Dual-Function Radar and Communication (DFRC) system with full-duplex (FD) sensing and half-duplex (HD) communication is regarded as a promising way to improve safety and efficiency in vehicular networks, which have been widely used in vehicular networks. To achieve higher communication capacity, this paper extends the beamforming design of full-duplex sensing and communication DFRC systems to general scenarios within vehicular networks. Compared to the fully-connected hybrid beamforming architecture, the partially-connected hybrid beamforming architecture holds great potential of reducing hardware costs, thereby facilitating practical implementation in real-world wireless systems. However, the unique block-diagonal structure of its analog beamforming matrix introduces additional design challenges. This paper investigates an efficient partially-connected hybrid beamforming algorithm for FD-DFRC multi-input multi-output (MIMO) systems. Specifically, we formulate a multi-objective optimization problem to jointly optimize the dual-functional transmit beamformers for information receiving vehicles (IRVs) and the base station (BS), while also optimizing the received beamformers for information transmission vehicles (ITVs) at the full-duplex vehicle terminal (FD-VT). In particular, we decompose the original problem of maximizing the sum rate into several subproblems. We design an efficient algorithm based on lower bound maximization, Riemannian manifold optimization methods, and the alternating direction multiplier method (ADMM) algorithm. Numerical simulations demonstrate that the proposed scheme effectively optimizes the sum rate while maintaining radar performance thresholds, requiring fewer radio frequency chains, and adhering to energy offloading constraints.

Temporal knowledge graph (TKG) reasoning involves inferring future unknown facts based on historical data. Current approaches to temporal reasoning can be broadly categorized into two main paradigms: embedding-based methods and symbolic methods. While embedding-based methods excel at capturing time by representing temporal facts as vector, symbolic methods exploit temporal dependencies using techniques such as random walks for inference purposes. However, existing methods often fail to fully exploit both the inherent time and intricate temporal relationship patterns simultaneously. To address this limitation, we propose Temporal neural probabilistic logic learning (TNPLL), an innovative framework that seamlessly integrates symbolic logic with neural embeddings for robust temporal reasoning. Our approach incorporates two key components, a set of temporal logic rules equipped with explicit temporal relationships and a scoring module implemented through a novel temporal memory network architecture. The proposed method effectively combines time and temporal relationship patterns to predict future facts. We conducted experiments on several benchmark datasets, demonstrating that TNPLL achieves improved performance while fully leveraging time information. Specifically, our framework excels in scenarios where prior knowledge is available, but data samples are sparse. The experimental outcomes show that TNPLL outperforms state-of-the-art models in such cases.

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.

Integrated sensing and communication (ISAC) has been recognized as a key technology in sixth-generation wireless networks, and the additional spatial degrees of freedom obtained by movable antenna (MA) technology can significantly improve the performance of ISAC systems. This paper considers an ISAC-assisted vehicle-to-infrastructure (V2I) network, where extended kalman filter-based prediction is combined with real-time optimization to jointly optimize transmit antenna positions and beamforming and power allocation vectors in dynamic environments. We propose two algorithms: a preprocessing-schur complement-projected gradient ascent algorithm for scenarios without sensing quality of service (QoS) constraints, which explores the potential range of sensing performance to provide reference and warm-starting for subsequent constrained optimization; and a heuristic reflective projected dynamic particle swarm optimization algorithm for sensing QoS-constrained scenarios, which achieves substantial performance gains under non-convex constraints with a small number of iterations. Simulation results demonstrate that these approaches enhance both the communication sum-rate and the lower of the Cram´er-Rao lower bound of motion parameter estimation, validating the effectiveness of MA-assisted beamforming in dynamic V2I ISAC networks.

Unmanned aerial vehicles (UAVs) are poised for explosive growth in the low-altitude economy, causing spectrum congestion and posing a challenge to airspace regulation. Although integrated sensing and communication (ISAC) enables simultaneous communication and sensing, alleviating the spectrum shortage, the capability of one single base station (BS) is generally limited. Therefore, a multi-BS cooperative ISAC system is developed to perceive the status of UAVs at the cell edge. Multiple BSs share the same time-frequency resources and adopt a time-division scheme to avoid mutual interference between communication and sensing functionalities. Specifically, the frame structure of the communication system is modified to accommodate the sensing functionality. A robust interference nulling based beam pattern is first proposed to prevent the line-of-sight (LoS) interference between BSs from overrunning the dynamic range of the analog-to-digital converter (ADC). Moreover, we designed a space-time block codec-based orthogonal frequency division multiplexing (OFDM) to separate echo signals originating from different BSs, which transforms the inter-BS reflected interference into bistatic sensing signals. Furthermore, a data-level fusion method based on the signal-to-interference-plus-noise ratio (SINR) of the range profile is applied to improve the positioning accuracy. The numerical results reveal that the proposed beam pattern greatly avoids LoS interference. The echo signals originating from neighboring BSs can assist in target detection and angle of arrival (AoA) estimation. Compared to soft fusion and single-BS schemes, the proposed fusion method enhances positioning precision by an order of magnitude, and is practically feasible even in the presence of clock synchronization errors.

This paper studies a wireless control system at network edge, in which a base station (BS) wirelessly coordinates the closed-loop control of multiple subsystems each consisting of a plant, a sensor, and an actuator. In this system, the BS first collects the state information from the sensors of plants, then processes the information via edge computing, and finally sends the obtained command signals back to the actuators for controlling the plants. In particular, we consider the hyper-reliable and low-latency communications (HRLLC) for the state and command signal transmission, by using the rate formulas based on short-packet communication. Under this setup, we first present a time-division-multiple-access (TDMA) protocol for coordinating the sensing, communication, and computation among the multiple plants. Then, we jointly optimize the task scheduling as well as the communication and computation resource allocations to minimize the closed-loop control latency while ensuring the stability of the multiple control subsystems. The considered problem is a highly non-convex combinatorial optimization problem that is difficult to solve. To resolve this issue, we present efficient algorithms by first optimizing the communication and computation resource allocations under given task scheduling via the techniques of alternating optimization and successive convex approximation, and then designing the task scheduling based on the exhaustive search or the low-complexity flow-shop scheduling. Numerical results show that the proposed joint resource allocation design with exhaustive search based task scheduling significantly outperforms other benchmark schemes without such joint optimization, and the proposed low-complexity task scheduling based on flow-shop scheduling achieves performance close to the upper bound by exhaustive search.

Emerging applications such as low-altitude economy and intelligent transportation hold the promise of significant economic and social benefits, requiring the support of technologies that integrate robust communication with precise sensing. Integrated sensing and communication (ISAC), as a technology enabled seamless connection between communication and sensing, is regarded a core enabling technology for these applications. However, the accuracy of single-node sensing in ISAC systems is limited, prompting the emergence of multi-node cooperative sensing. In multi-node cooperative sensing, the synchronization error limits the sensing accuracy, which can be mitigated by the architecture of cell-free massive multi-input multi-output (CF-mMIMO), whose fiber-optic interconnections ensure high synchronization accuracy. However, the multi-node cooperative sensing in CF-mMIMO ISAC systems faces the following challenges: 1) The joint optimization of placement and resource allocation of distributed access points (APs) to improve the sensing performance in multi-target detection scenario is difficult; 2) The fusion of the sensing information from distributed APs with multi-view discrepancies is difficult. To address these challenges, this paper proposes a joint placement and antenna resource optimization scheme for distributed APs to minimize the sensing Cramér-Rao bound for targets’ parameters within the area of interest. Then, a symbol-level fusion-based multi-dynamic target sensing (SL-MDTS) scheme is provided, effectively fusing sensing information from multiple APs. The simulation results validate the effectiveness of the joint optimization scheme and the superiority of the SL-MDTS scheme. Compared to state-of-the-art grid-based symbol-level sensing information fusion schemes, the proposed SL-MDTS scheme improves the accuracy of localization and velocity estimation by 41.8% and 38.7%, respectively.

A near-field motion parameter estimation method is proposed. In contrast to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cramér-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications.

With the rapid development of the low-altitude economy, the widespread deployment of unmanned aerial vehicles (UAVs) necessitates effective sensing technologies for airspace monitoring. Integrated sensing and communication (ISAC) enables mobile communication base stations (BSs) to function as sensing nodes, providing a promising solution for UAV detection. Accurate identification of UAVs often relies on the extraction of distinctive micro-Doppler signatures generated by their rotating blades. However, in urban environments, these signatures are frequently obscured due to low signal-to-noise ratio (SNR) and strong dynamic interference from vehicles, pedestrians, and, in particular, birds. To address this challenge, this paper proposes a robust micro-Doppler feature extraction method based on multicarrier integration and a rotor micro-Doppler null space pursuit (rmD-NSP) algorithm. In one real-world scenario, both a bird and a UAV appeared within the same range cell, with the UAV’s micro-Doppler signals heavily masked by the bird’s strong reflections. After applying the proposed algorithm, distinct micro-Doppler features are successfully extracted, revealing approximately eight rotor blade flashes of the UAV within a 0.1s interval and two wingbeat cycles of the bird within the 0.5s observation window.

A subarray-based near-field joint location and velocity estimation framework is proposed for sensing a moving target using extremely large-scale antenna arrays. To tackle the intricate near-field non-linear phase, the piecewise-far-field channel model is adopted, approximating the near-field channel by partitioning the transmit and receive arrays into subarrays and applying the near-field assumption between subarrays and the far-field assumption within each subarray. Based on this model, the complex near-field estimation problem can be transformed into a far-field joint multiple bistatic radar parameter estimation problem, enabling separable location and velocity estimation. An efficient three-stage algorithm is developed, exploiting joint sparsity across transmit-receive subarray pairs. In the first stage, a mixed-norm minimization method is employed to obtain coarse estimates of the location and complex channel gain, which are refined using the gradient descent method in the second stage. Finally, the velocity is estimated using the multiple signal classification spectrum estimation method, based on the refined location estimate. Simulation results demonstrate the effectiveness of the proposed framework and reveal a trade-off in system design: location estimation accuracy improves with increased subarray size, while velocity estimation benefits from a greater number of smaller subarrays.

The increasing demand for delay-sensitive services in industrial manufacturing, the Internet of Vehicles, and smart logistics imposes stringent delay requirements on intelligent machine (IM) networks. To reduce latency, short packet transmissions are widely used. However, their impact on network delay performance remains underexplored, particularly in large-scale deployments prone to packet collisions and queuing congestion. This paper develops a theoretical framework for modeling downlink communication and derives analytical expressions for three key delay metrics: transmission success probability, expected delay, and delay jitter. By incorporating finite blocklength constraints, we accurately characterize the effects of IM density and packet length on delay performance. Simulation results validate our model, offering valuable insights for optimizing IM network design and improving real-time communication efficiency.

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (MPC) from enemy into friend is a viable and mutually beneficial option. In this paper, we preliminarily explore the MPC-aided ISAC signal processing and apply a space-time code to improve the ISAC performance. Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment. Simulation results demonstrate that the proposed SFMC scheme has more robust localization performance with higher accuracy, compared with the existing state-of-the-art schemes. The proposed SFMC would benefit highly reliable communication and sub-meter level localization in rich multipath scenarios.

A trade-off between instantaneous power variance (IPV) and ranging sidelobe was found when choosing the orthogonal signal base for integrated sensing and communication (ISAC) waveform. Recent study shows that the orthogonal frequency division multiplexing (OFDM) waveform achieves a lowest ranging sidelobe while single-carrier (SC) waveform achieves the highest under orthogonal signal bases. In this paper, we use IPV as a substitute metric for peak-to-average ratio (PAPR) to measure nonlinear power amplifier distortion and prove that SC waveform has the lowest PAPR while OFDM has highest. We give an efficient trade-off design through partial DFT-spread OFDM and prove that the interleaved DFT-spread OFDM achieves the Pareto optimal of this kind. We also utilize the metric in multi-antenna precoding design in order to reduce the IPV in each antenna. Simulation results are given that verify our analysis.

Recent years have witnessed an increasing number of mobile devices, posing a more diversified demand for device localization solutions. Existing methods can locate connected devices but fail to address the spatial proximity between devices lacking direct communication links. This limitation impedes numerous emerging applications, such as implicit control of IoT device and proximity-based autonomous aerial vehicles scheduling. In response to this technical challenge, we introduce RF-Prox, the pioneering system designed for the proximity estimation of nondirectly connected devices. RF-Prox determines the proximity between devices by extracting and analyzing the spatiotemporal correlation between two signals. RF-Prox introduces a multiresolution spatiotemporal encoder (MRSTE) that extracts multiscale features from complex-valued wireless signals, capturing both spatial and dynamic temporal characteristics. Additionally, the proximity metric adaptation network (PMAN) bridges the gap between high-dimensional signal characteristics and physical proximity. To enhance scalability, we leverage a transfer learning framework, significantly reducing the need for extensive data collection and retraining. Extensive experiments demonstrate RF-Prox’s outstanding performance across Wi-Fi and cellular networks, achieving fine-tuned accuracy rates of 98.6% indoors and 91.3% outdoors. Even without fine-tuning, the pretrained model achieves strong zero-shot performance, showcasing its exceptional performance in both proximity estimation accuracy and domain generalizability.

Emerging applications, such as autonomous driving and Internet of Things (IoT) services put forward the demand for simultaneous sensing and communication functions in the same system. Integrated sensing and communication (ISAC) has the potential to meet the demands of ubiquitous communication and high-precision sensing due to the advantages of spectrum and hardware resource sharing, as well as the mutual enhancement of sensing and communication. However, the ISAC system faces severe interference requiring effective interference suppression, avoidance, and exploitation techniques. This article provides a comprehensive survey on the interference management techniques in ISAC systems, involving network architecture, system design, signal processing, and resource allocation. We first review the channel modeling and performance metrics of the ISAC system. Then, the methods for managing self-interference (SI), mutual interference (MI), and clutter in a single base station (BS) system are summarized, including interference suppression, interference avoidance, and interference exploitation methods. Furthermore, cooperative interference management methods are studied to address the cross-link interference (CLI) in a coordinated multipoint ISAC (CoMP-ISAC) system. Finally, future trends are revealed. This article may provide a reference for the study of interference management in ISAC systems.

Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communication channels. Therefore, ISAC channel modeling serves as a fundamental cornerstone for evaluating and optimizing ISAC systems. This article provides a comprehensive survey on the ISAC channel modeling methods. Furthermore, the methods of target radar cross section (RCS) modeling and clutter RCS modeling are summarized. Finally, we discuss the future research trends related to ISAC channel modeling in various scenarios.

In the edge learning process of B5G Internet of Things (IoT) systems, the allocation strategy of edge sensors will directly affect the learning results of the system. This article proposes multiple methods to allocate edge devices that can learn and predict the usage of spectrum data, aiming to improve the efficiency and fairness of edge learning. We design an efficient edge device allocation strategy to enhance the edge learning efficiency and propose a metric called ineffective transmission parameter (ITP) to evaluate its performance. To solve the optimization problem, mathematical analysis is performed and closed-form expressions are obtained. The scenarios considered include: devices with different learning performance and the same learning performance on the same band. We propose three edge device allocation methods: 1) iterative hierarchical Hungarian allocation; 2) bow allocation; and 3) category-divided allocation to ensure the fairness of edge learning among sub-bands. The fairness of the system is measured by evaluating the lowest learning performance in the sub-band. To adapt to the actual scenario, we enhance fairness by introducing band attribute parameters (considering the priority, anti-interference ability, and congestion level of the main users of the sub-band). Simulation results show that the proposed strategy significantly improves the ITP of edge learning in IoT systems, especially in the case of varying band utilization. The fairness scheme improves the overall edge learning fairness of the system.

The massive access in Internet of Things (IoT) introduces significant communication and computation overheads. Besides, the widespread IoT terminals placed in unattended area and with limited capabilities are vulnerable to various attacks such as physical attack. To alleviate the huge communication and computation overheads and to resist physical attacks, we propose a physically unclonable function (PUF)-based group authentication protocol in this paper, where a pre-stored PUF challenge scheme with PUF acting as the root key is proposed to limit the size of signalings in a group. Different from existing work with assumptions on secure communication channels and trusted group leader (GL), we consider untrusted GL and insecure communication channels among the device, the GL, and the home network (HN) and propose a simplified PUF-based device-to-device authentication scheme to perform mutual authentication and key sharing between the devices and the untrusted GL. Finally, the proposed protocol is evaluated with formal security analysis, where a novel threat model is presented for the physical attacker to overhear the secret in device’s memory. Results show that the proposed protocol can achieve desired authentication and confidentiality goals even the GL is under physical attacks and the communication channels are insecure. Further, simulations are demonstrated to show the outperformance of the proposed protocol in communication overhead, computation overhead, and security, compared with baseline solutions.

A novel modular extremely large-scale multiple-input-multiple-output integrated sensing and communication system is investigated in this paper. The piecewise-far-field channel model is employed to characterize both communication and sensing channels, capturing the far-field propagation within each subarray and the near-field effects among subarrays due to the small subarray aperture and large inter-subarray spacing. Then, a joint transmit-receive beamforming problem is formulated to optimize communication spectral efficiency while satisfying the sensing signal-to-clutter-plus-noise ratio requirement. To solve this problem, an alternating optimization framework is proposed to iteratively update the transmit beamformer and receive beamformer until convergence. For a fixed receive beamformer, a closed-form optimal analog beamformer is firstly derived by exploiting the near-field propagation characteristics among subarrays, transforming the transmit hybrid beamforming problem into a low-dimensional digital beamforming optimization and substantially reducing the computational complexity. Then, two efficient algorithms are proposed to solve the rank-constrained digital beamforming problem. First, the semi-closed form of the optimal digital beamformer is derived and shown to form a complex Stiefel manifold. Based on this structure, a joint Riemannian-Euclidean gradient descent algorithm is developed for iterative optimization. Second, an semidefinite relaxation-based approach is proposed, where a near-optimal solution is obtained through rank constraint relaxation and randomization. Extensive simulations validate the superiority of the proposed algorithms, revealing that the optimal subarray scale balances spatial multiplexing and beamforming gains based on user distance, while increasing subarray numbers significantly enhances range resolution due to more pronounced spherical wavefronts.

Physical layer (PHY) secret key generation (SKG) has been widely studied as a promising approach to achieving One-Time-Pad security. The improvement of SKG rate is quite a huge challenge, especially in scenarios with untrusted internal helpers or eavesdroppers that aim to wiretap the negotiated secret keys between legitimate parties. In this paper, we propose a token-based SKG scheme to deal with the problem of information leakage with internal eavesdropping attacks. The basic idea is to cover random pilots with protective tokens to confuse eavesdroppers. Three scenarios including passive external eavesdropping, active internal eavesdropping with a reconfigurable intelligent surface (RIS)-assisted untrusted helper, and active internal eavesdropping with an untrusted relay are considered and analyzed to evaluate the performance of the proposed anti-eavesdropping scheme. Theoretical analysis shows that the proposed token-based SKG scheme can perfectly secure the key negotiation, achieving zero information leakage even in the untrusted relaying scenario without a direct link between Alice and Bob. Moreover, closed-form expressions for secret key capacity (SKC) are obtained. Finally, numerical results indicate that the proposed scheme outperforms the state-of-the-art methods. Using a token-generation mapping function with greater diversity in amplitude and phase, our approach achieves enhanced SKC performance across various scenarios, including those with a passive eavesdropper, a RIS-assisted untrusted helper, and an untrusted relay.

Federated Learning (FL) is a communication-efficient distributed machine learning method that allows multiple devices to collaboratively train models without sharing raw data. FL can be categorized into centralized and decentralized paradigms. The centralized paradigm relies on a central server to aggregate local models, potentially resulting in single points of failure, communication bottlenecks, and exposure of model parameters. In contrast, the decentralized paradigm, which does not require a central server, provides improved robustness and privacy. The essence of federated learning lies in leveraging multiple local updates for efficient communication. However, this approach may result in slower convergence or even convergence to suboptimal models in the presence of heterogeneous and imbalanced data. To address this challenge, we study decentralized federated averaging via random walk (DFedRW), which replaces multiple local update steps on a single device with random walk updates. Traditional Federated Averaging (FedAvg) and its decentralized versions commonly ignore stragglers, which reduces the amount of training data and introduces sampling bias. Therefore, we allow DFedRW to aggregate partial random walk updates, ensuring that each computation contributes to the model update. To further improve communication efficiency, we also propose a quantized version of DFedRW. We demonstrate that (quantized) DFedRW achieves convergence upper bound of order$\mathcal {O}(\frac{1}{k^{1-q}})$under convex conditions. Furthermore, we propose a sufficient condition that reveals when quantization balances communication and convergence. Numerical analysis indicates that our proposed algorithms outperform (decentralized) FedAvg in both convergence rate and accuracy, achieving a 38.3% and 37.5% increase in test accuracy under high levels of heterogeneities, without increasing communication costs for the busiest device.

Integrated sensing and communication (ISAC) is a potential technology of the sixth-generation (6G) mobile communication system, which enables communication base station (BS) with sensing capability. However, the performance of single-BS sensing is limited, which can be overcome by multi-BS cooperative sensing. There are three types of multi-BS cooperative sensing, including cooperative active sensing, cooperative passive sensing, and cooperative active and passive sensing, where the multi-BS cooperative passive sensing has the advantages of low hardware modification cost and large sensing coverage. However, multi-BS cooperative passive sensing faces the challenges of synchronization offset mitigation and sensing information fusion. To address these challenges, a non-line of sight (NLoS) and line of sight (LoS) signal cross-correlation (NLCC) method is proposed to mitigate carrier frequency offset (CFO) and time offset (TO). Besides, a symbol-level fusion method of multi-BS sensing information is proposed. The discrete samplings of echo signals from multiple BSs are matched independently and coherently accumulated to improve sensing accuracy. Moreover, a low-complexity joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation method is proposed to reduce the computational complexity. Simulation results show that symbol-level multi-BS cooperative passive sensing scheme has an order of magnitude higher sensing accuracy than single-BS passive sensing. This work provides a reference for the research on multi-BS cooperative passive sensing.

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)-enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and low-frequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, spatial filtering and cyclic cross-correlation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cramér-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.

This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propose a new cooperative sensing-assisted predictive beam tracking design. In each TTS, the BSs use echo signals to cooperatively track the mobile device as a sensing target, and continuously adjust the beam directions to follow the device for enhancing the performance for both communication and sensing. First, we propose a cooperative sensing design to track the device, in which the BSs first employ the two-dimensional discrete Fourier transform (2D-DFT) technique to perform local target estimation, and then use the extended Kalman filter (EKF) method to fuse their individual measurement results for predicting the target parameters. Next, based on the predicted results, we obtain the achievable rate for communication and the predicted conditional Cramér-Rao lower bound (PC-CRLB) for target parameters estimation in the next TTS, as a function of the beamforming vectors. Accordingly, we formulate the predictive beamforming design problem, with the objective of maximizing the achievable communication rate in the following TTS, while satisfying the PC-CRLB requirement for sensing. To address the resulting non-convex problem, we first propose a semi-definite relaxation (SDR)-based algorithm to obtain the optimal solution, and then develop an alternative penalty-based algorithm to get a high-quality low-complexity solution. Simulation results indicate that the proposed cooperative sensing design achieves higher target tracking accuracy than other benchmark schemes. The results also validate the benefits of multi-BS cooperative sensing in improving tracking performance compared with the conventional single-BS sensing.

Addressing the communication and sensing demands of sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) has garnered traction in academia and industry. With the sensing limitation of single base station (BS), multi-BS cooperative sensing is regarded as a promising solution. The coexistence and overlapped coverage of macro BS (MBS) and micro BS (MiBS) are common in the development of 6G, making the cooperative sensing between MBS and MiBS feasible. Since MBS and MiBS work in low and high frequency bands, respectively, the challenges of MBS and MiBS cooperative sensing lie in the fusion method of the sensing information in high and low-frequency bands. To this end, this paper introduces a symbol-level fusion method and a grid-based three-dimensional discrete Fourier transform (3D-GDFT) algorithm to achieve precise localization of multiple targets with limited resources. Simulation results demonstrate that the proposed MBS and MiBS cooperative sensing scheme outperforms traditional single BS (MBS/MiBS) sensing scheme, showcasing superior sensing performance.

The application of inverse synthetic aperture radar (ISAR) is investigated in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. In contrast to velocity sensing of a point target of most ISAC works, ISAR enables rotational velocity sensing to obtain the cross-range values of different scatterers on an extended target. To utilize this characteristic, we initially derive the ISAR OFDM received signal reconstruction in the frequency domain, which demonstrates that the ISAR OFDM echo signal can be equivalent to the signal received by an array, including the decoupled radial range and cross-range parameters. According to the derived signal model, a supporting parameter estimation algorithm based on the equivalent array form is proposed to estimate the range and cross-range parameters for resolvable scatterers on the extended target. Finally, numerical results confirm the effectiveness of utilizing ISAR sensing in wideband ISAC systems.

The integrated sensing and communication (ISAC) has been identified as a crucial enabling technology for the development of the sixth-generation (6G). This paper specifically focuses on the uplink orthogonal frequency division multiplexing (OFDM) ISAC system. By utilizing uplink ISAC signals, the base station (BS) can obtain a comprehensive understanding of its surroundings. However, due to the rapid growth of the sensing and communication services, spectrum resources are becoming increasingly scarce, which consequently leads to the tradeoff between sensing and communication. To address this challenge, we employ subcarrier and power allocation techniques that minimize the Cramer-Rao lower bound (CRLB) while satisfying the requirements of peak-to-sidelobe level ratio (PSLR) and communication data rate (CDR). Through this approach, subcarriers exhibit exceptional sensing performance are screened out to create a wider virtual sensing bandwidth. The resulting optimization problem is formulated as a nonconvex one. By utilizing the convex relaxation and cyclic minimization algorithm (CMA), the resource allocation problems are solved iteratively. Numerical results prove that our proposed strategy outperforms the conventional methods in terms of sensing and communication tradeoffs.

In the Integrated Sensing and Communication (ISAC) system, the pilot signal has high sensing performance due to its good autocorrelation and high transmit power. However, the equally spaced pilot signal reduces the maximum unambiguous range and velocity compared with the OFDM signal with continuous resources, limiting the sensing performance of base station (BS). Additionally, BS fails to estimate the distances and velocities of multiple targets in coherent signals. To address these problems, we propose a pilot design scheme with coprime and periodic stepping values for pilot indices. Theoretical analysis and simulation results demonstrate that the proposed pilot signal does not reduce the maximum unambiguous range and velocity, which can be processed by the smoothing algorithm and the multiple signal classification (MUSIC) algorithm to accurately estimate the distances and velocities of multiple targets in coherent signals.

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.

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for integrated sensing and communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning-based multinode ISAC 4D environment reconstruction method with the uplink-downlink (UL-DL) cooperation, which employs virtual aperture technology, constant false alarm rate (CFAR) detection, and mutiple signal classification (music) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving the multinode cooperation and UL-DL cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models attention gate gridding residual neural network (AGGRNN) and multiview sensing fusion network (MVSFNet) to enhance the density of the sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multilevel fusion strategy incorporating both the data-level and feature-level fusion to fully leverage the advantages of the multinode cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms the other comparative method, enabling high-precision environmental reconstruction using the ISAC system.

Integrated sensing and communication integrated sensing and communications (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this article, we investigate transmit beamforming design for the multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multitarget sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among the reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multiobjective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between the communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, the simulation results validate that the proposed method achieves a better tradeoff between the multiuser communication and multitarget sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multitarget resolution capability and angle estimation accuracy.

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization root mean square error (RMSE) by about 6 dB compared with the maximum likelihood esimation (MLE)-based benchmark method.

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

Perceptive mobile network (PMN) is an emerging concept for next-generation wireless networks capable of conducting integrated sensing and communication (ISAC). A major challenge for realizing high performance sensing in PMNs is how to deal with spatially separated asynchronous transceivers. Asynchronicity results in timing offsets (TOs) and carrier frequency offsets (CFOs), which further cause ambiguity in ranging and velocity sensing. Most existing algorithms mitigate TOs and CFOs based on the line-of-sight (LOS) propagation path between sensing transceivers. However, LOS paths may not exist in realistic scenarios. In this paper, we propose a cooperation based joint active and passive sensing scheme for the non-LOS (NLOS) scenarios having asynchronous transceivers. This scheme relies on the cross-correlation cooperative sensing (CCCS) algorithm, which regards active sensing as a reference and mitigates TOs and CFOs by correlating active and passive sensing information. Another major challenge for realizing high performance sensing in PMNs is how to realize high accuracy angle-of-arrival (AoA) estimation with low complexity. Correspondingly, we propose a low complexity AoA algorithm based on cooperative sensing, which comprises coarse AoA estimation and fine AoA estimation. Analytical and numerical simulation results verify the performance advantages of the proposed CCCS algorithm and the low complexity AoA estimation algorithm.

Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes.

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance.

To realize higher-level autonomous driving and advanced transportation applications, the introduction of the integrated sensing and communication (ISAC) technique in vehicular networks is indispensable. Different from the existing works, this paper investigates the power allocation problem for onboard ISAC systems of vehicles, during the vehicle-to-infrastructure communication, vehicle-to-vehicle communication and sensing progress, in case of the time-varying communication channel gains, the time-varying impulse responses of sensed targets, and the stochastic traffic. Note that both the inter-beam interference of a single vehicle and the inter-vehicle interference are important considerations. Specifically, we formulate a stochastic programming problem, which optimizes the sensing performance, subject to constraints on the network stability, power limits and quality-of-service requirements. Leveraging the Lyapunov optimization technique, this stochastic programming problem is transformed into a single-time slot non-convex problem. Taking advantages of genetic algorithm and particle swarm optimization (PSO), a hybrid meta-heuristic algorithm is designed to solve the non-convex problem. Typically, we improve the traditional PSO to balance the global search ability and local search ability of particles. Finally, a dynamic power allocation strategy is proposed. The theoretical analysis and simulation results show that this strategy achieves a communication performance-sensing performance tradeoff of [$ {\mathrm {O(}}1/V{\mathrm {)}} $,$ {\mathrm {O(}}V{\mathrm {)}} $] with$ V $being a control parameter.

This paper investigates a reconfigurable intelligent surface (RIS) assisted cooperative multicell integrated sensing and communication (ISAC) system with multiple users and targets. In particular, the RIS is leveraged to assist the joint transmission of the multiple base stations (BSs) to multiple users, while assisting cooperative sensing by multiple BSs to perform multiple targets sensing. We formulate a problem for the purpose of minimizing the transmit power via jointly designing the transmit beamforming of the BSs and phase shifts of the RIS, while guaranteeing the achievable communication rate requirements and the sensing mutual information requirements. To address this non-convex problem, a high-quality alternating optimization algorithm is developed to split the intractable problem into two sub-problems. Specifically, with the given phase shifts of the RIS, the transmit beamforming sub-problem is addressed by semidefinite relaxation-based algorithm. A successive convex approximation (SCA) method-based and penalty function-based convex-concave procedure algorithm is proposed to tackle the RIS phase-shift optimization sub-problem. To reduce the computational complexity, an efficient low-complexity alternating optimization algorithm is developed. For the transmit beamforming design, an SCA method-based second-order cone programming algorithm is proposed, while for the RIS phase-shift design, a circle manifold optimization-based algorithm is introduced by utilizing penalty function. Simulation results validate the advancement of deploying RIS in enhancing the performance of cooperative multicell ISAC systems in terms of transmit power. Furthermore, our results illustrate the significant superiority of the proposed algorithms over the benchmark schemes.

This paper studies a networked integrated sensing and communication (ISAC) system, where distributed base stations (BSs) implement coordinated transmit beamforming to communicate with their respective user and cooperatively perform multi-static target sensing. To fully reap the performance gains provided by the networked ISAC system, accurate channel state information (CSI) and time synchronization (TS) among distributed BSs are crucial. However, CSI errors and TS errors are inevitable in practice due to the imperfect channel training and the inaccurate synchronization. To reveal the effect of CSI errors on communication, a Gaussian distributed CSI error model is formulated based on the channel estimation process, and accordingly, the users’ achievable rates with CSI errors are derived. To characterize the effect of TS errors on multi-static sensing, the Cramér-Rao lower bound (CRLB) for estimating target position in the presence of TS errors is derived. It is shown that due to the existence of CSI errors and TS errors, additional terms are introduced in the achievable rate and CRLB formulas, degrading the communication and sensing performance, respectively. Based on the above derivations, we aim at maximizing the sum-rate of users by designing the coordinated transmit beamforming at the BSs, while guaranteeing the CRLB requirements for target sensing. In particular, we consider two cases with and without TS errors, for which the corresponding non-convex optimization problems are solved via a penalty-based algorithm and an alternating optimization algorithm, respectively. Simulation results show that the proposed algorithms significantly outperform benchmark schemes for both cases with and without CSI/TS errors, thus validating the robustness in ISAC performance optimization.

Reconfigurable intelligent surface (RIS) is acknowledged as a promising technique in improving physical layer (PHY) secret key generation (SKG) performance by enhancing the randomness of radio electromagnetic environment. However, RIS can also be adopted by illegal eavesdropper to destroy the channel reciprocity between key-sharing parties. Thus, there is a tradeoff between the enhanced randomness and the decreased channel reciprocity with the application of RIS. This paper aims to answer the question that whether the adversarial RIS is harmful to SKG by studying the SKG performance in the RIS scenario where Alice and Eve each applies a RIS. To this end, the closed-form expression for the upper bound of secret key rate (SKR) and expressions for the autocorrelation coefficients of two probing channel samples within a coherent duration are provided. Results show that the relative positions of Eve and adversarial RIS to Alice and Bob play a key role in determining whether the adversarial RIS is harmful or beneficial to the PHY SKG system.

5th Generation (5G) mobile communication systems operating at around 28 GHz have the potential to be applied to simultaneous localization and mapping (SLAM). Most existing 5G SLAM studies estimate environment as many point targets, instead of extended targets. In this paper, we focus on the performance analysis of 5G SLAM for multiple extended targets. To evaluate the mapping performance of multiple extended targets, a new mapping error metric, named extended targets generalized optimal sub-pattern assignment (ET-GOPSA), is proposed in this paper. Compared with the existing metrics, ET-GOPSA not only considers the accuracy error of target estimation, the cost of missing detection, the cost of false detection, but also the cost of matching the estimated point with the extended target. To evaluate the performance of 5G signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB, 5G signal sensing can barely meet to meet the requirements of SLAM for multiple extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23 GHz, and the antenna size of 32.

With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated by sensors, and transmitted to the control center through uplink wireless communications. The corresponding control commands are generated and sent back to the devices through downlink wireless communications. Since wireless communications, sensing and control are tightly coupled, there are big challenges on the modeling and design of such closed-loop systems. In particular, existing theoretical tools of these functionalities have different modelings and underlying assumptions, which make it difficult for them to collaborate with each other. Therefore, in this paper, an analytical closed-loop model is proposed, where the performances and resources of communication, sensing and control are deeply related. To achieve the optimal control performance, a co-design of communication resource allocation and control method is proposed, inspired by the model predictive control algorithm. Numerical results are provided to demonstrate the relationships between the resources and control performances.

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.

The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era since it provides wide coverage and on-demand deployment services in a distributed manner. The detection of Sybil attacks is essential to ensure trusted communication in FANET. Nevertheless, the conventional methods only utilize the untrusted information that UAV nodes passively “heard” from the “auditory” domain (AD), resulting in severe communication disruptions and even collision accidents. In this paper, we present a novel VA-matching solution that matches the neighbors observed from both the AD and the “visual” domain (VD), which is the first solution that enables UAVs to accurately correlate what they “see” from VD and “hear” from AD to detect the Sybil attacks. Relative entropy is utilized to describe the similarity of observed characteristics from dual domains. The dynamic weight algorithm is proposed to distinguish neighbors according to the characteristics' popularity. The matching model of neighbors observed from AD and VD is established and solved by the vampire bat optimizer. Experiment results show that the proposed VA-matching solution removes the unreliability of individual characteristics and single domains. It significantly outperforms the conventional RSSI-based method in detecting Sybil attacks. Furthermore, it has strong robustness and achieves high precision and recall rates.

This paper studies the wireless control system at network edge, in which one base station (BS) coordinates the closed-loop wireless control of multiple subsystems each consisting of a plant, sensor, and actuator. In this system, the BS first collects the state information from the sensors of plants, then processes the information via edge computing, and finally sends the obtained command signals back to the actuators for controlling the plants. In particular, we consider the ultra-reliable low-latency communication (URLLC) for the state and command signal transmission, by using the rate formulas based on short-packet communication. Under this setup, we first present a time-division-multiple-access (TDMA) protocol for coordinating the sensing, communication, and computation among the multiple plants. Then, we jointly optimize the communication and computation resource allocations to minimize the closed-loop control latency while ensuring the stability of the controlled plants. Though the considered problem is difficult to solve, we transform it into a non-convex problem with semi-definite constraints, and then present an efficient solution via the techniques of alternating optimization and convex approximation. Numerical results show that the proposed solution efficiently reduces the closed-loop control latency as compared to other benchmark schemes with heuristic resource allocations.

This paper designs a low peak-to-average power ratio (PAPR) Integrated Sensing and Communication (ISAC) waveform based on OFDM. Firstly, we propose an ISAC waveform structure, in which radar subcarriers within the OFDM symbols are randomly located anywhere within non-contiguous Physical Resource Blocks (PRBs). Using this OFDM-based ISAC waveform structure, the sensing mutual information (MI) between the radar channel and the received waveform is derived and maximized under the constraints of communication data information rate (DIR), PAPR, and transmit power. Then, an optimization algorithm is proposed to obtain the optimal power allocation of subcarriers. Finally, simulation results verify the effectiveness and flexibility of our designed waveform.

Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization. The design of ISAC signal based on the orthogonal frequency division multiplex (OFDM) signal is the mainstream. However, when detecting the long-range target, the delay of echo signal exceeds CP duration, which will result in inter-symbol interference (ISI) and inter-carrier interference (ICI), limiting the sensing range. Facing the above problem, we propose to increase useful signal power through coherent compensation and improve the signal to interference plus noise power ratio (SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the improvement of SINR of range-doppler map (RDM) is verified by simulation, which will expand the sensing range.

Millimeter automotive radar has great potential in advanced driver assistance systems (ADASs) to enable safety features, such as adaptive cruise control and collision avoidance. However, with widely deployment of millimeter radars on vehicles, the risk of radar mutual interference becomes a major factor limiting the high performance of radar detection. In this article, we analyze the mutual interference among multiple frequency modulated continuous wave (FMCW) radars. On the one hand, we study the interference in detail by considering co-channel interference (CCI) and adjacent channel interference (ACI) simultaneously. Besides, the CCI is analyzed by employing stochastic geometry model while the ACI is assessed by the deterministic analysis method. On the other hand, we propose a time-frequency division multiple access (TFDMA) scheme to mitigate the interference in a coordinated manner and evaluate it in terms of mitigation delay, the probability of interference, effective detectable density, maximum number of interference-free radar, and control signaling overhead. Finally, we study the power allocation strategy to enable the effectiveness of the coordinated interference mitigation approach based on the interference analysis. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of the coordinated interference mitigation approach is 3.5 dB.

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing, and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A, and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method, and super-resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication systems.

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in the B5G/6G network thanks to its appealing features, such as wide coverage and on-demand deployment. Emergency communication (EC) is essential to promptly inform UAVs of potential danger to avoid accidents, whereas the conventional communication-only feedback-based methods, which separate the digital and physical identities (DPI), bring intolerable latency and disturb the unintended receivers. In this paper, we present a novel DPI-Mapping solution to match the identities (IDs) of UAVs from dual domains for visual networking, which is the first solution that enables UAVs to communicate promptly with what they see without the tedious exchange of beacons. The IDs are distinguished dynamically by defining feature similarity, and the asymmetric IDs from different domains are matched via the proposed bio-inspired matching algorithm. We also consider Kalman filtering to combine the IDs and predict the states for accurate mapping. Experiment results show that the DPI-Mapping reduces individual inaccuracy of features and significantly outperforms the conventional broadcast-based and feedback-based methods in EC latency. Furthermore, it also reduces the disturbing messages without sacrificing the hit rate.

The collaborative sensing of multiple Integrated sensing and communication (ISAC) base stations is one of the important technologies to achieve intelligent transportation. Interference elimination between ISAC base stations is the prerequisite for realizing collaborative sensing. In this paper, we focus on the mutual interference elimination problem in collaborative sensing of multiple ISAC base stations that can communicate and radar sense simultaneously by transmitting ISAC signals. We establish a mutual interference model of multiple ISAC base stations, which consists of communication and radar sensing related interference. Moreover, we propose a joint optimization algorithm (JOA) to solve the collaborative precoding problem with total power constraint (TPC) and per-antenna power constraint (PPC). The optimal precoding design can be obtained by using JOA to set appropriate tradeoff coefficient between sensing and communication performance. The proposed collaborative precoding design algorithm is evaluated by considering sensing and communication performance via numerical results. The complexity of JOA for collaborative precoding under TPC and PPC is also compared and simulated in this paper.

As one of the promising technologies of the future 6G network, Integrated Sensing and Communication (ISAC) is able to provide tremendous benefits such as performance improvement and cost reduction through integrating the two systems as a whole. The use of ISAC technology will introduce new sensing abilities and enhance information processing capabilities at base stations, and makes the interaction between base stations and vehicles more frequent, which brings huge benefits to connected automated vehicles(CAV). If the BS only transmits the single beam at a given period, the sensing functions will not be guaranteed as there are blind zone and other issues. Therefore, we propose a multi-beam model for the ISAC downlink transmission. Furthermore, considering the uneven distribution of resources among different users, an ISAC multi-beam power allocation algorithm is proposed. Such convex optimization problem is solved using CVX toolbox and optimal solutions are obtained. The simulation results show that compared with the traditional average power allocation and water filling algorithms, proposed algorithm will improve the total communication rate for multi-user scenario, and provide a satisfied sensing accuracy of less than 1m sensing error.

In this paper, a novel multiple access method is proposed and evaluated for integrated sensing and communication (ISAC) enabled UAV ad hoc network, in which the UAVs can perform sensing and communicating simultaneously. With integrated signal, a novel spatial division method is proposed based on a multi-beam framework with tunable analog antenna arrays for ISAC system. With the implementation of such spatial division method, we design a new time-frequency resource allocation scheme by dividing the integrated signal into Radar (R) mode and Radar Communication (RC) mode. Moreover, according to the packet arrival rate, to make full use of spectrum resources, a novel procedure to assign channels is proposed. The performance of medium access method is analyzed by using Markov model. Simulation results shows that the multiple access method proposed in this paper has improved the throughput of UAV nodes with the assistance of sensing information.

Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANETs routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive$Q$-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAVs nodes via the queuing theory, and then the closed-form solutions of neighbors’ change rate (NCR) and neighbors’ change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive$Q$-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time with low cost. The simulation results verify the accuracy of the topology dynamic analysis model, and also prove that our TARRAQ outperforms the$Q$-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

Three-dimensional detection is essential for autonomous driving and intelligent transportation system, as it enables vehicles to detect and track surrounding objects. Usually, autonomous vehicles are equipped with multiple sensing modalities to achieve robust and precise detection. This work focuses on fusing millimeter-wave radar data with monocular images, as radar can make up for the lack of explicit depth information. We propose a novel approach that fuses radar data and images at the feature level for 3-D detection. Radar points are first merged into a raw feature map with data set statistics by a novel transformation method. With this transformation, radar features can be extracted by convolutional neural networks and fused with image features. Object properties, including location, dimension, and rotation are regressed from the fused features. In this article, the proposed fusion strategy is implemented with a keypoint-based 3-D detection framework and evaluated on the challenging NuScenes data set. Experimental results suggest that the fusion of radar data promotes 3-D detection capability in public benchmarking.

Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicularad hocnetwork (VANET). However, the convergence efficiency of ND is facing the requirements of multivehicle fast networking of VANET with frequent topology changes. This article proposes the gossip-based information dissemination and sensing information-assisted ND with multipacket reception (GSIM-ND) algorithm for VANET. The GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multipacket reception (MPR). Besides, through the multitarget detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, the GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of the GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered are derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this article can always reach the short-term convergence quickly. Moreover, the GSIM-ND algorithm is more efficient and stable compared with the completely random algorithm (CRA), scan-based algorithm (SBA), and gossip-based algorithm. The convergence time of the GSIM-ND algorithm is 40%–90% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.

Unmanned aerial vehicles (UAVs) are widely applied in civil applications, such as disaster relief, agriculture and cargo transportation, and so on. With the massive number of UAV flight activities, the anti-collision technologies aiming to avoid the collisions between UAVs and other objects have attracted much attention. The anti-collision technologies are of vital importance to guarantee the survivability and safety of UAVs. In this article, a comprehensive survey on UAV anti-collision technologies is presented. We firstly introduce laws and regulations on UAV safety which prevent a collision at the policy level. Then, the process of anti-collision technologies is reviewed from three aspects, i.e., obstacle sensing, collision prediction, and collision avoidance. We provide a detailed survey and comparison of the methods of each aspect and analyze their pros and cons. Besides, the future trends on UAV anti-collision technologies are presented from the perspective of fast obstacle sensing and fast wireless networking. Finally, we summarize this article.

Thanks to the advantages of flexible deployment and high mobility, unmanned aerial vehicles (UAVs) have been widely applied in the areas of disaster management, agricultural plant protection, environment monitoring, and so on. With the development of UAV and sensor technologies, UAV-assisted data collection for the Internet of Things (IoT) has attracted increasing attention. In this article, the scenarios and key technologies of UAV-assisted data collection are comprehensively reviewed. First, we present the system model, including the network model and the mathematical model of UAV-assisted data collection for IoT. Then, we review the key technologies, including clustering of sensors, UAV data collection mode as well as joint path planning and resource allocation. Finally, the open problems are discussed from the perspectives of efficient multiple access as well as joint sensing and data collection. This article hopefully provides some guidelines and insights for researchers in the area of UAV-assisted data collection for IoT.

The Internet of Things (IoT) permeates every aspect of our daily lives as billions of interconnected devices are deployed in the physical world. However, IoT networks operate in an untrusted environment and often suffer from many malicious active attacks. Automatic modulation classification (AMC), which can identify the modulation format of intercepted signals without prior knowledge, is a vital technology in countering physical-layer threats of IoT. However, most of the existing algorithms assume the channel is time invariant, and the AMC in time-varying channels is not been well studied. To deal with this dilemma, a novel AMC algorithm MCBLDN consisting of multiple convolutional neural networks (CNNs), a bidirectional long short-term memory network (BLSTM), and a deep neural network (DNN) is proposed. In MCBLDN, a multislot constellation diagram (CD) method is proposed to extract time-evolution characteristics for generating more discriminative features. Specifically, different grayscale subimages generated by slotted CDs are processed serially by their respective CNNs. Therefore, MCBLDN is overparameterized and time consuming. In addition, the frequency offset and phase offset caused by time-varying channels are neglected in MCBLDN, which is detrimental to the performance of AMC. To address the mentioned disadvantages, a lightweight MCBLDN with a spatial transformer network (SLCBDN) is proposed. First, the multiple CNNs in MCBLDN are pruned into a lightweight classification model, and the input data are rearranged to facilitate parallel processing by the lightweight CNN. Additionally, the spatial transformer network (STN) is utilized to reduce the influence of frequency offset and phase offset. Numerical results verify that the proposed method achieves superior performance and higher speed compared to the baseline algorithm MCBLDN.

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV network, the throughput of UAV network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV network.

Despite its advantages of flexility and low-cost networking, unmanned aerial vehicle (UAV) communications face various attacks such as eavesdropping. Existing studies on secure UAV communications assume fixed-location eavesdroppers and rarely consider interactions between legitimate nodes and eavesdroppers. In this paper, we investigate eavesdropping and anti-eavesdropping interaction between a UAV-enabled eavesdropper (UAV-E) and a UAV-enabled base station (UAV-BS) in a downlink wiretap system. The UAV-E aims to wiretap downlink signals by adaptively adjusting its trajectory while the UAV-BS aims to maximize secrecy-sum-rate with minimum power consumption by jointly optimizing user scheduling, power control, and trajectory. Dynamic differential equations are formulated to characterize motions of UAVs, following which a zero-sum differential game is formulated to model the “pursuit-evasion” interaction between the UAV-BS and the UAV-E. Definition and existence of Nash equilibrium (NE) are provided. To obtain the NE, Pontryagins minimum principle is leveraged to solve the trajectory design problem. Further, Gauss-Seidel-like implicit finite-difference method is leveraged to obtain saddle-point strategies at NE. Finally, numerical results are provided to verify the effectiveness of the proposed game model. It is revealed that the differential game can well-characterize the strategy interactions between UAVs. Moreover, results show that the initial positions and weights of UAVs, the energy consumption factor, and the user scheduling have key impacts on motion interactions between the UAV-BS and the UAV-E and further on UAV-BS’s power control.

Driven by the vision of intelligent connection of everything and digital twin towards 6G, a myriad of new applications, such as immersive extended reality, autonomous driving, holographic communications, intelligent industrial internet, will emerge in the near future, holding the promise to revolutionize the way we live and work. These trends inspire a novel technical design principle that seamlessly integrates two originally decoupled functionalities, i.e., wireless communication and sensing, into one system in a symbiotic way, which is dubbed symbiotic sensing and communications (SSaC), to endow the wireless network with the capability to “see” and “talk” to the physical world simultaneously. Noting that the term SSaC is used instead of ISAC (integrated sensing and communications) because the word “symbiotic/symbiosis” is more inclusive and can better accommodate different integration levels and evolution stages of sensing and communications. Aligned with this understanding, this article makes the first attempts to clarify the concept of SSaC, illustrate its vision, envision the three-stage evolution roadmap, namely neutralism, commensalism, and mutualism of SaC. Then, three categories of applications of SSaC are introduced, followed by detailed description of typical use cases in each category. Finally, we summarize the major performance metrics and key enabling technologies for SSaC.

Positioning is of importance to Unmanned Aerial Vehicle (UAV) networks, which is mainly realized using Global Navigation Satellite System (GNSS). However the GNSS signals are not always available in environments such as denied or indoor areas. To address this issue, in this paper, a joint neighbor discovery and positioning method for UAV network is proposed to deal with the scenarios with poor GNSS signals. Specifically, during the neighbor discovery process, the Two-Way Ranging (TWR) and Angle of Arrival (AoA) algorithm is utilized. In addition, an approximate optimal multi-hop positioning method is proposed. The performance of the proposed method is analyzed. Numerically, the results demonstrate the positioning accuracy and the convergence of the neighbor discovery process. This paper verifies the value and feasibility of multi-hop positioning, providing a basis for further research in joint positioning and communication in UAV networking.

Due to energy- and computation-efficiency, physical layer authentication has been acknowledged as a powerful approach in verifying the identity of mobile terminals, especially in the massive machine type communication (mMTC) scenario with resource-constraint terminals. In existing literatures, most of works are mainly focused on point-to-point verification, where only one terminal can be authenticated at a time. In this paper, we propose a novel physical layer group authentication mechanism exploiting the benefits of non-orthogonal multiple-access (NOMA) and the irreversibility of hash operation. The proposed mechanism is especially suitable for group authentication of multiple terminals in the mMTC networks with massive connections to one access point. In the authentication procedure, challenge-response signals are multiplied with upper layer keys with hash operations and then exchanged at the physical layer. Binary hypothesis test is employed to verify multiple terminals. Missing rate and false alarm rate are investigated to evaluate the performance of the proposed authentication mechanism. Signal to noise ratio(SNR) is the ratio of the power of total transmission signal and noise. The proposed scheme can achieve a missing rate of 0.8% with the false alarm rate below 1% under the SNR of 25dB.

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar-sensing signals to improve the communication reliability and radar-sensing accuracy, we propose a novel code-division orthogonal frequency-division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancelation-based signal processing technique that obtains code-division multiplex gain, which is compatible with the prevalent orthogonal frequency-division multiplex (OFDM) communication system. To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system and the error propagation performance. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.

The integrated radar and communications system (IRCS) is promising for Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a tremendous challenge for the fusion center to collect detection information within the delay threshold. Based on only path loss of the channel, this paper performs power allocation to minimize the total transmit power while meeting the detection performance for radar and guaranteeing the latency violation probability (LVP) for communication. Using effective capacity theory, the latency constraint is expressed with introduced latency exponents. The resource allocation problem is non-convex and formulated to a convex one, which can be solved with a global optimum. Simulation results demonstrate the effectiveness of the proposed algorithm from the perspectives of the total transmit power and the latency of the communication links.

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul.

Providing reliable and efficient coverage for cell-edge mobile users (MUs) is a key issue in wireless communication networks. With non-uniform structure and heterogeneity of network topology in the 5G/B5G networks, performance improvement of cell-edge MUs becomes even more challenging. Unmanned aerial vehicle (UAV) exhibits a comparable advantage in enhancing cell edge performance due to its flexible mobility and line-of-sight air-to-ground links. In this paper, we study UAV-assisted cell-edge MU offloading in the non-uniform heterogeneous cellular networks. A base station (BS) coordination and ground-to-air offloading scheme is proposed to enhance the cell-edge MUs' performance, whereby cell-edge MUs are periodically scheduled between coordinated ground BSs and a flying UAV. Furthermore, a theoretical framework is developed to analyze the average spectral efficiency (SE) and average network throughput. Specifically, closed-form expressions for the average SE are derived for MUs associated with the ground BSs. Upper and lower bounds for the average SE are also obtained when the MU is offloaded to the flying UAV. Finally, numerical and simulation results are provided to validate the theoretical analysis and investigate the impact of key system parameters on the system performance, which also demonstrate the advantages of the UAV-assisted offloading scheme, compared with benchmark solutions.

With the unique agility and flexibility, unmanned aerial vehicles (UAVs) are widely applied in various scenarios. Especially in the areas with disasters, UAVs can act as base stations (BSs) to provide wireless communication services for ground users. In order to reduce the costs, we prefer to use as few UAVs as possible. However, due to the coverage constraint, each UAV can only provide services for a certain number of ground users. Moreover, considering the acceptable receiving power, the coverage radius of UAV is limited. Combined with the above considerations, we present an efficient 3D placement algorithm of UAV-Mounted BSs to cover all of the ground users. In the horizontal direction, the Edge-Prior Placement Algorithm is proposed, which gives the preferential coverage to the outermost users. The complexity of the algorithm is O(n2log n). Then, the optimal height of each UAV is assigned. As a result, these UAVs fly at different heights and cover the ground users with different radii. Simulation results are provided to evaluate the performance of the proposed algorithm. We demonstrate the impacts of the upper bound of coverage radius and capacity constraint on the number of UAVs that are used to cover the ground users, which could provide a guideline for the deployment of UAVs.

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence, the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is θ(n/logn) times higher than that of the multihop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

This paper studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average ε-outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter-receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a three-dimensional (3D) space. The ST mobility model induces a uniform distribution (of the eavesdropper's waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and non-trivial mathematical manipulations. Validated by simulations, our analysis is tight and reveals that the ground transmission is particularly vulnerable to aerial eavesdropping which can be carried out in a distance without being noticed. 3D spherical regions are identified, within which the secrecy rates vanish. This sheds useful insights to protect terrestrial wireless networks from aerial eavesdropping.

In the Internet of Things (IoT) services, the data of wireless sensor network needs to be collected. However, in the scenarios that have no infrastructure support, the data collection of sensors has great difficulty. Since unmanned aerial vehicle (UAV) has the characteristics of flexibility, it can be applied in the data collection for wireless sensor network (WSN). In this paper, we study UAVs supported data collection for WSN. Firstly, the entire region is divided into multiple cells. Secondly, the flight paths for single UAV and multiple UAVs are designed to cover all cells. The per-node capacity of sensor is derived, which is a function of the number of cells, the height of UAV, the number of sensors and the energy capacity of UAV. It is found that the per- node capacity with multiple UAVs is much larger than that with single UAVs. Then the optimal number of cells is derived to maximize the per-node capacity of WSN. Finally, we provide simulation results to verify our analysis. The discoveries in this paper may provide guideline for the UAVs assisted data collection in WSN.

Due to the similarities between neural networks and ad hoc networks, ad hoc networks can be applied to construct distributed neural network to perform complex computation. In this paper, the broadcasting nature of wireless signal and communication interactions among the nodes in ad hoc network are applied to construct neural network, which reveals the interplay between communication and computing. In the constructed neural network, the neuron is the wireless node. The communication interactions are applied to increase the number of hidden layers. The constructed neural network is further applied in target positioning and its accuracy is verified by simulation results. This paper shows that the wireless networks can be applied in computation, which may motivate the construction of large-scale neural networks via the wireless networks.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. In this study, we propose a novel trust model for UAVNs that is based on the behavior and mobility pattern of UAV nodes and the characteristics of inter-UAV channels. The proposed trust model consists of four parts: direct trust section, indirect trust section, integrated trust section, and trust update section. Based on the trust model, the concept of a secure link in UAVNs is formulated that exists only when there is both a physical link and a trust link between two UAVs. Moreover, the metrics of both the physical connectivity probability and the secure connectivity probability between two UAVs are adopted to analyze the connectivity of UAVNs. We derive accurate and analytical expressions of both the physical connectivity probability and the secure connectivity probability using stochastic geometry with or without Doppler shift. Extensive simulations show that compared with the physical connection probability with or without malicious attacks, the proposed trust model can guarantee secure communication and reliable connectivity between UAVs and enhance network performance when UAVNs face malicious attacks and other security risks.

Since unmanned aerial vehicles (UAVs) can be flexibly deployed in the scenarios of environmental monitoring, remote sensing, search-and-rescue, etc., the network protocols of UAV networks supporting high data rate transmission have attracted considerable attentions. In this paper, the three-dimensional aerial sensor network is studied, where UAVs act as aerial sensors. The delay tolerant multiple access control protocol which is called UD-MAC1, is designed for UAV networks. The UD-MAC exploits the returning UAVs to store and carry the data of the UAVs along the returning paths to the destination. The returning UAVs are discovered by ground via the Control and Non-Payload Communication (CNPC) links, which contain the sense and avoid information among UAVs. And they are confirmed by the other UAVs via data links. Simulation results show that UD-MAC protocol increases the enhances of accessing channel by 31.8% compared with VeMAC protocol.

In this paper, we investigate the angle-domain spectrum opportunities of secondary users with directional transmission, and analyze the detection probability of spectrum holes. We also analyze the detection probability of interference allowed scenarios. By calculating the angle mean of spectrum holes, we get the mathematical statistical properties. Through the results of the numerical analysis in different scenarios, we demonstrate that when N is large enough (for example, N amp;#62; 10), the size of the angle delta has a dominant impact on the spectrum opportunity. Meanwhile, we prove that utilizing directional antennas and allowing interference can obtain more angle-domain spectrum opportunities and improve the spectrum utilization.

The exploitation of spectrum opportunities in the dimension of height will bring another transmission degree of freedom for wireless networks. Besides, the modern wireless networks are deployed in the three dimensional (3D) space, which need cognitive radio technologies to enhance their performances. With these motivations, the capacity of 3D cognitive radio networks (CRNs) is addressed in this paper. Since there is one additional dimension of interference in 3D CRNs, the network protocols need to be designed to coordinate the interference and guarantee the connectivity of CRNs. Then the link capacity and routing density of 3D CRNs are investigated. Finally, we have derived the per-node capacity of primary network and secondary network respectively. We have verified that the path loss factor α has an impact on the capacity of 3D CRNs, namely, α = 3 is a watershed of capacity scaling laws. Besides, when α > 2.5, the capacity of 3D CRNs is higher than 2D CRNs with the same amount of nodes asymptotically. Therefore our results may provide an insight into the design of 3D cognitive radio networks.

Recent studies suggest nodes in practical networks are more likely to communicate with nearby nodes than far away nodes, which is referred to as proximity preference. In this paper, we model proximity preference by assuming the probability of communication follows a power law distribution with respect to distance and analyze its influence on the throughput of a hybrid network. Moreover, L-maximum-hop routing strategy is adopted to enforce delay constraints. Throughput is derived as a function of maximum hop L, proximity preference index α and the number of base stations (BSs) m. It is also found that per-node throughput changes with α. When 0 ≤ α ≤ 2, proximity has no influence on throughput. When 2 ≤ α ≤ 3, the throughput increases with α. Otherwise, the throughput reaches its maximum and remains constant. Our results demonstrate the interplay of various networks parameters with proximity preference and provide guidelines for the design of practical networks.

Abstract For the problem of spectrum scarcity and wastage, cognitive radio (CR) technology provides a solution to utilizing the vacant spectrum more efficiently. As one of the most promising techniques to obtain the cognitive information in TV white spaces, geo‐location database approach has attracted a lot of recent attentions, with its goal of enhancing the efficiency of spectrum usage and avoiding the interference to TV receivers. However, existing works mainly focus on the construction and applications of geo‐location database, and seldom consider how to deliver the cognitive information from the database to TV band devices. In this paper, we investigate the tradeoff between increasing the accuracy of cognitive information delivery and reducing the overhead. We design two mesh fusion algorithms to reduce the redundancy of cognitive information and improve the efficiency of cognitive information delivery. Finally, we verify our analysis and evaluate the efficiency of the proposed mesh fusion algorithms through numerical studies. Copyright © 2016 John Wiley & Sons, Ltd.

With the development of Time Division Long Term Evolution (TD-LTE) system, the TD-LTE base station's density is increasing rapidly. The working spectrum of Compass Navigation Satellite System (COMPASS, also called BeiDou Navigation Satellite System) is adjacent to TD-LTE system. Because the signal received by COMPASS Equipment (CE) is very weak, large frequency isolation between TD-LTE downlink and COMPASS is necessary to avoid inter-system interference, which causes severe spectrum waste. There is still no effective solution to fix this problem. In this paper, we propose an algorithm to change the subbands power allocation of TD-LTE to reduce the interference in COMPASS system caused by TD-LTE downlink, meanwhile reduce the necessary frequency isolation between the two systems. In this algorithm, it is assumed that BSs receive the position information and interference information of CE. The algorithm decreases the interference received by CE to an acceptable level by adjusting the transmit power of nearby BSs' subbands within preset scope, thus, reduce the necessary frequency isolation. Finally, a system level simulation is conducted to investigate the performance of IEA-SPR.

In this paper, we study the relation between network capacity and the density of micro base stations in heterogeneous networks (HetNets) scenario consisting of macro base station (MaBS) and micro base station (MiBS) tiers. First, the distribution of the distance between a typical user and its serving base station (BS) is derived in a stochastic geometry model. Assuming users access the BS with the strongest received signal, we obtain the probability of users' association with the MiBS tier as a function of MiBS density. Then the impact of BS density on the interference inside the MiBS tier is also achieved in closed form. Finally, we derive the closed form solution of the network capacity as a function of BS density. We find that although there are more available channels with higher MiBS density, the rate of each channel is degraded because of stronger interference. Therefore the problem of maximizing network capacity with respect to MiBS density is formulated and the optimal MiBS density is obtained. Simulations are provided to verify the correctness of our analysis. One interesting finding is that network capacity doesn't increase monotonously with BS density. Thus deploying more MiBS may not always be a good choice1.

In this paper, a distributed two-level Stackelberg game for bandwidth allocation and pricing in heterogeneous wireless networks is proposed. The proposed Stackelberg game consists of two competition game levels, namely, a user level game and a network level game. Networks are the Stackelberg leaders which play the network level game and decide price to maximize the revenue of networks. The multi-mode users are the Stackelberg followers which play the user level game and decide bandwidth allocation to maximize the utility of users. In the user level game, the notion of Match-Degree is introduced to take into account the suitability of networks to various traffics. Then the existence of Stackelberg equilibrium (SE) is verified for this Stackelberg game. To obtain the SE, an iterative algorithm is constructed. Simulation results show that our proposed game not only can significantly increase the utility of users compared with traditional bandwidth allocation schemes, but also can set suitable network price by considering network competition and user behavior.

Due to the severe traffic overload in wireless networks, offloading traffic to other networks is envisioned as a promising solution. However, since networks are dynamic, they can not accurately determine when and how much traffic to offload. To address the problem, this paper proposes a practical network state motivated traffic offloading scheme for two-tier heterogeneous networks, where users connect to the base station with the highest biased received signal strength. We derive the congestion probability of macro base station (MBS) in closed-form and introduce the Traffic Offloading Region (TOR). According to the location of network state in TOR, network can get the appropriate time to offload and the amount of offloaded traffic of MBS. To guide practical traffic offloading, our scheme obtains the association bias that can offload desired amount of traffic when base stations follow Poisson point process. Finally, simulation results are provided to validate our scheme.

Throughput scaling laws for two coexisting ad hoc networks with m primary users (PUs) and n secondary users (SUs) randomly distributed in an unit area has been widely studied. Early work showed that the secondary network performs as well as stand-alone networks, namely, the per-node throughput of the secondary networks is equation. In this paper, we show that by exploiting directional spectrum opportunities in secondary networks, the SU throughput can be improved. If the main lobe of the SU antenna pattern can be as narrow as possible, then the SUs can achieve a per-node throughput of equation which is Θ(log n) times higher than the the throughput without directional transmission. If we consider practical constraints and assume the minimum angle of the main lobe is δth, then the SU throughput gain is equation compared with the throughput without directional transmission. We also explore the statistics of directional spectrum holes in this paper.

To efficiently utilize the vacant spectrum resources, different environment awareness techniques, such as the spectrum sensing, have been applied in cognitive radio networks (CRNs). However, the existing research works ignore the effects and differences between the long term and short term vacant spectrum quality of primary users. Therefore, the cognitive information sequence (CIS) concept has been proposed in this paper to represent the information sequence of the environment awareness results. Besides, the temporal entropy is proposed to reveal the uncertainty of CIS and the cognitive information is used to measure the uncertainty of the internal and external states of primary user (PU) that can be removed by the secondary user (SU). Moreover, the information theory techniques are applied to analyze the mathematical properties of temporal entropy and cognitive information in CIS. By utilizing the temporal entropy values which reveal the vacant spectrum quality, the optimal solutions for the efficient spectrum sensing techniques are proposed and verified by numerous results, such as the optimal period of spectrum sensing and the threshold of energy detector.

This paper analyzes the connectivity of cognitive radio networks. The connectivity of cognitive network which is more consistent with reality is redefined in our article and the closed-form formula of relation between connectivity and density of PUs, density of SUs, and transmission radius of SU is given. Specifically, the impact of correlation of adjacent nodes for connectivity of cognitive radio ad hoc network is illustrated. On the basis of Random geometric graph and probability theory, a novel method that divides connectivity of cognitive radio ad hoc network into topological connectivity and physical connectivity is proposed to derive the close-form formula. We prove theoretically that once the density of secondary users is large enough and the transmission radius is appropriate, the probability of cognitive network connectivity tends to a stable non-zero value under the condition that density of primary users is small.

In this work, we present an optimal power allocation scheme dedicated for variable-hop cooperative relays in cognitive networks, which consists of Secondary users (SUs) operating in Amplify-and-Forward (AF) mode, with a Primary user (PU) receiver nearby. In order to assure the quality of service (QoS) for PU, transmit power of cognitive radio (CR) nodes must be tightly controlled which leads to narrow communication range. The issue of minimal feasible hop count Nminand energy-saving with relays which can only cause limited interference to PU are considered to enable the communication between remote nodes under the constraint of minimal signal to noise ratio (SNR) γth. Closed-form solution for the optimal power allocation scheme is obtained by Lagrangian algorithm along with an O(N) complexity dichotomy method to achieve Nmin. We prove that the energy consumption in the system is reduced with the growth of N by both theoretical derivation and extensive simulation, and the general relation between Nminand γth.

In this paper, we investigate the connectivity of random cognitive radio networks with different routing schemes. Two coexisting ad hoc networks are considered with m primary users (PUs) and n secondary users (SUs) randomly distributed in a unit area. The relation between n and m is assumed to be n = mβ. We show that with the HDP-VDP routing scheme, which is widely employed in the analysis of throughput scaling laws of ad hoc networks, the connectivity of a single SU can be guaranteed when β > 1, and the connectivity of a single secondary path can be guaranteed when β > 2. While circumventing routing can improve the connectivity of cognitive radio ad hoc network (CRAHN), we verify that the connectivity of a single SU as well as a single secondary path can be guaranteed when β > 1. Thus to achieve the connectivity of secondary networks, the density of SUs should be larger (asymptotically) than that of the PUs. A smart routing scheme can also improve the connectivity of CRAHN. Our results serve as a guide to deployment and routing design for cognitive radio networks.

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.

In order to improve the spectrum utilization in cognitive radio networks, the spectrum holes in space-time-frequency multiple dimensions should be exploited accurately and efficiently. Therefore, a novel three region scheme, which includes the black region, grey region and white region, has been designed and proposed with one primary transmitter at the center, taking into account key interference factors from secondary users (SUs) and the miss detection and false alarm probabilities in spectrum sensing. Between the black region where only primary users (PUs) have exclusive right to use the spectrum and the white region where SUs can utilize the same spectrum without causing severe interference to PUs, the grey region has been designed, which has temporal spectrum access opportunities in time dimension once neglected by existing works. Moreover, the condition of the existence of a transition zone between grey region and white region is analyzed with theoretical results, where power control should be applied to SUs. The closed-form bounds of three regions are obtained, which can be used in the space-time spectrum sensing and access in cognitive radio networks.

In this paper, we investigate the power saving issue in cognitive radio (CR) multi-hop relay network. Due to the dynamic property of the wireless channel, the quality of service (QoS) guarantee for multi-hop transmission is quite challenging. To deal with these problems, automatic repeat-request (ARQ) protocol in an end-to-end manner is incorporated. For multi-hop transmission evaluation purpose, the end-to-end packet delivery probability is put forward as a QoS indicator in this paper. Besides, by underlay spectrum sharing, each relay is possessed of a power budget (i.e., maximum transmit power) to protect primary user from suffering intolerable interference. This paper addresses the power saving problem under each relay's power budget constraint, which means the end-to-end QoS constraints can be satisfied with the minimum total power consumption for relays along the optimal path. Motivated by this, we propose a joint Lagrange dual method based power allocation and exhaustive search based relay selection algorithm to obtain the solution. Numerical simulations are presented to validate the theoretical analysis. The results show that the proposed algorithm achieves a good performance in power saving.

In this paper, the subcarrier and power allocation problem for energy efficiency maximization is addressed, which is different from traditional throughout maximization. Lagrangian dual decomposition (LDD) is applied in this problem and a multilevel water-filling for power allocation is derived. But the water-filling in this paper is a transcendental equation, which is different from traditional water-filling form. To solve this equation, fixed point iteration is applied. Besides, a sufficient condition for the existence of the fixed point is derived and joint resource allocation algorithms are designed. Finally, numerical results verify our work and the energy efficiency is improved compared with the capacity maximization scheme.

In the underlay spectrum sharing systems, secondary users (SUs) are allowed to transmit their data in the licensed spectrum band when primary users(PUs) are also transmitting, as long as the transmission of SUs do not interfere PUs' communications. In cognitive relay networks, the source and relay nodes both need to tune their transmit power to mitigate the interference to PU. In this paper, we investigate the outage performance of cognitive relay networks with PU's interference to signal ratio (ISR) constraint, where both average and peak ISR constraint are considered. Finally, We derive the exact outage probability in the scenario without cooperation and the upper bound of outage probability in the scenario with cooperation.

In spectrum sharing systems, a secondary user (SU) is permitted to share frequency bands with a primary user (PU) as long as its transmission does not interfere with the PU's communication. In this paper, the outage probability is investigated for the cognitive relay system over Nakagami-m fading channel. By applying the interference temperature constraints at the source nodes and relay nodes in secondary systems, we analyze the outage performance in two-hop underlay spectrum sharing with the best relay selection criterion. The probability density function (PDF) and cumulative distribution function (CDF) of the signal to noise ratio (SNR) at the SU's receiver are derived to obtain the closed-form upper bound of the outage probability of the secondary relay system. Simulations results demonstrate the validity and accuracy of the theoretical analysis.