Despite significant progress in Transformer-based visual tracking, standard self-attention mechanisms inherently lack the discriminative power required for accurate template search and matching. Due to the global normalization of softmax, the model inevitably assigns probabilistic quality to background noise terms that are semantically similar to the target. This "attention noise" not only reduces tracking accuracy but also forces the network to require substantial training data and computational costs to learn robust feature suppression. To address this, we propose DINOTrack, a noise-aware tracking framework designed to systematically eliminate background clutter. First, we construct a discriminative representation foundation using a frozen DINOv3 backbone with hierarchical multi-level feature fusion, ensuring the model captures robust semantic features essential for distinguishing the target from distractors. Second, to mitigate noise before feature interaction, we design a Class-Guided Heatmap Modulation (CGHM) module. By utilizing the template’s class label as a semantic prior, this module explicitly suppresses background interference and enhances potential target responses. Crucially, we introduce a Differential Denoising Interaction (DDI) mechanism for active noise cancellation. By performing a difference operation on dual query-key pairs, the DDI module effectively eliminates shared attention noise caused by semantic aliasing, enabling precise target matching. Extensive experiments demonstrate that DINOTrack achieves significant performance improvements on datasets such as LaSOT, GOT-10k, TNL2K, LaSOText, and TrackingNet.

Current mainstream methods for multimodal image fusion rely on CNN and Transformers, which often suffer from severe information loss or high computational complexity. However, state-space models offer a promising alternative with their linear complexity and reduced historical information loss. This paper proposes a novel adaptive Mamba network, named MGMFuse, for infrared and visible image fusion, aiming to seamlessly integrate thermal targets with high-resolution texture details. Specifically, feature extraction is performed using a dual-branch architecture. We design the MoEGMamba module that uses the Mixture of Experts'prompt generation mechanism to dynamically guide Mamba for adaptive state space modeling. Furthermore, a specialized mechanism is proposed to execute modality-specific channel-wise rectification and cross-modal interaction to align deep semantic features. Experimental results on multiple datasets demonstrate that the proposed algorithm outperforms existing methods. Furthermore, it significantly enhances the performance of downstream visual tasks.

This paper investigates the problem of direction-of-arrival (DoA) estimation in integrated orbital angular momentum (OAM) radar-communication systems. Unlike previous studies, we consider a more realistic and challenging scenario where coherent signals result from mainlobe overlap among echoes from multiple targets. To tackle this issue, we propose an iterative DoA estimation framework based on the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Specifically, the coherent echo signals are forcibly decoupled to form a data set comprising multiple independent components, each associated with the DoA of a specific target. A novel iterative estimation strategy is then introduced, wherein a recursive ESPRIT algorithm is sequentially applied to each component during each iteration to extract the corresponding DoA information. The estimated angles are subsequently used to update the data set for the next iteration. This alternating process continues until the DoA estimates converge to the true values. Simulation results demonstrate the effectiveness and robustness of the proposed method under coherent echo conditions.

In digital-twin-enabled heterogeneous vehicular networks (DT-HetVNets), vehicles need to synchronize data to their DTs deployed in the cloud for decision-making. However, for a vehicle which is simultaneously covered by a group of heterogeneous network infrastructures, the DT of the vehicle (DT-V) can connect with the DTs of infrastructures (DT-Is) in different infrastructure groups across regions in the virtual networks so that each DT-V may deceive the DT-Is by interacting with multiple DT-I groups and selecting the optimal one to synchronize data. To this end, we propose an on-demand data synchronization scheme for DT-Vs and DT-Is. In the scheme, infrastructures and vehicles are grouped based on their geographical locations and the arrival time of each vehicle through which the DT-Vs and DT-Is can interact with each other to make decisions in groups. Then, the requirements of DT-Vs (i.e., minimize synchronization cost and maximize synchronization satisfaction) and DT-Is (i.e., maximize profits) are considered to design their utility functions and the decision-making process between the DT-Vs in each group and the DT-Is in each group is formulated as a Stackelberg game to obtain their optimal strategies. After that, considering the deceptive behavior of vehicles, a joint optimization algorithm that integrates the Stackelberg game and the selection of each DT-V is designed to obtain the real equilibrium solution for DT-Vs and DT-Is to maximize their utilities. Simulation results show that our scheme can obtain the highest utilities compared with the traditional schemes.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

To ensure high array gain, RIS is evolving towards the extra-large RIS (XL-RIS). However, XL-RIS encounters significant challenges in high pilot overhead. In this paper, we consider a downlink XL-RIS communication system, which is used for assisting the data transmission from a single base station (BS) to multiple user equipments (UEs). We first propose a beam training strategy by turning on only a few RIS elements to reduce pilot overhead, so that the RIS can determine the UE areas. Then, the position information of the UEs is able to be obtained by estimating the far-field channels between the UEs and RIS. After that, all elements are activated for high- throughput data transmission, in which the near-field XL-RIS beamforming is performed based on the connection between the near-field and the far-field channels that we disclose with the positional information of UEs, ultimately forming multiple beams directed at different UEs with optimal power allocation.

Millimeter-Wave radar has been widely applied in the field of autonomous driving due to an excellent performance under complex weather conditions. However, in practical roadside scenarios, the challenge of sparse point clouds leading to clustering difficulties and the issue of large vehicle point clouds dispersing, resulting in fragmentation, currently hampers the practical ap-plication of radar sensors. We propose an adaptive point cloud clustering algorithm based on DBSCAN. First, we propose an improved DBSCAN clustering algorithm based on distance and speed thresholds, which enhances the differentiation of point clouds between different vehicles, and an adaptive ellipse gate strategy to solve the large vehicle point clouds fragmentation problem. Then, a secondary clustering algorithm based on azimuth is exploited, effectively addressing the issues of large vehicle fragmentation and anomalous speed values. Practical roadside experimental results demonstrate that our proposed algorithm significantly outperforms traditional algorithms, showing considerable potential in practical applications.

Connected vehicles (CVs) utilizing cellular vehicle-to-everything (C-V2X) technology are increasingly coexisting on the road with regular vehicles (RVs). As these CVs interact with each other and with roadside infrastructure through vehicle-vehicle and vehicle-infrastructure cooperation, the characteristics of traffic flow are changing in significant ways. It is therefore crucial to understand how different parameters of CVs, roadside sensors, and V2X communications affect the stability of heterogeneous traffic flow. In this research, we investigate the impact of several transportation and infrastructure parameters on the stability of heterogeneous traffic flow. Specifically, we first examine the effects of traffic density, penetration rate of CVs, detection accuracy of roadside sensors, and time delays in V2X communications. We propose a novel C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture and develop a car-following model based on it. Then, the theoretical stability condition for heterogeneous traffic flow is derived, which reveals the interdependence of transportation and infrastructure parameters. The numerical simulations show that the proposed C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture achieves traffic flow stability at lower CV penetration rates compared to existing studies that only consider vehicle-to-vehicle communications. This finding highlights the importance of leveraging the full potential of C-V2X technology for improving traffic flow stability in real-world settings.

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.

Consider a communication system in which a single-antenna user equipment exchanges information with a multi-antenna base station via a reconfigurable intelligent surface (RIS) in the presence of spatially correlated channels and electromagnetic interference (EMI). To exploit the attractive advantages of RIS technology, accurate configuration of its reflecting elements is crucial. In this paper, we use statistical knowledge of channels and EMI to optimize the RIS elements for 1i) accurate channel estimation and 2) reliable data transmission. In both cases, our goal is to determine the RIS coefficients that minimize the mean square error, resulting in the formulation of two non-convex problems that share the same structure. To solve these two problems, we present an alternating optimization approach that reliably converges to a locally optimal solution. The incorporation of the diagonally scaled steepest descent algorithm, derived from Newton’s method, ensures fast convergence with manageable complexity. Numerical results demonstrate the effectiveness of the proposed method under various propagation conditions. Notably, it shows significant advantages over existing alternatives that depend on a suboptimal configuration of the RIS and are derived on the basis of different criteria.

Orbital angular momentum with index modulation (OAM-IM) presents an innovative information modulation scheme for OAM wireless communications, which is expected to achieve superior spectral efficiency (SE) and energy efficiency (EE) by utilizing a set of activated OAM modes to carry additional information based on the principle of IM. However, in the existing OAM-IM communication systems, the accurate alignment between the transmitter and receiver is required to avoid a larger performance penalty. To break the bottleneck, we present a novel misaligned OAM multi-mode multiplexing with IM (OAM-MMUX-IM) scheme based on uniform circular arrays (UCAs), including the generation of OAM-MMUX-IM signals and the multi-mode OAM detection. At the transmit UCA, the OAM-IM signals are designed to carry additional information by activating a subset of the available OAM modes. According to the characteristics of IM, the multi-layer back-propagation neural network (BPNN) is applied to the misaligned receive UCA for the first time to achieve the OAM multi-mode detection. The simulation results from MATLAB show that the presented OAM-MMUX-IM scheme can flexibly identify multiple OAM modes in the misaligned OAM system and achieve excellent SE and error performance.

With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem. To obtain the topology information of a noncooperative wireless network that could not be accessed by the monitoring sensors, we propose a topology inference algorithm based on the network two-dimensional spatiotemporal features (TDSTFs). Specifically, the monitoring sensor network monitors the power of the noncooperative network and locates the nodes of the noncooperative network exploiting the neural network (NN)-based method. Then, the communication time and distance between the noncooperative nodes are used as characteristics to infer the topology of the noncooperative network based on$K$-nearest neighbors (KNNs). Simulation results validate that the proposed TDSTF topology inference algorithm outperforms other topology inference algorithms that do not consider both spatial and temporal features and can greatly improve the inference accuracy.

The heterogeneous vehicular networks (HetVNets) can accelerate the deployment of Internet of Vehicles (IoV) and enrich the content distribution methods. However, the diverse requirements of vehicular users (VUs), the limited cache resources of roadside units (RUs), and the frequent interactions between VUs and RUs pose great challenges to efficiently distribute contents. To address these challenges, we propose an on-demand content delivery scheme in digital twin-enabled HetVNets (DT-HetVNets). Specifically, we first design an on-demand content delivery architecture in DT-HetVNets which uses DT communication mode to simplify the frequent interactions between VUs and RUs. With this architecture, by jointly considering the popularity of each content and the relevance between different contents, the personal content requirement of each DT of VU (DT-VU) can be perceived and the VUs within the coverage of the same RU can collaboratively request contents in groups. Then, we formulate the interaction between each group and the DT of the RU (DT-RU) as a double auction game to determine the transaction price of the perceived content, where the request information of the contents which are accepted by the groups can be shared between different DT-RUs based on the path of each group, enabling collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different DT-RUs and the content popularity, the content caching model of each DT-RU is formulated as a knapsack problem, where a collaborative content caching algorithm is designed to obtain the optimal caching strategy with the target of making full use of the limited cache resources. Compared with the conventional schemes, the simulation results show that our scheme can not only bring the highest utility to the RUs but also lead to the highest hit ratio and the lowest delay.

As a new degree of freedom (DOF), orbital angular momentum (OAM) has the potential of greatly enhancing the channel capacity of wireless communication systems. However, the OAM beams diverge with the increase of transmission distance and OAM mode, which makes the OAM beams modulated with information difficult to be received with a limited aperture array. Therefore, there is still a big challenge for achieving the expected extremely high data rate with orthogonal multiplexed OAM beams in a long-distance communication link. To reduce the divergence of OAM beams, the spherical Luneberg lens is proposed for the uniform circular array (UCA)-based OAM communication system to realize OAM beam convergence. However, due to the non-negligible UCA aperture relative to the Luneberg lens, we find through electromagnetism (EM) simulation that the 0-mode OAM beam becomes more divergent after passing through Luneberg lens. To solve this problem, we propose a hybrid circular array with a center element (CAC) and spherical Luneberg lens (HCCL) structure. When the HCCL structure is applied in the OAM wireless communication system, the divergence angles of generated OAM beams are significantly reduced, and the received signal-to-noise ratio (SNR) is expected to be significantly improved. Besides, the multiplexed OAM modes in the proposed HCCL-based OAM communication system keep orthogonal. Therefore, the channel capacities of the proposed HCCL-based OAM communication systems are obviously better than that of the existing UCA-based OAM system in the long-distance transmission. Furthermore, both numerical analysis and simulation results validate that the performance of the OAM system equipped with the HCCL structure both at transmitter and receiver is significantly better than that of the system equipped with the HCCL structure at one side

With the growing number of cars in cities, smart parking is gradually becoming a strategic issue in building a smart city. As the precondition in smart parking, accurate parking prediction can reduce the time drivers spend searching for parking spaces and relieve traffic congestion. Meanwhile, VANET and the Internet-of-things (IoT) are the key elements of the current intelligent transportation system. With the IoT devices based on VANET becoming more extensively employed, a large amount of parking data is generated every day, and various methods are proposed for parking prediction, therefore, it is time to systematically summarize the parking prediction issues and the state-of-the-art prediction methods. In this survey, we first provide a comprehensive review of the existing methods used for parking prediction ranging from conventional statistical methods to the latest graph neural network methods. Then, we classify a variety of parking problems such as parking availability prediction, parking behavior prediction, and parking demand prediction. We also compile all the evaluation metrics, open data, and open-source code of the surveyed literature. Finally, we present the challenges and future directions of the parking prediction technique. As far as we know, this is the first survey exploring parking prediction methods, which will be of interest to both researchers and practitioners engaging in intelligent transportation systems (ITS) and smart cities.

Terahertz (THz) wireless communications are commonly regarded as one of the key technologies of 6G communication. Combined with THz, the newly exploited physical layer transmission dimension orbital angular momentum (OAM) that multiplexes a set of orthogonal modes on the same frequency channel, can unleash its potential in achieving high spectrum efficiency. Currently, most of the research on radio OAM communications focus on the point-to-point scenario in microwave and millimeter-wave (mmWave) bands. In this paper, we propose a uniform circular array (UCA)-based THz multi-user OAM (MU-OAM) communication system including downlink and uplink transmission schemes that simplifies the signal detection to the despiralization and amplitude detection (AD). A salient feature of the proposed MU-OAM communication scheme is lower computational complexity than the traditional MU-MIMO scheme without sacrifacing bit error rate (BER) and achievable sum rate performances, which is validated by mathematical analysis and numerical simulations.

Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based OAM communication system to circumvent large performance degradation induced by small antenna misalignment in practical environment. However, in the case of large-angle misalignment, the OAM channel capacity cannot be effectively compensated only by the electronic beam steering. To solve this problem, we propose a hybrid mechanical and electronic beam steering scheme, in which mechanical rotating devices controlled by pulse width modulation (PWM) signals as the execution unit are utilized to eliminate the large misalignment angle, while electronic beam steering is in charge of the remaining small misalignment angle caused by perturbations. Furthermore, due to the interferometry, the receive signal-to-noise ratios (SNRs) are not uniform at the elements of the receive UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to maximize the channel capacity, in which the simulated annealing algorithm is adopted to obtain the optimal rotation angle at first, then the servo system performs mechanical rotation, at last the electronic beam steering is adjusted accordingly. Both mathematical analysis and simulation results validate that the proposed hybrid mechanical and electronic beam steering scheme can effectively eliminate the effect of diverse misalignment errors of any practical OAM channel and maximize the OAM channel capacity.

Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantages, in this paper, we design a novel radar-centric joint OAM radar-communication (RadCom) scheme based on uniform circular arrays (UCAs), which modulates information signals on the existing OAM radar waveform. In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets without beam scanning. Then, we derive the posterior Cramér-Rao bound (PCRB) of the OAM-based estimates and, finally, we analyze SE of the integrated system. To achieve the best trade-off between imaging and communication, the transmitted integrated OAM beams are optimized by means of an exhaustive search method. Both mathematical analysis and simulation results show that the proposed radar-centric joint OAM RadCom scheme can accurately estimate the 3-D position and rotation velocity of multiple targets while ensuring the SE of the communication receiver, which can be regarded as an effective supplement to existing joint RadCom schemes.

With the characteristics of self-organization and no network infrastructure, Ad Hoc network is widely used in civil and military communication. Critical node identification is an important factor in Ad Hoc network security. To evaluate the importance of nodes, we propose a critical node identification algorithm based on cascading failure from the overall performance of the network, which is called the connectivity loss potential (CLP) algorithm. Specifically, the connectivity loss potential is used as a measure to complete the preliminary screening of critical nodes and then expand horizontally and vertically according to the spatial locations of the initial node-set. Select the nodes that can greatly decrease the network's connectivity as the identification results of critical nodes. Simulation results show that the proposed CLP algorithm outperforms other critical node identification algorithms, leading to the networks collapse with fewer nodes removed.

For multi-user multiple-input multiple-output (MIMO) channel, the hybrid Tomlinson-Harashima vector perturbation (TH-VP) precoding with perfect channel state information (CSI) at transmitter is shown to approach the performance of the dirty paper coding. However, the CSI error is inevitable in practice. In this paper, we focus on the design of robust TH-VP (rTH-VP) precoding with quantized channel side information, which aims to minimize the mean-squared-error between the perturbed transmitted and received signal vectors. In particular, each receiver decomposes its downlink channel matrix in forms of the newly defined channel direction information (CDI) and channel magnitude information (CMI) for feedback to the transmitter. Bit error rate results indicate that (i) different from conventional VP precoding the feedback of CMI is not necessary for TH-VP precoding, and (ii) the proposed rTH-VP precoder is less sensitive to quantization errors.

The space-air-ground integrated vehicular networks (SAGIVNs) can efficiently accelerate the deployment of the Internet of Vehicles (IoV) and enrich the content distribution methods in the networks. In this paper, we propose a content delivery scheme in SAGIVNs that integrates sensing, communication, and caching. Specifically, we first perceive the content requests of the vehicles through which the vehicles covered by the same roadside unit (RU) can be facilitated to request the contents collaboratively. Then, based on the location and path of each vehicle, the perceived request information can be transmitted between different RUs, enabling efficient collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different RUs, the popularity of each content, and the limited cache resources, the content caching model of each RU is formulated as a knapsack problem, where a dynamic programming method is designed to obtain the optimal caching strategy. Compared with the conventional schemes, the simulation results show that the proposed scheme can lead to the highest hit ratio and the lowest transmission delay.

The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time critical, ultrareliable, and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On the one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This article first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then, we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.

The vehicle localization, which aims to identify a vehicle and then position the vehicle with a high precision, can be used to facilitate various applications and services in vehicular networks. Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet the accuracy requirements especially in certain specific scenarios, such as tunnels. At the same time, Ultrahigh frequency (UHF) radio frequency identification (RFID) has become an efficient booster for internet of things (IoT) due to the desirable advantages, such as low cost, battery-free, and unique identification. In this paper, based on the UHF-RFID, we propose a novel real-time vehicle localization scheme in GPS-Less Environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we adopt single antenna multi-frequency ranging scheme, in which the integer ambiguity problem is solved by the maximum-likelihood estimation (MLE)-based robust Chinese remainder theorem (CRT). With the reconstructed distances between the tags and the reader, the coordinates of the vehicle then can be calculated with the Levenberg-Marquardt (LM) algorithm. Furthermore, the computational complexities of the algorithms and the time consumption of the proposed scheme are analyzed. The experimental results demonstrate that the proposed scheme can track vehicle’s location with error lower than 27 cm at the probability of 90%.

Efficient path planning is the key enabling technology for the realization of intelligent transportation systems (ITS). However, due to poor real-time performance and lack of effective incentive methods, it is difficult for traditional path planning schemes to significantly improve the efficiency of traffic management. In addition, existing solutions that use driving distance and driving time as indicators cannot meet the personalized requirements of vehicle users. To this end, by considering the personalized requirements of vehicle users, we propose a digital-twin (DT) enabled path planning scheme to facilitate traffic management. To be specific, based on the collection of traffic data, we first establish a DT architecture for traffic scheduling to reduce the delay of path planning. Then, according to the traffic density of different road sections, we regard road sections as resources and set different rewards for different road sections to encourage vehicles to obey the scheduling instructions. In addition, by jointly considering the driving time and rewards, we further design personalized utility models to map the requirements of different vehicle users. After that, based on the personalized requirement of the vehicle user, we use a$Q$-learning algorithm to obtain the optimal path with the target of maximizing the user's utility. The simulation results show that the proposed scheme can bring higher utility to the vehicle users than the conventional schemes.

Collaborative localization methods employed in various scenarios have been extensively investigated. As a widely concerned collaborative localization method, belief propagation (BP) has a good positioning performance when a large portion of nodes have available prior location information such as GPS. However, the localization accuracy of BP deteriorates in GPS-challenged environments. Therefore, in this paper we employ multidimensional scaling (MDS) to obtain the prior locations of nodes in GPS-challenged environments, then the preferable initial location information is supplied to BP. We show that the two algorithms can be integrated with single range measurements among nodes and that the proposed MDS-BP algorithm greatly enhances the positioning accuracy of BP. Even in GPS-denied environments, the proposed algorithm is still robust and can provide a relative map with high accuracy.

In recent years, unmanned aerial vehicles (UAVs), especially swarming UAVs are widely deployed in a variety of Internet-of-Things (IoT) scenarios. Since UAVs' positions are essential for their collaboration, high-precision localization for swarming UAVs has attracted a lot of attention. Although the global positioning system (GPS) receiver has been widely integrated in UAV, it is not accurate enough and is prone to accidental or deliberate interferences. In this article, we propose a distributed and collaborative localization method for swarming UAVs that combines super multidimensional scaling (SMDS) and patch dividing/merging with GPS information. Specifically, the SMDS is first used to get the relative coordinates of the UAVs in each patch, then we merge relative map patches into a global map and transform the relative coordinates of the UAVs to their absolute coordinates. Furthermore, we propose a low-complexity algorithm that greatly reduces the computational complexity of SMDS with a large number of UAVs. Simulation results validate that with accurate enough angle measurements, the proposed SMDS localization algorithm outperforms the other MDS-based collaborative localization algorithms and can greatly improve the localization accuracy and robustness of swarming UAVs.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, OAM communication system requires perfect alignment of the transmit and the receive antennas and this harsh precondition greatly challenges practical multiplexing gain of OAM system. In this paper, we propose a UCA-based multi-mode OAM reception method including the beam steering with the estimated angle of arrival (AoA) and the amplitude detection with the estimated distance. Both mathematical analysis and simulation results validate that the proposed OAM reception method can completely eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.

Orbital angular momentum (OAM) multiplexing is considered to have the potential of further improving the spectral efficiency of communication systems. However, a common uniform circular array (UCA)-based multi-mode OAM communication system using discrete Fourier transform (DFT) and inverse DFT (IDFT) has high peak-to-average power ratio (PAPR), which reduces the radio frequency power amplifier (PA) efficiency and degrades system performance. In this paper, a constant envelope OAM (CE-OAM) system is proposed to achieve the minimum signal PAPR to maximize the PA efficiency. The transmitter using a phase modulator converts the high PAPR signal into constant envelope signal with $0$dB PAPR, and the receiver performs inverse transform after OAM symbol detection, followed by the conventional OAM demodulation. Simulation results validate the feasibility of the proposed CE-OAM system and shows that CE-OAM system with appropriate modulation coefficient outperforms conventional OAM system in both PAPR and bit error rate (BER) performances.

Combined with block diagonalization (BD) methods, Tomlinson-Harashima (TH) precoding and vector perturbation (VP) are attractive non-linear precoding techniques for multi-user multiple-input multiple-output (MU-MIMO) broadcast channels. In this paper, we first briefly review several non- linear precoding schemes including BD-VP, TH and zero-forcing (TH-ZF) and TH-VP, and then derive the achievable sum rate of the hybrid TH-VP precoding. After that, we compare the achievable sum rates and diversity gains of these non-linear precoding schemes with zero-forcing VP (ZF-VP) and dirty paper coding (DPC). The achievable sum rate of TH-VP is shown to approach the sum capacity of DPC asymptotically and outperforms BD-VP, TH-ZF and ZF- VP.

As being able to share radio frequency (RF) hardwares and spectrum bandwidth, joint communication and radar sensing system has attracted a lot of attention. The integrated waveforms based on orthogonal frequency-division multiplexing (OFDM) with fast Fourier transform (FFT-OFDM) and OFDM with discrete fractional Fourier transform (DFRFT-OFDM) have been developed. However, the existing derived ambiguity function does not distinguish the two waveforms clearly. In addition, few range and velocity estimation algorithm for DFRFT-OFDM-based integrated waveform has been investigated. Therefore, in this paper we derive the ambiguity function of the DFRFT- OFDM-based waveform based on wideband analysis, which shows the superiority of the DFRFT-OFDM-based integrated waveform on range resolution. Moreover, range and velocity estimation algorithms based on pulse compression and coherent accumulation are proposed for the DFRFT-OFDM-based integrated waveform, respectively. Simulation results validate that the proposed algorithms can accurately estimate the distances and velocities of multiple moving targets.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, OAM communication system requires perfect alignment of the transmit and the receive antennas and this harsh precondition greatly challenges practical application of OAM mode multiplexing gain. In this paper, we first investigate the effect of non- parallel misalignment on the inter-mode interference of the RF-OAM communication system equipped with uniform circular array (UCA). Then, large-scale system analysis shows that increasing the number of antennas has little effect on the inter-mode interference induced by oblique angle, but beam steering approach could eliminate the inter-mode interference completely. Numerical simulations validate our conclusion on the effect of beam steering.

In order to meet the requirement of dual-band MIMO operation in 5G smartphones, a 12-port dual-band inverted F-shaped antenna array for 3.5 GHz (3400- 3600 MHz) and 5.8 GHz (5725-5785 MHz) bands is designed. The proposed dual-band antenna array elements are located symmetrically on the inner surface of side frames instead of on the circuit board. Thus, the structure without ground clearance along the long edges is suitable for wide-screen smartphones. To embed 12 antenna elements into the limited space of a smartphone without additional decoupling structures, we propose a structure that bend the branches of inverted-F antenna to a smaller size (8mm×6mm) reducing the occupied space of antennas. A prototype of the proposed 12-antenna array is manufactured and measured. Both simulation and experimentally measured results show that our designed antenna array achieves desirable antenna impedance matching (better than 6/10 dB return loss), acceptable isolation (better than 10 dB) and antenna efficiency (better than 40%). Envelope correlation coefficients and channel capacities are also calculated to validate that the proposed dual- band antenna arrays have good radiation and MIMO capacity performance.

MmWave systems with most prior work focused on its narrowband hybrid analog/digital precoding, however, will likely operate on wideband channels with frequency selectivity. Therefore, in this paper we investigate wideband angular beamforming schemes for mmWave massive MIMO-OFDM systems. First, for the RF analog beamforming, the optimal beamforming of an unconstrained antenna array (UAA) is given as the performance benchmark. Then, the optimal angular beamforming (OAB) and a simple dominant angular beamforming (DAB) of a shared antenna array (SAA) are compared in received SNR and implementation cost. Second, for the baseband digital precoding, the space-frequency vector perturbation (SFVP) precoding is proposed to collect both spatial and multi-path diversity. Finally, analytical and simulation results show that: a) DAB is a cost- effective RF beamforming scheme under LOS channel environment; b) the proposed hybrid DAB-SFVP beamforming scheme achieves the array gain equaling the number of transmit antennas Ntand diversity gain equaling the product of the number of RF chains K and the number of temporal resolvable clusters ℒ.

Orbital angular momentum (OAM) provides extra degrees of freedom to improve spectrum efficiency. However, the harsh requirement that transmit antenna(s) and receive antenna(s) need aligning throws out a great challenge to its application. To investigate the reason of performance degradation caused by misalignment, we first consider uniform circular array (UCA) based OAM system and set up two free space channel models respectively for two misalignment case, i.e., non- parallel and off-axis. Then, the capacities of single- mode and multi-mode OAM system are analyzed and compared under different oblique angles and axis deviations. Analysis and simulation results show that OAM transmission especially the multi-mode OAM is quite sensitive to the misalignment between transmitter and receiver. At last, we propose a misalignment-robust receiving method through extracting the phases of channel state information (CSI) and implementing joint phase compensation and signal detection. Simulation results show that proposed receiving scheme is more robust to misalignment than conventional receiving scheme.

This manuscript deals with the modeling, analysis, and measurement of a small scale fading (SSF) mobile radio channel. The physics of SSF are first reviewed to reveal the generating mechanisms of channel selectivity. A stochastic channel model is then derived as a function of time, antenna array displacement and frequency, which falls in the category of tapped delay line model. Specifically, the taps can be represented as a combination of a possible line of sight or dominant reflected path and a Gaussian distributed component comprised of unresolvable scattered paths. After that, general analytical solutions are provided for the 3D temporal-spectral-spatial correlations of SSF via the exploitation of channel statistical properties. We show that this function can be expressed as the product of three low order temporal, spectral and spatial correlations individually under appropriate assumptions on the associated wireless propagation environment. This will definitely facilitate the derivations of the closed form expression regarding the correlation function of SSF. From engineering perspective, this analysis can be utilized to develop network correlation maps for example. Finally, out field measurement results verify the validity of our theoretical analysis.

Vector perturbation (VP) is a prominent precoding technique attracted a lot of attention in recent years. Until now, however, various extended VP techniques proposed to apply in multiuser precoding are almost restricted to one antenna configuration of each user. The restriction does not meet the development of next generation wireless systems. So, the well-known block diagonal (BD) algorithm and VP is naturally combined and proposed, named BD-VP for short, to solve this problem. However, the BD-VP completely suppressing multi user interference (MUI) at the expense of noise enhancement results in performance degradation. To overcome the shortcoming of BD-VP, we propose generalized channel inversion VP (GCI-VP) algorithms. Analysis and simulation results show that the proposed ZF GCI-VP is equivalent to the BD-VP, while the algorithm MMSE GCI-VP I and MMSE GCI VP II greatly outperform the BD-VP.

In point to point MIMO systems, uniform channel decomposition (UCD) has been proven to be optimal in BER performance and strictly capacity lossless when perfect channel state information (CSI) are assumed to be available at both the transmitter and receiver side. However, in practice, CSI is always contaminated by channel estimation error. In this paper, we proposed a novel Robust UCD scheme which is capable of improving the BER and capacity performance in the context of imperfect CSI compared with the conventional UCD scheme. We also derived the capacity lower bound of the MIMO channel using the Robust UCD scheme with channel estimation error.