of MCV distance, speed, and location to enhance prioritization, thereby optimizing the charging route and potentially reducing travel costs. Third, a time-allocated partial charging model improves charging efficiency. Numerical results show that the proposed protocol outperforms cutting-edge protocols in energy usage efficiency, travel distance, charging delay, and service time.

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.

The emergence of connected autonomous vehicles (CAVs) has transformed the realms of transportation and communications. Meeting the demands of future CAVs networks requires the seamless integration of two essential functions: communications and sensing. However, in dense CAVs scenarios, meeting the demands for one-to-one beam-CAV services become challenging due to limited spatial freedom and severe beams interference. In this paper, a novel sensing-assisted CAV s clustering and beams alignment method is proposed. Based on the echo signal, the kinematic parameters are measured, and the extended Kalman filter (EKF) is designed for angle tracking. On this basis, a CAV s clustering method is also proposed. Simulation results verify superior tracking performance compared to other methods. The root mean square error (RMSE) is less than 0.03 and the sum rates can be improved by up to 4 times.

In this study, we explore integrated sensing and communication (ISAC) networks to strike a more effective balance between sensing and communication (S&C) performance at the network scale. We leverage stochastic geometry to analyze the S&C performance, shedding light on critical cooperative dependencies of ISAC networks. According to the derived expres-sions of network performance, we optimize the user/target loads and the cooperative base station cluster sizes for S&C to achieve a flexible trade-off between network-scale S&C performance. It is observed that the optimal strategy emphasizes the full utilization of spatial resources to enhance multiplexing and diversity gain when maximizing communication ASE. In contrast, for sensing objectives, parts of spatial resources are allocated to cancel inter-cell sensing interference to maximize sensing ASE. Simulation results validate that the proposed ISAC scheme realizes a remarkable enhancement in overall S&C network performance.

This paper addresses the design of the receive combining matrix in a multiuser multiple-input multiple-output (MU-MIMO) downlink system, where the base station (BS) employs symbol-level precoding (SLP) to transmit multiple data streams to multiple users with multiple antennas. Unlike in the single-antenna user scenario, the design of the receive combining matrix becomes crucial in this context. To overcome the challenge of the receive combining matrix's dependency on the transmit signals, we propose a practical scheme utilizing the interference rejection combiner (IRC) for signal decoding. However, directly applying the IRC receiver to the considered MU-MIMO system presents challenges due to the rank-one transmit precoding matrix. To address this issue, we propose a new regularized IRC (RIRC) receiver. The problem is tackled by using the alternating optimization (AO) method, enabling the derivation of an optimal solution structure for the transmit precoding matrix. Numerical results demonstrate the substantial performance gain of the practical SLP scheme with the RIRC receiver over conventional Block Diagonalization (BD) based approach.

The key to friendly collaboration in vehicular communication systems lies in the reliable communication between vehicles. The current systems, which employ fixed transmission schemes, significantly constrain system capacity in respect of spectrum. Besides, considering the dynamic environment of vehicular communications, the requirement for real-time scenario identification is particularly urgent. Hence, in order to improve the reliability of communications, this paper proposes an adaptive modulation and coding (AMC) technique driven by deep learning (DL)-based scenario identification (SI) in vehicular communication systems, namely SI-AMC. In contrast to the traditional AMC technique, our proposed SI-AMC attains a refined channel response estimation through the pre-discrimination of scenario features, thereby further enhancing vehicular communication performance. During the transmission process, the SI-AMC scheme achieves environment adaptability through rate-adaptive adjustments. Moreover, in terms of SI, we creatively design an enhanced convolutional neural network structure which ex-ploits a novel activation function and regularization strategies to enhance the robustness of the model. Simulation results show that the accuracy of SI reaches up to 97.86 % and the throughput of the entire link in vehicular communications is effectively promoted. Code and the model will be released at https://github.com/communicationDLl/SI-AMC.

Integrated sensing and communication (ISAC) base stations can provide communication and wide range sensing for vehicles via downlink (DL) transmission, thus enhancing the driving safety. One major challenge for achieving the high performance of communication and sensing is how to deal with the DL mutual interference among adjacent ISAC base stations, which includes not only communication-related interference but also sensing-related interference. In this article, we establish a DL mutual interference model of adjacent ISAC base stations, and analyze the relationship between the communication and sensing mutual interference channels. To mitigate the mutual interference, we propose a collaborative precoding design for adjacent base stations under the transmit power constraint and constant modulus constraint. To solve the nonconvex collaborative precoding design problem, we first relax the problem into a convex programming by omitting the rank constraint, and propose a joint optimization algorithm to solve the problem. To reduce computational complexity, We further propose a sequential optimization algorithm, which divides the collaborative precoding design problem into four subproblems and finds the optimum via a gradient descent algorithm. Finally, we evaluate the collaborative precoding design algorithms by considering sensing and communication performance via numerical results.

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.

This article presents a new sensing channel modeling approach by jointly considering ray-tracing (RT) and stochastic methods, to accurate and efficient model sensing channels for vehicle-to-everything (V2X) applications. For the former, moving targets are modeled through accurate RT simulations while for the latter a statistical approach is used for generating complex environmental clutter by emphasizing for the first time individual object modeling. This approach is used to form a feature library of objects which ensures space-time consistency while significantly improving the modeling speed. The channel transfer functions generated by RT and stochastic methods are jointly considered through coherent superposition to form a more complete sensing channel which includes both clutters and targets. To verify its effectiveness and accuracy, a comprehensive experimental study has been conducted taking systematic measurements using a 77-GHz mmWave radar as it is the prevalent equipment for sensing used for intelligent driving applications. We have considered a typical V2X scenario, with the radar deployed on vehicles traveling along roads at an urban intersection. The experimental results obtained have demonstrated that the accuracy in target distance detection and velocity estimation has improved leading to errors of less than 0.5 m and less than 0.2 m/s, respectively, while for clutter modeling, the error of power is 3 to 6 dB. Moreover, compared to traditional RT methods, the proposed approach is 20 times faster. Through the proposed approach, realistic sensing channel data can be obtained in a systematic, effective, and accurate manner, facilitating research of sensing-assisted communication applications.

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.

Integrated sensing and communications (ISAC) has received considerable attention from both industry and academia. By sharing the spectrum and hardware platform, ISAC significantly reduces costs and improves spectral, energy, and hardware efficiencies. To support the large number of communication users (CUs) and sensing targets (STs), the design of multiple access (MA) is a fundamental issue in ISAC. MA techniques in ISAC are expected to avoid mutual interference between sensing and communicating functions under the critical constraints of both functions. In this article, we present an overview on approaches of MA for ISAC, from orthogonal transmission strategies to nonorthogonal ones, realized in time, frequency, code, spatial, delay-Doppler, power, and/or multiple domains. We discuss their individual implementation schemes and corresponding resource allocation strategies, as well as highlight future research opportunities.

Consider a multiuser multiple-input multiple-output (MU-MIMO) downlink system in which the base station (BS) sends multiple data streams to multi-antenna users via symbol-level precoding (SLP), where the optimization of receive combining matrix becomes crucial, unlike in the single-antenna user scenario. We begin by introducing a joint optimization problem on the symbol-level transmit precoder and receive combiner. The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions. To address the challenge that the above joint design is difficult to implement, we propose a more practical scheme where the receive combining optimization is replaced by the interference rejection combiner (IRC), which is however difficult to directly use because of the rank-one transmit precoding matrix. Therefore, we introduce a new regularized IRC (RIRC) receiver to circumvent the above issue. Numerical results demonstrate that the practical SLP-RIRC method enjoys only a slight communication performance loss compared to the joint transmit precoding and receive combining design, both offering substantial performance gains over the conventional BD-based approaches.

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. Recognizing the inherent tradeoff between energy harvesting and sensing performance, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first algorithm involves the semi-positive definite programming techniques, and the second algorithm is based on the successive convex approximations and majorization minimization to design the closed form solutions of the optimization variables, which can effectively reduce the computational complexity. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. This research contributes to the integration of wireless sensing and wireless energy in intelligent transportation systems and underscores the optimization of system performance through the introduction of IRS.

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks.

The internet of things (IoT) and wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The attributes are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, charging coverage, survival rate, travel distance, queue length, and service time.

Integrated sensing and communication (ISAC) is studied in the airborne domain, where Unmanned Aerial Vehicles (UAVs) act as communication base stations and radars simultaneously. The UAV transmits signals to users while leveraging these signals to localize targets. This research focuses on jointly improving communication and sensing (C&S) performances by designing the UAV trajectory and allocating user’s bandwidth. Since UAV’s sustainability is determined by its onboard battery, energy supply is considered as a constraint in the trajectory design. Communication performance is evaluated by total transmitted data, while sensing performance is assessed through Cramér-Rao bound (CRB). A tradeoff objective is formulated with normalization. To achieve a flexible tradeoff between C&S, the trajectory design is formulated as a weighted sum optimization problem. To improve the formulation accuracy of trajectory design, a multi-stage trajectory design (MSTD) is proposed. While the resultant design problem is difficult to solve directly, an iterative algorithm is developed to obtain a local optimal solution of UAV trajectory. Finally, numerical results are presented to show UAV trajectories determined by the tradeoff between C&S and the energy supply. Benefits of ISAC-based UAV scenario are highlighted by comparing the single-functional UAV scenarios.

In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink integrated sensing and communications (ISAC) system employing faster-than-Nyquist (FTN) signaling. Our proposed technique minimizes the minimum mean squared error (MMSE) for the sensed parameter estimation while ensuring the communication per-user quality-of-service through the utilization of constructive interference (CI) methodologies. While the formulated problem is non-convex in general, we tackle this issue using proficient minorization and successive convex approximation (SCA) strategies. Numerical results substantiate that our FTN-ISAC-SLP framework can increase communication throughput by up to 20% while reducing sensing MMSE by about 1 dB.

In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for ASE maximization tends to use all spatial resources for multiplexing and diversity gain, without interference nulling. In contrast, for sensing objectives, resource allocation tends to eliminate interference, especially when there are sufficient antenna resources, because inter-cell interference becomes a more dominant factor affecting sensing performance. This work first reveals the insight into spatial resource allocation for ISAC networks. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.

In this paper, we investigate the sensing-aided physical layer security (PLS) towards Integrated Sensing and Communication (ISAC) systems. A well-known limitation of PLS is the need to have information about potential eavesdroppers (Eves). The sensing functionality of ISAC offers an enabling role here, by estimating the directions of potential Eves to inform PLS. In our approach, the ISAC base station (BS) firstly emits an omnidirectional waveform to search for potential Eves’ directions by employing the combined Capon and approximate maximum likelihood (CAML) technique. Using the resulting information about potential Eves, we formulate secrecy rate expressions, which is a function of the Eves’ estimation accuracy. We then formulate a weighted optimization problem to simultaneously maximize the secrecy rate with the aid of the artificial noise (AN), and minimize the Cramér-Rao Bound (CRB) of targets’/Eves’ estimation. By taking the possible estimation errors into account, we enforce a beampattern constraint with a wide main beam covering all possible directions of Eves. This implicates that security needs to be enforced in all these directions. By improving estimation accuracy, the sensing and security functionalities provide mutual benefits, resulting in improvement of the mutual performances with every iteration of the optimization, until convergence. Our results avail of these mutual benefits and reveal the usefulness of sensing as an enabler for practical PLS.

This paper proposes an integrated sensing, navigation, and communication (ISNC) framework for safeguarding unmanned aerial vehicle (UAV)-enabled wireless networks against a mobile eavesdropping UAV (E-UAV). To cope with the mobility of the E-UAV, the proposed framework advocates the dual use of artificial noise transmitted by the information UAV (I-UAV) for simultaneous jamming and sensing to facilitate navigation and secure communication. In particular, the I-UAV communicates with legitimate downlink ground users, while avoiding potential information leakage by emitting jamming signals, and estimates the state of the E-UAV with an extended Kalman filter based on the backscattered jamming signals. Exploiting the estimated state of the E-UAV in the previous time slot, the I-UAV determines its flight planning strategy, predicts the wiretap channel, and designs its communication resource allocation policy for the next time slot. To circumvent the severe coupling between these three tasks, a divide-and-conquer approach is adopted. The online navigation design has the objective to minimize the distance between the I-UAV and a pre-defined destination point considering kinematic and geometric constraints. Subsequently, given the predicted wiretap channel, the robust resource allocation design is formulated as an optimization problem to achieve the optimal trade-off between sensing and communication in the next time slot, while taking into account the wiretap channel prediction error and the quality-of-service (QoS) requirements of secure communication. To account for the E-UAV state sensing uncertainty and the resulting wiretap channel prediction error, we employ a fully-connected neural network to model the complicated mapping between the state estimation error variance and an upper bound on the channel prediction error, which facilitates the development of a low-complexity suboptimal user scheduling and precoder design algorithm. Simulation results demonstrate the superior performance of the proposed design compared with baseline schemes and validate the benefits of integrating sensing and navigation into secure UAV communication systems. We reveal that the dual use of artificial noise can improve both sensing and jamming and that navigation is more important for improving the trade-off between sensing and communications than communication resource allocation.

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.

By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) based integrated sensing and communication (ISAC) framework has been envisioned as a key technology for future wireless networks. Most DFRC beamforming works focus on block-level precoding, which fails to exploit constructive interference. To tackle this issue, we propose symbol-level joint radar sensing and communication beamforming algorithms in this paper. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference into SLP, so as to improve the energy efficiency. To address the formulated problem, we tailor a highly parallelizable iterative algorithm, which is shown to converge to stationary points. To achieve better performance, we further propose an efficient recursive optimization algorithm. In particular, the recursive algorithm monotonously improves the performance of interest as the recursive procedure proceeds.

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the near-field communications. Different from the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, in which energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the ID sum-rate and BS transmit power. To solve this non-convex optimization problem, an efficient algorithm is proposed to obtain a suboptimal solution by leveraging the binary variable elimination and successive convex approximation methods. Numerical results demonstrate that our proposed joint design achieves substantial performance gain over other benchmark schemes.

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (DID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.

In this paper, we shed light on the performance gain of integrated sensing and communications (ISAC) from the perspective of channel correlations between radar sensing and communication (S&C), namely ISAC subspace correlation. To begin with, we consider a multi-input multi-output (MIMO) ISAC system and reveal that the optimal ISAC signal is in the subspace spanned by the transmitted steering vectors of the sensing channel and the right singular matrix of the communication channel. By leveraging this result, we study a basic ISAC scenario with a single target and a single-antenna communication user, and derive the optimal waveform covariance matrix for minimizing the estimation error under a given communication rate constraint. To quantify the integration gain of ISAC systems, we define the subspace “correlation coefficient” to characterize the coupling effect between S&C channels. Finally, numerical results are provided to validate the effectiveness of the proposed approaches.

Predictive millimeter-wave (mmWave) beamforming is a promising technique to enable low-latency and high-rate ground-air communications for cellular-connected unmanned aerial vehicles (UAVs). However, the high vulnerability of mmWave to blockages poses practical challenges to the implementation of such a technology. In this paper, we tackle the challenges by proposing a channel knowledge map (CKM)-assisted predictive beamforming approach based on the echoed joint communication and sensing signal, whereby the line-of-sight (LoS) link identification is performed via hypothesis testing using prior information provided by CKM. Depending on the identification result, extended Kalman filtering (EKF) is adopted to reliably track the target UAV. Furthermore, if the non-line-of-sight (NLoS) state is identified, the target UAV will be immediately connected to a candidate base station (BS), namely a handover will be triggered to alleviate the communication outage. The simulation results show that the proposed method can significantly enhance the UAV tracking and mmWave communication performance compared to the benchmarking schemes without using CKM or LoS identification.

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.

Sensing-assisted predictive beamforming is a promising technique for reducing the communication overhead and latency in millimeter wave (mmWave) communication systems. In this paper, we propose a robust sensing-assisted predictive beamforming scheme that performs non-line-of-sight (NLoS) identification based on the reflected joint communication and sensing signal. Specifically, the time delay, Doppler shift and reflection coefficient are estimated from the echo signal, based on which a hypothesis test problem is formulated to determine whether the echo signal is reflected by the target vehicle or obstacles. Besides, based on the estimated channel parameters, extended Kalman filtering (EKF) is employed to estimate and predict the motion parameters of the target vehicle. If the echo signal is reflected by obstacles, it implies that the LoS link between the base station (BS) and the target vehicle is blocked. In this case, the proposed scheme can adjust the communication mode and vehicle tracking mechanism accordingly to enhance link reliability. Numerical results demonstrate that the proposed predictive beamforming scheme with NLoS identification can substantially enhance the achievable communication rate as compared to the existing techniques without taking blockage into account.

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.

Orthogonal time frequency space (OTFS) modulation has shown promising application perspectives, thanks to its strong delay and Doppler resilience. Furthermore, the delay-Doppler domain channel response directly reflects the physical attributes of channel scatterers, which provides fundamentally new perspectives for channel estimation (CE) and radar sensing. The success of OTFS has stimulated various CE and equalization algorithms with promising performance. However, only few of them were validated by hardware experiments. In this paper, we develop an OTFS communication and sensing (C&S) system using software defined radio (SDR), which invokes the off-grid target sensing and minimum mean square error (MMSE) channel equalization. In particular, we design and emulate the high-mobility wireless channel with multiple scatterers (sensing targets) and conduct the channel equalization with MMSE for data detection. Moreover, we study the influence of transceiver impairments, such as in-phase and quadrature (IQ) imbalance, DC offset, and carrier frequency offsets (CFO). With real-time experiments, the results show that the addition of scatterers engenders the distortion of the DD domain signals which curtail the BER performance of the communication system and further trims the MSE of sensing parameters with the increasing number of scatterers.

Location-awareness is essential in various wireless applications. The capability of performing precise ranging is substantial in achieving high-accuracy localization. Due to the notorious ambiguity phenomenon, optimal ranging waveforms should be adaptive to the signal-to-noise ratio (SNR). In this letter, we propose to use the Ziv-Zakai bound (ZZB) as the ranging performance metric, as well as an associated waveform design algorithm having theoretical guarantee of achieving the optimal ZZB at a given SNR. Numerical results suggest that, in stark contrast to the well-known high-SNR design philosophy, the detection probability of the ranging signal becomes more important than the resolution in the low-SNR regime

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.

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data transmission, theintegrated sensing and communication(ISAC) technique enables simultaneous data collection and delivery in the physical layer. By exploiting the analog-wave addition property in a multi-access channel,over-the-air computation(AirComp) has been proposed as a communication approach that also enables function computation. The promising performances of ISAC and AirComp motivate the current work on developing a framework calledintegrated sensing, communication, and computation over-the-air(ISCCO). Two schemes are designed to supportmultiple-input-multiple-output(MIMO) ISCCO simultaneously, namely theseparated and sharedschemes. The separated scheme splits antenna array for radar sensing and AirComp, while all the antennas transmit a joint waveform for both radar sensing and AirComp in the shared scheme. The performance of radar sensing is evaluated by themean squared error(MSE) of the estimated target response matrix, while the MSE of the estimated function is adopted as the metric to evaluate the performance of the coupled communication and computation in AirComp. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The application of ISCCO on target location estimation is further demonstrated via simulation.

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain by dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only-based and ISAC kinematic-only-based technique in tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.

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.

In this paper, we consider a beampattern design problem for a dual-functional radar-communication (DFRC) system with the aid of the reconfigurable intelligent surface (RIS). The goal of the design is to meet the communication requirements of the users while the beampattern can match the ideal beampattern. We formulate the beampattern design as a non- convex optimization problem by jointly optimizing the transmit and passive beamformers. To solve the problem, three methods, namely the semidefinite relaxation (SDR)-based method, penalty- based method, and Riemannian conjugate gradient (RCG)-based method are proposed. The simulation results illustrate that: i) the radar performance can be significantly enhanced with the aid of RIS; ii) a tradeoff should be made between the communication performance and radar performance; iii) 4 control bits are sufficient to make the performance close to that of continuous phase shift.

A sensing-assisted predictive beamforming scheme for vehicle-to-infrastructure (V2I) communication is considered, which is built upon massive multi-input-multi-output (mMIMO) and millimeter wave (mmWave) techniques. In practical V2I networks, vehicles cannot be modeled as point targets in terms of the narrow beamwidth and high range resolution. Accordingly, the communication receiver (CR) may be beyond the beam even the vehicle is accurately tracked, which makes robust beam alignment and tracking challenging. We thus consider the extended target case, in which the beamwidth should be adjusted in real-time to cover the entire vehicle. Then an extended Kalman filtering (EKF) is presented to track the CR according to the resolved high-resolution geometry results. Finally, numerical results are provided to validate the effectiveness of the proposed approach.

This paper proposes an integrated sensing, jamming, and communications (ISJC) framework for securing unmanned aerial vehicle (UAV)-enabled wireless networks. The proposed framework advocates the dual use of artificial noise transmitted by an information UAV for simultaneous jamming and sensing of an eavesdropping UAV. Based on the information sensed in the previous time slot, an optimization problem for online resource allocation design is formulated to maximize the number of securely served users in the current time slot, while taking into account a tracking performance constraint and quality-of-service (QoS) requirements regarding the leakage information rate to the eavesdropper and the downlink data rate to the legitimate users. A channel correlation-based algorithm is proposed to obtain a suboptimal solution for the design problem. Simulation results demonstrate the security benefits of integrating sensing into UAV communication systems.

We consider multi-input multi-output (MIMO) dual function radar communication (DFRC) systems, and design a transmit beamforming matrix that optimizes a weighted combination of the radar estimate Cramer-Rao bound (CRB) and the communication rate. A hybrid beamforming structure is considered, with fewer RF chains than antennas, to achieve the benefits of MIMO systems while maintaining low cost. However, such a structure may have a rank-deficient beamforming matrix, resulting in degraded estimation performance. We propose antenna selection as means to ensure a full-rank beamforming matrix, and also select the communication channels so that high communication rate can be achieved. A learning approach is employed to optimally select antennas and design the corresponding beamforming matrix. By leveraging a combination of softmax neural networks, the proposed solution is able to optimize the joint performance metric for a DFRC system.

In this paper, we explore a multiple-input multiple- output (MIMO) system with orthogonal frequency division multiplexing (OFDM) transmissions and study the low peak- to-average power ratio (PAPR) MIMO-OFDM waveform design for integrated sensing and communications (ISAC). This is done by leveraging a weighted objective function on both communication and radar performance metrics under power and PAPR constraints. The formulated optimization problem can be equivalently transformed into several sub-problems which can be parallelly solved by the semi-definite relaxation (SDR) method and the optimal rank-1 solution can be obtained in general. The feasibility, effectiveness, and flexibility of the proposed low-PAPR MIMO-OFDM waveform design method are demonstrated by a range of simulations on communication sum rate, symbol error rate as well as radar beampattern and detection probability.

Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. The globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain over that of conventional edge intelligence. Simulation results are provided to verify the effectiveness of the proposed ISAC-assisted edge intelligence system. Interestingly, we find that ISAC is always beneficial, when the duration of generating a sample is more than the duration of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative. Nevertheless, we still derive a sufficient condition, under which a positive ISAC gain is feasible.

We consider an unmanned aerial vehicle (UAV) based joint radar localization and communication system, where a UAV transmits the downlink signal to a ground communication user and the transmitted signal is also exploited to localize a target coordinates. We aim to optimize the UAV path with energy constraints. We formulate the trajectory design into a weighted optimization problem, where a scalable performance trade-off between localization and communication can be achieved. An iterative algorithm is exploited then to address the trajectory design formulation. Numerical results are provided to validate the effectiveness of the proposed UAV trajectory design approaches.

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).

The space-air-ground-sea integrated network (SAGSIN) plays an important role in offering global coverage. To improve the efficient utilization of spectral and hardware resources in the SAGSIN, integrated sensing and communications (ISAC) has drawn extensive attention. Most existing ISAC works focus on terrestrial networks and cannot be straightforwardly applied in satellite systems due to the significantly different electromagnetic wave propagation properties. In this work, we investigate the application of ISAC in massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems. We first characterize the statistical wave propagation properties by considering beam squint effects. Based on this analysis, we propose a beam squint-aware ISAC technique for hybrid analog/digital massive MIMO LEO satellite systems exploiting statistical channel state information. Simulation results demonstrate that the proposed scheme can operate both the wireless communications and the target sensing simultaneously with satisfactory performance, and the beam-squint effects can be efficiently mitigated with the proposed method in typical LEO satellite systems.

We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously. Uniquely for such scenarios, the radar target is regarded as a potential eavesdropper which might surveil the information sent from the base station (BS) to communication users (CUs), that is carried by the radar probing signal. Transmit waveform and receive beamforming are jointly designed to maximize the signal-to-interference-plus-noise ratio (SINR) of the radar under the security and power budget constraints. We apply a Directional Modulation (DM) approach to exploit constructive interference (CI), where the known multiuser interference (MUI) can be exploited as a source of useful signal. Moreover, to further deteriorate the eavesdropping signal at the radar target, we utilize destructive interference (DI) by pushing the received symbols at the target towards the destructive region of the signal constellation. Our numerical results verify the effectiveness of the proposed design showing a secure transmission with enhanced performance against benchmark DFRC techniques.

In the era of big data, anonymity is recognized as an important attribute in privacy-preserving communications. The existing anonymous authentication and routing designs are applied at higher layers of networks, ignoring the fact that physical layer (PHY) also contains privacy-critical information. In this paper, we introduce the concept of PHY anonymity, and reveal that the receiver can unmask the sender’s identity by only analyzing the PHY information, i.e., the signaling patterns and the characteristics of the channel. We investigate two scenarios, where the receiver has more antennas than the sender in the strong receiver case, and vice versa in the strong sender case. For each scenario, we first investigate sender detection strategies at the receiver, and then we develop anonymous precoding to address anonymity while guaranteeing high signal-to-interference-plus-noise-ratio (SINR) for communications. In particular, an interference suppression anonymous precoder is first proposed, assisted by a dedicated transmitter-side phase equalizer for removing phase ambiguity. Afterwards, a constructive interference anonymous precoder is investigated to utilize inter-antenna interference as a beneficial element without loss of the sender’s anonymity. Simulations demonstrate that the anonymous precoders are able to preserve the sender’s anonymity and simultaneously guarantee high SINR, opening a new dimension on PHY anonymous designs.

This paper explores tailored power allocation (PA) for dual functional radar-communication (DFRC) in the vehicle-to-infrastructure (V2I) network, where a road side unit (RSU) provides both localization and communication services to multiple vehicles. Going beyond classical communications-optimal water-filling solutions, we formulate a PA optimization problem, which minimizes the summation of the Cramér-Rao bound (CRB) for multiple vehicles, subject to downlink sum-rate constraint. We prove that the problem can be solved in closed-form for given sum-rate requirement regions. Numerical results demonstrate that our approach achieves significantly lower estimation errors while improving the communication rate, as compared to the classical water-filling.

In this paper, we propose an energy-efficient radar beampattern design framework for Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) systems, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy while incurring low cost.

In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication.

In this paper, directional modulation (DM) is investigated to enhance physical layer security. Practical transmitter designs are exploited under imperfect channel state information (CSI) and hardware constraints, such as finite-resolution phase shifters (PSs) and per-antenna power budget. Tailored for the practical issues in realizing DM, a series of practical scenarios are investigated. Starting from the scenario where eavesdroppers (Eve)s' information is completely unknown, corresponding designs are proposed to optimize legitimate users (LU)s' receiving performance while randomizing the Eves' received signal. When the Eves' CSI is imperfectly known, in the second scenario, the Eves' receiving performance is further deteriorated by imposing destructive interference to the Eves. For each scenario, three algorithms are proposed under hardware constraints and imperfect CSI, i.e., one direct-mapping algorithm suitable for high/moderate number of bits in PSs, one heuristic algorithm with improved receiving performance at the cost of complexity, and one iterative-closed-form algorithm with enhanced practicality of symbol-level based DM. Simulation demonstrates that the algorithms achieve lower symbol error rate (SER) at the LUs while significantly deteriorating the Eves' SER, leading to an improved secrecy throughput over the benchmarks.

This article studies the physical layer security in a multiple-input-multiple-output (MIMO) dual-functional radar-communication (DFRC) system, which communicates with downlink cellular users and tracks radar targets simultaneously. Here, the radar targets are considered as potential eavesdroppers which might eavesdrop the information from the communication transmitter to legitimate users. To ensure the transmission secrecy, we employ artificial noise (AN) at the transmitter and formulate optimization problems by minimizing the signal-to-noise ratio (SNR) received at radar targets, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirement at legitimate users. We first consider the ideal case where both the target angle and the channel state information (CSI) are precisely known. The scenario is further extended to more general cases with target location uncertainty and CSI errors, where we propose robust optimization approaches to guarantee the worst-case performance. Accordingly, the computational complexity is analyzed for each proposed method. Our numerical results show the feasibility of the algorithms with the existence of instantaneous and statistical CSI error. In addition, the secrecy rate of secure DFRC system grows with the increasing angular interval of location uncertainty.

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

In this paper, we focus on 1-bit precoding for large-scale antenna systems in the downlink based on the concept of constructive interference (CI). By formulating the optimization problem that aims to maximize the CI effect subject to the 1-bit constraint on the transmit signals, we mathematically prove that, when relaxing the 1-bit constraint, the majority of the obtained transmit signals already satisfy the 1-bit constraint. Based on this important observation, we propose a 1-bit precoding method via a partial branch-and-bound (P-BB) approach, where the BB procedure is only performed for the entries that do not comply with the 1-bit constraint. The proposed P-BB enables the use of the BB framework in large-scale antenna scenarios, which was not applicable due to its prohibitive complexity. Numerical results demonstrate a near-optimal error rate performance for the proposed 1-bit precoding algorithm.

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

In this paper, we focus on 1-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI). For both PSK and QAM signaling, we firstly formulate the optimization problem that maximizes the CI effect subject to the requirement of the 1-bit transmit signals. We then mathematically prove that, when employing the CI formulation and relaxing the 1-bit constraint, the majority of the transmit signals already satisfy the 1-bit formulation. Building upon this important observation, we propose a 1-bit precoding approach that further improves the performance of the conventional 1-bit CI precoding via a partial branch-and-bound (P-BB) process, where the BB procedure is performed only for the entries that do not comply with the 1-bit requirement. This operation allows a significant complexity reduction compared to the fully-BB (F-BB) process, and enables the BB framework to be applicable to the complex massive MIMO scenarios. We further develop an alternative 1-bit scheme through an `Ordered Partial Sequential Update' (OPSU) process that allows an additional complexity reduction. Numerical results show that both proposed 1-bit precoding methods exhibit a significant signal-to-noise ratio (SNR) gain for the error rate performance, especially for higher-order modulations.

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.

Dual-functional radar communication (DFRC) system has recently attracted significant academic attentions as an enabling solution for realizing radar-communication spectrum sharing. During the DFRC transmission, however, the critical information could be leaked to the targets, which might be potential eavesdroppers. Therefore, the physical layer security has to be taken into consideration. In this paper, fractional programming (FP) problems are formulated to minimize the signal-to-interference-plus-noise ratio (SINR) at targets under the constraints for the SINR of legitimate users. By doing so, the secrecy rate of communication can be guaranteed. We first assume that communication CSI and the angle of the target are precisely known. After that, problem is extended to the cases with uncertainty in the target's location, which indicates that the target might appear in a certain angular interval. Finally, numerical results have been provided to validate the effectiveness of the proposed method showing that it is viable to guarantee both radar and secrecy communication performances by using the techniques we propose.

In this paper, we propose a beamforming design for dual-functional radar-communication (DFRC) systems at the mil-limeter wave (mmWave) band, where hybrid beamforming and sub-arrayed MIMO radar techniques are jointly exploited. We assume that a base station (BS) is serving a multi-antenna user equipment (UE), which in the meantime actively detects multiple targets. Given the optimal communication beam-former and the desired radar beampattern, we propose to design the analog and digital beamformers under non-convex constant-modulus (CM) and power constraints, such that the weighted summation of the communication and radar beam-forming errors is minimized. The formulated optimization problem can be decomposed into three subproblems, and is solved by the alternating minimization approach. Numerical simulations verify the feasibility of the proposed beamforming design, and show that our approach offers a favorable performance tradeoff between sensing and communication.

Distributed antenna (DA) is considered as a strong alternative to conventional centralized multiple-input multiple-output (MIMO), to provide a greener and user-centric network structure. However, physical layer (PHY) security becomes more challenging in DA systems because of the proximity to the transmitters. In this paper, we jointly optimize DA activation/deactivation and secure precoding to minimize the total power consumption, subjected to legitimate user's (LU) quality-of-service (QoS) and PHY security constraints against potential eavesdroppers (Eves). A practical scenario is considered, where channel state information (CSI) of all the nodes can only be imperfectly obtained. In the presence of infinite probabilities of CSI uncertainties, a deterministic robust based algorithm is designed to always satisfy LU's QoS requirement and address PHY security constraints against Eves. Moreover, essentially different from existing artificial noise (AN)-aided secure transmission schemes, where AN' leakage effect at LU needs to be suppressed, we utilize AN as a beneficial element at LU end while keeping it destructive at potential Eves. Simulation results verify that, the proposed algorithm incurs much lower power consumption compared to its benchmarks, thanks to the additional degrees of freedom of antenna selection and utilizing constructive AN. Last but not least, by adaptively facilitating DA activation/deactivation, the proposed algorithm addresses a user-centric network structure, which is more flexible over the conventional centralized MIMO systems.

In this paper, we focus on the coexistence between a MIMO radar and cellular base stations. We study the interfering channel estimation, where the radar is operated in the “search and track” mode, and the BS receives the interference from the radar. Unlike the conventional methods where the radar and the cellular systems fully cooperate with each other, in this paper, we consider that they are uncoordinated and the BS needs to acquire the interfering channel state information (ICSI) by exploiting the radar probing waveforms. For completeness, both the line-of-sight (LoS) and Non-LoS (NLoS) channels are considered in the coexistence scenario. By further assuming that the BS has limited a priori knowledge about the radar waveforms, we propose several hypothesis testing methods to identify the working mode of the radar, and then obtain the ICSI through a variety of channel estimation schemes. Based on the statistical theory, we analyze the theoretical performance of both the hypothesis testing and the channel estimation methods. Finally, the simulation results verify the effectiveness of our theoretical analysis and demonstrate that the BS can effectively estimate the interfering channel even with the limited information from the radar.

We study multi-user massive multiple-input single-output systems and focus on downlink transmission for PSK modulation, where the base station employs a large antenna array with low-cost 1-bit digital-to-analog converters (DACs). The direct combination of existing beamforming schemes with 1-bit DACs is shown to lead to an error floor at medium-to-high SNR regime, due to the coarse quantization of the DACs with limited precision. In this paper, based on the constructive interference, we consider both a quantized linear beamforming scheme where we analytically obtain the optimal beamforming matrix and a non-linear mapping scheme where we directly design the transmit signal vector. Due to the 1-bit quantization, the formulated optimization for the non-linear mapping scheme is shown to be non-convex. The non-convex constraints of the 1-bit DACs are first relaxed into convex, followed by an element-wise normalization to satisfy the 1-bit DAC transmission. We further propose a low-complexity symbol scaling scheme that consists of three stages, in which the quantized transmit signal on each antenna element is selected sequentially. Numerical results show that the proposed symbol scaling scheme achieves a comparable performance to the optimization-based non-linear mapping approach, while the corresponding performance-complexity tradeoff is more favorable for the proposed symbol scaling method.

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts as radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

In this paper, we propose a novel approach for the spectrum sharing between Multi-Input-Multi-Output (MIMO) radar and downlink multi-user Multi-Input- Single-Output (MU-MISO) communication system. To obtain a power-efficient beamforming at the base station (BS), we utilize the constructive multi- user interference (MUI) as a source of green signal power. The proposed beamforming design mainly focuses on two optimization problems, i.e., transmit power minimization for BS and interference minimization for radar, subject to given performance requirements of the two systems. We further consider the impact of the proposed methods on radar, where the detection probability for MIMO radar in the presence of the interference from BS is analytically derived, and important trade-offs are revealed. Numerical results show that the proposed approach outperforms the conventional beamforming designs by achieving a significant performance gain under the discussed coexistence scenario.

In this letter, we propose a novel manifold-based algorithm to solve the constant envelope (CE) precoding problem with interference exploitation. For a given power budget, we design the precoded symbols subject to the CE constraints, such that the constructive effect of the multiuser interference is maximized. While the objective function for the original problem is not complex differentiable, we consider the smooth approximation of its real representation, and map it onto a Riemannian manifold. By using the Riemmanian conjugate gradient algorithm, a local minimizer can be efficiently found. The complexity of the algorithm is analytically derived in terms of floating-points operations (flops) per iteration. Simulations show that the proposed algorithm outperforms the conventional methods on both symbol error rate and computational complexity.