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

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

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

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

In our original paper, we omitted a key step involving the transformation of variableR̃ISACk,j[n]. In this work, we recognize that our initial conclusion, stating that "Hk,jis a negative definite matrix in the feasible region" requires additional clarification and adjustments. To ensure the correctness of the work, we provide the necessary modifications and detailed discussions in this revised version.

This paper studies the use of multiple planar fluid antennas at a full-duplex base station (BS) for integrated sensing and communication (ISAC). In this model, the BS communicates with a downlink user, an uplink user, and performs target sensing simultaneously. Our objective is to maximize the communication sum-rate of the up and downlink users while meeting the sensing and power constraints. Given that the problem is non-convex, we first reformulate the problem using the fractional programming (FP) framework. After that, we iteratively optimize the beamforming vectors of the BS, the uplink transmit power from the user, and the antenna positions of both transmit and receive fluid antenna systems (FASs) at the BS. In particular, the transmit and receive beamforming vectors are optimized by utilizing the majorization-minimization (MM) framework, and a closed-form solution for the uplink transmit power is derived. To optimize the BS antenna positions, we transform the problems into convex quadratically constrained quadratic programs (QCQP) by using Taylor series expansion. The subproblems can then be solved based on the successive convex approximation (SCA). Simulation results show that FAS can greatly improve the communication rate compared to the traditional fixed-position antenna (FPA) system.

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

Unmanned aerial vehicles (UAVs) have attracted plenty of attention due to their high flexibility and enhanced communication ability. However, the limited coverage and energy of UAVs make it difficult to provide timely wireless service for large-scale sensor networks, which also exist in multiple UAVs. To this end, the advanced collaboration mechanism of UAVs urgently needs to be designed. In this paper, we propose a multi-UAV collaborative scheme for seamless data collection and transmission, where UA s are dispatched to collection points (CPs) to collect and transmit the time-critical data to the ground base station (BS) simultaneously through the cooperative backhaul link. Specifically, the mission completion time is minimized by optimizing the trajectories, task allocation, collection time scheduling, and transmission topology of UAVs while ensuring backhaul link to the BS. However, the formulated problem is non-convex and challenging to solve directly. To tackle this problem, the CP locations and transmission topology of UAVs are obtained by sensor node (SN) clustering and region division. Next, the transmission connectivity condition between UAVs is derived to facilitate the trajectory discretization and thus reduce the dimensions of variables. This simplifies the problem to optimizing the UAV hovering locations, hovering time, and CP serving sequence. Then, we propose a point-matching-based trajectory planning algorithm to solve the problem efficiently. The simulation results show that the proposed scheme achieves significant performance gains over the two benchmarks.

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

Low-latency and high-precision vehicle localization plays a significant role in enhancing traffic safety and improving traffic management for intelligent transportation. However, in complex road environments, the low latency and high precision requirements could not always be fulfilled due to the high complexity of localization computation. To tackle this issue, we propose a road-aware localization mechanism in heterogeneous networks (HetNet) of the mobile communication system, which enables real-time acquisition of vehicular position information, including the vehicular current road, segment within the road, and coordinates. By employing this multi-scale localization approach, the computational complexity can be greatly reduced while ensuring accurate positioning. Specifically, to reduce positioning search complexity and ensure positioning precision, roads are partitioned into low-dimensional segments with unequal lengths by the proposed singular point (SP) segmentation method. To reduce feature-matching complexity, distinctive salient features (SFs) are extracted sparsely representing roads and segments, which can eliminate redundant features while maximizing the feature information gain. The Cramér-Rao Lower Bound (CRLB) of vehicle positioning errors is derived to verify the positioning accuracy improvement brought from the segment partition and SF extraction. Additionally, through SF matching by integrating the inclusion and adjacency position relationships, a multi-scale vehicle localization (MSVL) algorithm is proposed to identify vehicular road signal patterns and determine the real-time segment and coordinates. Simulation results show that the proposed multi-scale localization mechanism can achieve lower latency and high precision compared to the benchmark schemes.

Seafloor anchor nodes are a crucial component of Internet of Underwater Things (IoUT), which is designed to provide surface and underwater users with positioning, navigation, timing and communication (PNTC) services. Traditional anchor node positioning typically uses cross or circular shaped deployment. However, accurate positioning of underwater anchor nodes becomes a challenge task due to the nonuniform distribution of underwater sound speed, which is seldom considered in the literature. Due to the complexity of distance measurement caused by curved sound lines, the optimization of reference node deployment based on curved sound lines is worthy of further research. This article focuses on the optimal reference node deployment strategies for time-of-arrival (TOA) localization of anchor nodes in IoUT networks in 3-D underwater space. We adopt the criterion that minimizing the trace of the inverse Fisher information matrix (FIM) to facilitate the optimal reference nodes deployment with Gaussian measurement noise, the magnitude of which is positive related to the real signal propagation path. It is proved that the optimal reference node deployment position exhibits central symmetry with respect to the target, which greatly simplifies the analysis of the optimal node deployment position. Then, a semi-closed form of the optimal pitch angle is derived to determine the optimal geometries. To demonstrate the findings in this article, we conducted both simulations and real-world experiments on underwater anchor node positioning. Both the simulation and experiment results demonstrate that the proposed deployment scheme significantly improve the positioning accuracy.

With the rapid expansion of autonomous vehicles and entertainment applications, video traffic in the Internet of Vehicles (IoV) faces exponential growth. This surge in video demand presents significant challenges for effective pre-caching strategies, particularly due to high vehicle mobility and heterogeneous dwell times at edge nodes caused by varying speeds. In this paper, we propose an efficient adaptive video segment pre-caching scheme (AVSC) for the IoV, addressing varying travel durations of vehicles on road. Specifically, we develop two video evaluation models to balance the popularity of cached video segments with the fidelity of their distribution across the entire video, ensuring temporal continuity. Then, a multi-objective optimization problem is formulated to jointly maximize highlight entropy and segment distribution fidelity. By leveraging the time-frequency characteristics of the wavelet transform, initial segment candidates are identified by detecting significant changes in the time series of chunk popularity (derived from analyzing frame-level popularity). This approach reduces the search space and computation time for subsequent segment selection. Based on the initial segment candidates, the highlight-direction optimal algorithm is proposed to iteratively identify highlight candidates by improving highlight entropy. For Pareto-optimal solutions, a caching-segment adjustment algorithm based on neighborhood search is proposed to determine the final cached video segments. Theoretical guarantees are provided for the identification process. Furthermore, the adjustment algorithm is proven to detect the maximal improvement direction of distribution fidelity, enhancing convergence speed. Simulations on real-world video datasets demonstrate the effectiveness of the proposed AVSC.

The conventional direction-of-arrival (DoA) estimation approaches are effective only when the line-of-sight (LoS) link is available. In non-line-of-sight (NLoS) scenarios, it is challenging to effectively obtain the directional information of targets due to the uncontrollability of signal reflections from NLoS links. To handle this issue, a novel reconfigurable intelligent surface (RIS)-enabled gridless DoA estimation system for NLoS scenarios is proposed, where the RIS establishes a virtual LoS link between the base station and targets. First, considering the minable statistics of the signal, the RIS-enabled signal model in the covariance domain with a limited number of receiving antennas is proposed to help reduce resource consumption. Next, we estimate the noise variance by constraining the Frobenius norm of the measurement error matrix to enhance the robustness to noise. Then, we reconstruct the Hermitian Toeplitz matrix by addressing the atom norm minimization (ANM) problem on the covariance-noiseless matrix. To reduce the computation, an efficient iterative approach is designed via the alternating direction method of multipliers . Furthermore, this system’s Cramér–Rao lower bound is derived, which is further exploited as the DoA estimation’s reference bound. Numerical experiments validate the superiority of the proposed system over the benchmark in terms of computational efficiency and estimation precision.

Unmanned aerial vehicles (UAVs) have received plenty of attention due to their high flexibility and enhanced communication ability, nonetheless, the limited onboard energy restricts UAVs’ application on persistent data collection missions in large areas. In this paper, we propose a rechargeable UAV-assisted periodic data collection scheme, where a UAV is dispatched to periodically collect data from sensor nodes (SNs) in the mission area and charged by a wireless charging platform. Specifically, the periodic data collection completion time is minimized by optimizing the UAV trajectory to reach the optimal balance among the collection time, flight time, and recharging time. The formulated problem is non-convex and difficult to solve directly. To tackle this problem, we divide the main problem into two sub-problems and address them by leveraging successive convex approximation (SCA), bisection search, and heuristic methods. Then, we propose a periodic trajectory optimization algorithm to iteratively solve the two sub-problems to minimize the completion time. Furthermore, to deal with the dynamics of SNs, we propose a low-complexity trajectory adjustment strategy, where the trajectory can be maintained or adjusted locally at the SNs change, which significantly mitigates the computation cost of re-optimization. The simulation results show the superiority and robustness of the proposed scheme and the completion time is on average 39% and 33% lower than the two benchmarks, respectively.

To support the development of air-ground integrated sensing and communication (ISAC), network-level performance analysis is needed for providing an essential guide on the network design. Following the widely adopted orthogonal frequency-division multiplexing (OFDM) technology in existing wireless systems, a cooperative air-ground wireless network based on OFDM-ISAC is introduced in this paper, where the ISAC-enabled base stations (BSs) following the two-dimensional homogeneous Poisson point process (HPPP) distribution serve the terrestrial communication users while sensing the aerial targets. In particular, cooperative beamforming schemes are designed for mitigating the interference among ISAC BSs. First, we analyze the communication as well as sensing performances in terms of different metrics including area communication coverage probability, area communication spectral efficiency, area radar detection coverage probability, and average Cramér-Rao Bound. Simulation results are then presented to validate the theoretical analysis and illustrate the effects of key system parameters on the network performance. It is observed that both the communication and sensing (C&S) performances depend on the BS density and height, while the sensing performance also depends on the height of sensing target together with the numbers of OFDM subcarriers and symbols. Moreover, there exists a tradeoff between the C&S performances with respect to the BS density and height. The results of this paper provide useful guidance to the design and implementation of air-ground wireless network for harnessing the dual benefits of ISAC.

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

Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the derived Cramér-Rao lower bound (CRLB) expression of the localization accuracy unveils a significant finding: DeployingNISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the$\ln ^{2}N$scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of$N^{2}$achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance. Simulation results demonstrate that compared to the conventional time-sharing scheme or a non-cooperative scheme, the proposed cooperative ISAC scheme can effectively improve the average data rate and reduce the CRLB, hence striking an improved S&C performance tradeoff at the network level.

Vehicle localization is essential for intelligent trans-portation. However, achieving low-latency vehicle localization without sacrificing precision is challenging. In this paper, we propose a road-aware localization mechanism in heterogeneous networks (HetNet), where distinct features of HetNet signals are extracted for two-spatial-scale position mapping, enabling low latency with high precision. Specifically, we propose a sequence segmentation method to extract the low-dimensional positioning space on two scales. To represent roads and sub-segments according to HetNet signals, we propose a salient feature ex-traction method to eliminate redundant features and retain distinct features, thereby reducing feature-matching complexity and improving representation accuracy. Based on the extracted salient features, a two-spatial-scale localization algorithm is designed through salient feature matching, which can achieve low-latency road-aware localization. Furthermore, high-precision positioning is achieved by coordinate mapping based on curve fitting. Simulation results show that our mechanism can provide a low-latency and high-precision positioning service compared to the benchmark schemes.

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.

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cramér-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem. Then, we prove that the transformed problem is a monotonic optimization, which can be optimally solved by the Polyblock-based algorithm. Moreover, based on derived insights for the single-target case, we propose a heuristic algorithm to optimize the target association and time allocation for the multi-target case. Furthermore, we provide useful guidance for the practical implementation of the proposed localization scheme by theoretically analyzing the relationship between time slots, BSs, and targets. Simulation results verify that deploying IRS on vehicles and effective phase shift design can effectively improve the resolution ability of multi-vehicle positioning and reduce the requirements of the number of BSs.

Under numerous computation requirements in intelligent traffic, platoons comprised of several connected vehicles are expected to centralize vehicular computation resources, which can provide computing services for surrounding mobile users and thus facilitate the deployment of vehicular edge computing (VEC). Nevertheless, the computing resources in multi-platoon are distributed unevenly, making platoons’ resource allocation highly selective, especially for randomly distributed mobile users. Moreover, existing works mainly focus on single-platoon-assisted VEC, which lacks resource coordination among platoons and may result in resource imbalance. Thus, through coordinating computing resource allocation in platoons and base stations (BSs), a coordinated computing resource allocation scheme is proposed in this paper to maximize the utilities of mobile users. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. To address this issue, the efficiency-maximized optimal computing resources allocated from platoons to users are derived in a semi-closed form. Then, to decouple the variables in allocation coordination, the efficiency maximization problem is proven to be equivalent to a low-complexity resource allocation problem oriented for single users. Based on the above results, two algorithms are proposed to convert the NP-hard problem into convex ones, 1) through efficiency-maximized task offloading and backtracking iteratively, a profit efficiency backtracking algorithm is proposed to coordinate computing resource allocation among platoons, and 2) heterogeneous profit efficiency algorithm is proposed to solve the primal problem, where a partition ratio-based efficiency maximization computing resource allocation problem is optimized in heterogeneous edges. Extensive simulation results show that our proposed scheme can improve resource allocation efficiency, task success rates, and service continuity over benchmark schemes.

With the emerging environment-aware applications, ubiquitous sensing is expected to play a key role in future networks. In this paper, we study a 3-dimensional (3D) multi-target localization system where multiple intelligent reflecting surfaces (IRSs) are applied to create virtual line-of-sight (LoS) links that bypass the base station (BS) and targets. To fully unveil the fundamental limit of IRS for sensing, we first study a single-target-single-IRS case and propose a novel two-stage localization protocol by controlling the on/off state of IRS. To be specific, in the IRS-off stage, we derive the Cramér-Rao bound (CRB) of the azimuth/elevation direction-of-arrival (DoA) of the BS-target link and design a DoA estimator based on the MUSIC algorithm. In the IRS-on stage, the CRB of the azimuth/elevation DoA of the IRS-target link is derived and a simple DoA estimator based on the on-grid IRS beam scanning method is proposed. Particularly, the impact of echo signals reflected by IRS from different paths on sensing performance is analyzed and we show that only the signal passing through the BS-IRS-target link is required while that of the BS-target link can be neglected provided that the number of BS antennas is sufficiently large and the dedicated sensing beam at the BS is aligned with the departure transmit array response from the BS to the IRS. Moreover, we prove that the single-beam of the IRS is not capable of sensing, but it can be achieved with multi-beam. Based on the two obtained DoAs, the 3D single-target location is constructed. We then extend to the multi-target-multi-IRS case and propose an IRS-adaptive sensing protocol by controlling the on/off state of multiple IRSs, and a multi-target localization algorithm is developed. Simulation results demonstrate the effectiveness of our scheme and show that sub-meter-level positioning accuracy can be achieved.

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.

Vehicle-to-everything (V2X) communication is expected to support many promising applications in next-generation wireless networks. The recent development of integrated sensing and communications (ISAC) technology offers new opportunities to meet the stringent sensing and communication (S&C) requirements in V2X networks. However, considering the relatively small radar cross section (RCS) of the vehicles and the limited transmit power of the road site units (RSUs), the power of echoes may be too weak to achieve effective target detection and tracking. To handle this issue, we propose a novel sensing-assisted communication scheme by employing an intelligent omni-surface (IOS) on the surface of the vehicle. First, a two-phase ISAC protocol, including the S&C phase and the communication-only phase, was presented to maximize the throughput by jointly optimizing the IOS phase shifts and the sensing duration. Then, we derive a closed-form expression of the achievable rate which achieves a good approximation. Furthermore, a sufficient and necessary condition for the existence of the S&C phase is derived to provide useful insights for practical system design. Simulation results demonstrate the effectiveness of the proposed sensing-assisted communication scheme in achieving a high throughput with low transmit power requirements.

Due to the features of on-demand deployment and flexible observation, unmanned aerial vehicles (UAVs) are promising for serving as the next-generation aerial sensors by using their onboard sensing devices. Compared to a single UAV with limited sensing coverage and communication capability, multi-UAV cooperation is able to realize more effective sensing and transmission (S&T) services, and delivers the sensory data to the control center more efficiently for further analysis. Nevertheless, most existing works on multi-UAV sensing mainly focus on mutually exclusive task allocation and independent data transmission, which did not fully exploit the benefit of multi-UAV sensing and communication. Motivated by this, we propose a novel multi-UAV cooperative S&T scheme with overlapped sensing task allocation. Although overlapped task allocation may sound counter-intuitive, it can actually foster cooperative transmission among multiple UAVs through a virtual multi-antenna system and thus reduce the overall sensing mission completion time. To obtain the optimal task allocation and transmit power of the proposed scheme, a mission completion time minimization problem is formulated. To solve this problem, a condition that specifies whether it is necessary for the UAVs to perform overlapped sensing is derived. For the cases of overlapped sensing, this time minimization problem is transformed into a monotonic optimization and is solved by the generic Polyblock algorithm. To efficiently evaluate the mission completion time in each iteration of the Polyblock algorithm, new auxiliary variables are introduced to decouple the otherwise sophisticated joint optimization of transmission time and power. While for the degenerated case of non-overlapped sensing, the closed-form expression of the optimal transmission time is derived, which provides insights into the optimal solution and facilitates the design of an efficient double-loop binary search algorithm to optimally solve the degenerated problem. Finally, simulation results demonstrate that the proposed scheme significantly reduces the mission completion time over benchmark schemes.

Driven by unmanned aerial vehicle (UAV)’s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem with location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

Besides improving communication performance, intelligent reflecting surfaces (IRSs) are also promising enablers for achieving larger sensing coverage and enhanced sensing quality. Nevertheless, in the absence of a direct path between the base station (BS) and the targets, multi-target sensing is generally very difficult, since IRSs are incapable of proactively transmitting sensing beams or analyzing target information. Moreover, the echoes of different targets reflected via the IRS-assisted virtual links arrive at the BS from the same direction. In this paper, we study a wireless system comprising a multi-antenna BS and an IRS for multi-target sensing, where the beamforming vector and the IRS phase shifts are jointly optimized to improve the sensing performance. To meet the different sensing requirements, such as a minimum received power and a minimum sensing frequency, we propose three novel IRS-assisted sensing schemes: Time division (TD) sensing, signature sequence (SS) sensing, and hybrid TD-SS sensing. For TD sensing, the sensing tasks are performed in sequence over time. In contrast, the novel SS sensing scheme senses all targets simultaneously and establishes a relationship between the target directions and SSs. To strike a flexible balance between the beam pattern gain and sensing efficiency, we also propose a general hybrid TD-SS sensing scheme with target grouping, where targets belonging to the same group are sensed simultaneously via SS sensing, while the targets in different groups are assigned to orthogonal time slots. By controlling the number of groups, hybrid TD-SS sensing can provide a more flexible balance between beam pattern gain and sensing frequency. Moreover, we propose a two-layer penalty-based algorithm to solve the challenging non-convex optimization problem for the joint design of the BS beamformers, IRS phase shifts, and target grouping. Simulation results demonstrate the effectiveness of the proposed hybrid scheme in achieving a flexible trade-off between beam pattern gain and sensing frequency. Our results also reveal that the power leakage in unintended directions is larger for tighter interference constraints.

Sensor data search is an imminent component for the booming Internet of Things (IoT). Current proposals would refer to either keyword or spatial location, then the matched sensor data should be manually selected by requesters. However, the selection is arduous as there are massive connected devices. To this end, we propose a combinatorial-oriented feedback (COF) mechanism to guarantee the reliability and accessibility of feedback results via enabling an intuitive exhibition of aggregated sensor data. Two critical issues are addressed in this article: 1) sensor data combination and 2) aggregated results ranking. To facilitate the combination process, we first propose two decisive factors for aggregated result evaluation. The multiple sensor-data combinatorial (MSC) problem is converted into a multiobjective optimization model. To balance the tradeoff between competing quality metrics, we introduce Pareto sensor set (PSS) as an optimal solution for MSC problem, and devise the elitist directive breeding (EDB) method to get PSS solutions. In order to speed up the search efficiency and improve the feedback recall, we then develop the fast EDB (FEDB) algorithm, which is able to return top-${k}$ranked results according to different searching requirements. To demonstrate the effectiveness of COF mechanism, we conduct extensive simulations based on a virtual mobile sensing scenario. The results show that our proposed COF mechanism outperforms the state-of-the-art solutions significantly in terms of search efficiency and feedback quality, and specially, FEDB responds quite faster than EDB under the command of top-${k}$ranking.