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This paper proposes trajectory planning considering vehicle-to-vehicle (V2V) communication delay on connected and autonomous vehicles (CAVs). Firstly, the major influencing factors of communication delay are analyzed as the mean and standard deviation of probability distributions. Gaussian distribution is used to describe the probability distribution of communication delays. Markov chains are used to describe the probability distributions of delayed state shifts to calculate the probability distributions of associated vehicle trajectories. Secondly, the corridor expansion is performed based on the maximum probability trajectory of the associated vehicles. Therefore, the trajectory planning is constructed based on the corridor expansion of associated vehicles. Improved constraints and objective functions are defined, and experiments are conducted using the quintic polynomial of the Frenet frame. The results showed that the best stability is achieved when the trajectory planning duration is 6.0 s. Meanwhile, the stability of the model is better in all cases when the trajectory frequency is between 0.2 s and 0.5 s. By comparing and analyzing the model in this paper, the traditional human trajectory planning model and the state sampling trajectory planning model, it is found that the model in this paper is the best choice in terms of stability, safety and accuracy.

In view of the poor robustness of the integrated navigation system based on SINS/DVL in complex environments, an improved robust filter method for SINS/DVL system with earth frame error model is proposed in this paper. On the one hand, based on the traditional navigation frame SINS/DVL navigation system, a system mechanization for the strap-down inertial navigation algorithm and an integrated navigation algorithm based on the earth frame error model are designed. This method realizes the SINS/DVL integrated navigation method under the earth frame error model by recursively recursing the state equation of the earth frame, the measurement equation and the conversion between the earth frame and the navigation frame. On the other hand, based on the Huber M filter algorithm, an improved robust filter algorithm is proposed. The algorithm combines chi-square detection for pre-judgment, and introduces the backward Kalman filter algorithm. The improved algorithm can suppress the influence of continuous non-Gaussian strong noise and improve the filtering accuracy and stability. Finally, the validity of the method proposed in this paper is verified by the simulation and lake test.

At present, trajectory planning and tracking control methods for autonomous vehicles (AVs) are complicated and mainly suitable for specific scenarios. In this paper, based on vehicle dynamics model, a novel approach of cleverly introducing the velocity-related safe distance into the artificial potential field function is proposed. The improved traffic environment potential field is then combined with model predictive control (MPC), and lateral stability constraints such as lateral load transfer rate are added to the MPC when solving for the control quantity. We name this method AutoPField-MPC, and it offers the following advantages: First, it allows AVs to automatically adjust the distance from other obstacles in more complex scenarios, while also dynamically influencing steering and braking time. Second, it reduces extensive parameter adjustment work when considering the safe distance. Third, the improved design of the potential field function in this paper makes the method more concise and universal. The simulation results from CarSim and Simulink demonstrate that the method presented in this paper can simultaneously achieve multiple active safety objectives, such as speed maintenance, self-adaptive distance adjustment, lane keeping, lateral stability, emergency braking, and emergency steering, with good real-time performance. As a result, it can effectively address the security issues of vehicles and pedestrians when AVs avoid obstacles in various complex scenarios, such as wet roads, multi-obstacle vehicles, and emergency obstacle avoidance.

This paper proposes an air-ground anti-jamming controlled radiation pattern antenna (CRPA) communication system, in which a bent monopole antenna with a simple structure is adopted, and a circular array composed of four such monopoles can be finely used in air-ground communication because the 3-dB beam width of the array is about 128° (-64°∼64°). Thanks to the ability of each antenna to provide different electric field components, therefore the proposed array supports multiple polarization modes for wireless communication within the desired beam width. Different from the traditional research on algorithm anti-jamming, this paper proposes a new anti-jamming system architecture, which integrates the proposed antenna array and adaptive beamforming algorithm effectively to form a CRPA system, which is not only low cost but also strong anti-jamming ability, and its practicability is demonstrated by testing the circuit system in anechoic chamber environment.

This paper investigates the secure transmission problem in a cache-aided relaying network, where we assume that the source sends data to the destination via multiple cache-aided relays and the communication from the relays to the destination is wiretapped by an eavesdropper. Unlike the ideal assumption of Gaussian input signals, this paper considers practical finite-alphabet inputs, such as phase shift keying (PSK) and quadrature amplitude modulation (QAM). Our objective is to maximize the achievable secrecy rate by optimizing the beamforming vectors and power control at the source and relays as well as caching coefficient under constraints of transmit power, quality of service, and network congestion. For the formulated nonconvex multi-variable problem, we use the M/G/1 queueing theory to analyze the network congestion performance and reveal that the congestion decreases with increasing caching coefficient and increasing source-to-relay link rate but increases with increasing relay-to-destination link rate. Then, it is shown that the formulated problem can be solved by dividing it into two optimization subproblems. Subsequently, an auxiliary-variable based alternating optimization (AVAO) framework is developed, where the beamforming at each relay is designed by introducing the Moreau-Yosida regularization (MYR) technique and rank-one decomposition procedure (RDP) and the corresponding power control is derived by applying the optimality condition. Also, the complexity and convergence of the proposed framework are analyzed. Simulation results verify the efficacy of the proposed framework. Specifically, the algorithm based on finite-alphabet inputs proposed in this paper exhibits superior performance compared with the algorithm based on Gaussian inputs. Moreover, the secrecy rate increases with the congestion probability tolerance, the caching coefficient threshold, and the number of relays.

Channel modeling is considered as a fundamental step in the design, deployment, and optimization of vehicular wireless communication systems. For typical vehicular communication scenarios in urban areas, dense multipath may exist in the wireless channels. The propagation graph (PG) method is an efficient approach to simulate multipath radio propagation. In this paper, we extend the PG method into a Graph Neural Network (GNN) enabled data-driven method for calculating channel transfer function (CTF) and channel impulse response (CIR) in a given space. ChebNet, a classical GNN, is utilized for estimating the scattering coefficients of the edge gains in the PG method. The proposed GNN-enabled method performs better than baseline algorithms, such as multilayer perceptron (MLP), simulated annealing (SA) algorithm, and genetic algorithm (GA) in effectively estimating a large number of scattering coefficients in PG. Mean absolute errors of the proposed method are provided and evaluated in this paper. Additionally, the potential future research directions of the GNN-enabled PG method for channel modeling are discussed.

This paper proposes a novel LiDAR inertial odometry (LIO) incorporating vehicle motion characteristics (VMC) tailored for ground vehicles. While most advanced LIO systems are designed for 6 degrees of freedom (DOF) SE(3), which are not ideally suited for ground vehicles, this paper investigates the performance of the vehicle-LiDAR-IMU system by considering the vehicle motion characteristics. The system combines IMU propagation, LiDAR measurements, ground surface measurements and 3 dimension (3D) vehicle kinematic model measurements within an iterated Kalman filter framework. To ensure a robust system bootstrapping, the temporal and spatial parameters of the vehicle-LiDAR-IMU system are calibrated first. Then, in addition to the IMU preintegration and LiDAR measurements, constraints are established between the ground surface and the vehicle pose by calculating the normal of the ground beneath the vehicle in the local point cloud map. Moreover, measurements of the 3D vehicle kinematic model with angular velocity and velocity constraints are adopted. Ground surface is considered in the 3D kinematic model for vehicle angular velocity calculation. The results from simulations and vehicle experiments demonstrate that the proposed method improves accuracy compared with state-of-the-art LiDAR SLAM methods while maintaining a real-time implementation capability for vehicles.

The paper considers a more challenging task offloading scenario in hybrid UAV-assisted mobile edge computing (MEC) systems, where multiple dual-function UAVs tour in the sky to serve ground users (GUs) by acting as edge servers or aerial relays. Since each task can be executed on GUs, UAVs and the base station (BS) in parallel, the service assignment, task splitting, trajectory of UAVs, as well as resource and transmission power of both UAVs and GUs should be jointly optimized to minimize the system energy consumption with the subjection of the maximum tolerable latency and computing limitations. To tackle such mixed integer non-linear programming (MINLP) problem, a deep reinforcement learning (DRL) combined successive convex approximation (SCA) algorithm is proposed in the paper to seek a close optimal solution with low-complexity. In specific, the binary service assignment and continuous task splitting are obtained by DRL, while the trajectory planning and resource scheduling are jointly optimized by SCA in sequence to speed up the convergence. Numerical results demonstrate that the proposed DRL-SCA algorithm equipped with dual-function UAV scheme is more effective in making full use of the on-board resource of UAVs and reducing the overall system energy consumption.

Trajectory drift is a significant issue in intelligent vehicles that rely on multi-source fusion for achieving autonomous navigation, hindering the accurate positioning of these vehicles. Existing loop-closure algorithms mostly rely on single-sensor data, such as visual or LiDAR (Light Detection and Ranging) data, making their performance susceptible to degradation in challenging environments. This paper proposes a novel loop-closure detection algorithm. It employs ORB (Oriented FAST and Rotated BRIEF) feature points from visual data for loop-closure candidate frame detection, confirms loop-closure frames using depth values mapped from LiDAR data onto images, and finally utilizes GNSS (Global Navigation Satellite System) raw data to validate and correct trajectory drift further. The paper concludes by validating the stability and robustness of the proposed loop-closure algorithm through simulations with various publicly available datasets, thereby assessing its performance in large-scale and complex environments. Furthermore, by integrating the proposed loop-closure algorithm into an openly accessible online odometer, the results demonstrate an improvement in position estimation accuracy by 83.45%.

Reconfigurable intelligent surfaces (RISs) have attracted increasing interest from both academia and industry, thanks to their unique features on controlling electromagnetic (EM) waves. Although theoretical models for RIS-empowered communications have covered a variety of applications, yet, very few papers have investigated the modeling of real propagation characteristics. In this paper, we fill this gap by providing an empirical statistical channel model to describe the small-scale channel variations for an RIS-assisted broadband system at 2.6 GHz. Based on empirical channel measurements in outdoor, indoor and outdoor-to-indoor (O2I) environments, we compare and analyze the global, inter-cluster and intra-cluster parameters. Measurement results indicate that the deployment of an RIS with proper phase configurations can significantly improve the channel quality by enhancing the $K$-factor and reducing the time dispersion. The small-scale fading is well characterized by the proposed statistical model and empirical channel parameters. These results are essential for the design of emerging RIS-assisted wireless systems for future applications.

With the rapid development of edge computing technology, edge-assisted unmanned aerial vehicle (UAV) networks have become popular, helping with fast and cost-effective data collection in mobile crowdsensing (MCS) environments. This paper investigates the online data collection problem for MCS over an edge-assisted UAV network architecture, where UAVs work to collect the data required by tasks at different on-ground point-of-interests (PoIs) in an autonomous and cooperative manner. Different from conventional edge-assisted UAV networks, edge nodes in our paper help distribute, aggregate, share, and update the task state information (TSI, e.g., if a task has been completed, or if it still requires more data), to support efficient and cost-effective data collection, e.g., avoiding repetitive and ineffective task execution. In particular, a UAV can exchange TSI with an edge node when it is within the signal coverage of the edge node, while making decisions on PoI selection and path planning in an online and decentralized manner. To address the issue of edge-assisted UAV data collection, we propose a multi-agent deep reinforcement learning-based algorithm using personalized training with decentralized executing (PTDE) architecture. Different from the traditional centralized training with decentralized executing (CTDE) architecture, our considered architecture adopts the agent-specific state for critic networks instead of the joint observation, thus achieving effective utilization of environmental information. Furthermore, we propose an observation enhancement algorithm based on artificial potential field (APF). Extensive simulation results demonstrate that our proposed algorithm greatly outperforms baseline algorithms in terms of total profit, data collection ratio, geographical fairness, and energy efficiency.

This paper presents a study on asynchronous Federated Learning (FL) in a mobile network setting. The majority of FL algorithms assume that communication between clients and the server is always available, however, this is not the case in many real-world systems. To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL. By exploiting mobility, the study shows that clients can indirectly communicate with the server through another client serving as a relay, creating additional communication opportunities. This enables clients to upload local model updates sooner or receive fresher global models. We propose a new FL algorithm, called FedMobile, that incorporates opportunistic relaying and addresses key questions such as when and how to relay. We prove that FedMobile achieves a convergence rate $O(\frac{1}{\sqrt{NT}})$, where $N$ is the number of clients and $T$ is the number of communication slots, and show that the optimal design involves an interesting trade-off on the best timing of relaying. The paper also presents an extension that considers data manipulation before relaying to reduce the cost and enhance privacy. Experiment results on a synthetic dataset and two real-world datasets verify our theoretical findings.

This study constructs a decision-making control framework for autonomous ego vehicles (AEVs) based on Soft Actor-Critic (SAC) in a random driving task scenario at an unsignalized intersection. The environment vehicles include both AEV and surrounding vehicles, and the three driving tasks through unsignalized intersections are going straight, left turn, and right turn. Since the driving tasks of AEV and surrounding vehicles are random, the environment is characterized by high uncertainty and difficulty. There are three innovative points in this paper. First, this paper proposes a new Mix-Attention Network based on the attention mechanism. Second, this paper improves the state by introducing a new input quantity to represent the driving task of the vehicle itself. Third, this paper has been enhanced in replay buffer, using more collision and arrival experiences to train the neural network. In this paper, the performance of the original and improved models is evaluated in terms of safety and efficiency. The simulation results show that all three proposed improvement methods can improve performance and achieve better results.

Massive MIMO systems are appropriate technology for providing service to numerous high-rate Internet of Things (IoT) devices. In uncoordinated access, devices are not required to inform the BS when they have transmitted the pilot. Consequently, the current paper focuses on channel tracking of both active and inactive devices in an uncoordinated practical scenario. To this end, some suboptimal algorithms are introduced due to the intractability of the optimal uncoordinated practical scenario (O-UPS) tracker. An efficient MMSE-UPS (M-UPS) tracker is developed by inferring the concept of the minimum mean square error (MMSE) algorithm per access slot, resulting in a significant reduction in complexity. The performance of M-UPS is shown to be monotonically non-decreasing, concerning the increase in the activity probability of devices. When the channel state is stable, the following results are analyzed: I) upper and lower bounds for the MSE of M-UPS, II) stability and convergence of M-UPS, and III) a tight upper bound on the efficiency degradation between O-UPS and M-UPS. Furthermore, a necessary and sufficient condition to prove the stability of M-UPS for an unstable channel state is characterized. It is demonstrated that, under a certain assumption, the M-UPS tracker's error converges to O-UPS. More importantly, this paper indicates that the stability and convergence of M-UPS are identical to those of O-UPS, which is a significant property of M-UPS. Moreover, a new heuristic tracker is developed, and its stability under a necessary assumption is examined using Riccati difference equations. Finally, the simulation results reveal the designed trackers' performance.

This paper presents a new backscattering communication (BackCom)-assisted non-orthogonal multipleaccess (BAC-NOMA) transmission scheme for device-to-device (D2D) communications. This scheme facilitates energy and spectrum cooperation between BackCom devices and cellular downlink users in 6G ultra-massive machine-type communications (umMTC) scenarios. Given its quasi-uplink nature, the hybrid successive interference cancellation (SIC) is applied to further improve performance. The data rate of BackCom devices with high quality of service (QoS) requirements is maximized by jointly optimizing backscatter coefficients and the beamforming vector. The use of hybrid SIC and BackCom yields two non-concave sub-problems involving transcendental functions. To address this problem, this paper designs and compares convex optimization-based and unsupervised deep learning-based algorithms. In the convex optimization, the closed-form backscatter coefficients of the first sub-problem are obtained, and then semi-definite relaxation (SDR) is utilized to design the beamforming vector. On the other hand, the second sub-problem is approximated by using a combination of sequential convex approximation (SCA) and SDR. For unsupervised deep learning-based optimization, a loss function is properly designed to satisfy constraints. Computer simulations show the following instructive results: i) the superiority of the hybrid SIC strategy; ii) the distinct sensitivities and efficacies of these two algorithms in response to varying parameters; iii) the superior robustness of the unsupervised deep learning-based optimization.

A novel design of reconfigurable intelligent surfaces (RIS)-aided MIMO downlink communications is proposed in this paper. The new design employs successive zero-forcing dirty paper coding at base station and allows users to have multiple antennas for receiving, making it much more realistic for modern wireless communications. Furthermore, the new design uses RIS elements with finite and low resolution of phases and is highly practical for implementation. Simulation results show that the proposed design significantly improves the performance of RIS-aided downlink communications. In particular, for the classical scenario of single-antenna users, the design using only QPSK phases for RIS elements increases by more than 35–347% the energy efficiency of several existing works that require an infinite phase resolution for RIS elements. When applying to the MIMO scenario, the design can further double the energy efficiency.

This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood ratio (GLR) test. For all but one of the unknown parameters, it is possible to find closed-form maximum likelihood (ML) estimator functions. We can further compress the likelihood to only an unknown vector whose ML estimate requires maximizing a product of ratios in quadratic forms, which is carried out using a trust-region algorithm. We propose two approximations of the GLR that do not require any numerical optimization: one based on a sample-based estimator of the unknown parameter whose ML estimate cannot be obtained in closed-form, and one derived under low-SNR conditions. Notably, all the detectors are scale-invariant, and the approximations are functions of beamformed data. However, they are not GLRTs for data that has been pre-processed with a beamformer, a point that is elaborated in the paper. These detectors outperform previously published correlation detectors on simulated data, in many cases quite significantly. Moreover, performance results quantify the performance gains over detectors that assume only the dimension of the subspace to be.

Triggered by the introduction of higher frequencies (above 24 GHz), there has been a long-standing debate in the radio propagation community on whether higher frequency radio channels are sparser relative to channels below 6 GHz. Here, sparsity implies a few dominant multipath components containing the vast majority of the electromagnetic energy. This discussion has recently been revisited with the study and interest in bands above 100 GHz for future wireless access. In this paper, the level of sparsity is examined at 6, 26, and 105 GHz carrier frequencies by conducting channel measurements in an indoor office environment. By using the Gini index (value between 0 and 1) as a metric for characterizing sparsity, we show that increasing carrier frequency leads to increased levels of sparsity. The measured channel impulse responses are used to derive a Third-Generation Partnership Project (3GPP)-style propagation model, used to calculate the Gini index for the comparison of the channel sparsity between the measurement and simulation based on the 3GPP model. Our results show that the mean value of the Gini index in measurement is nearly twice the value in simulation, implying that the 3GPP channel model does not capture the effects of sparsity in the delay domain as frequency increases. In addition, a new intra-cluster power allocation model based on measurements is proposed to characterize the effects of sparsity in the delay domain of the 3GPP channel model. The accuracy of the proposed model is analyzed using theoretical derivations and simulations. Using the derived intra-cluster power allocation model, the mean value of the Gini index is 0.91 in the sub-THz band, while the spread of variability is restricted to 0.01, demonstrating that the proposed model is suitable for 3GPP-type channels. To our best knowledge, this paper is the first to perform measurements and analysis across a wide range of frequencies for the evaluation of channel sparsity in the same environment.

This paper considers intelligent reflecting surface (IRS)-assisted communication systems in the presence of arbitrary interfering signals. The IRS reflects both the desired and interference signals through the same fading channels towards the user equipment. In this scenario, the signal-to-interference-plus-noise ratio might become a ratio of correlated random variables. Therefore, this paper aims to identify practical scenarios in which it is acceptable to disregard or necessary to consider the correlation between the desired and interference signals sharing the same fading channels. Furthermore, in the presence of correlated desired and interference signals, the analytical expressions presented in the existing literature are inadequate for evaluating the system performance. To address this limitation, new accurate expressions for the ergodic capacity, outage probability, and bit error rate are derived, which are applicable to both correlated and uncorrelated scenarios. The accuracy of the newly derived expressions is then validated by Monte-Carlo simulations. In contrast to the existing literature, it is revealed that when interferers have line-of-sight (LOS) components, the desired and interference signals are always correlated. It is also revealed that, in the presence of LOS interference, for a very large number of reflecting elements, the system performance reaches a limit, which cannot be improved by increasing the number of reflecting elements.

In this paper, the direction of arrival (DOA) and orientation of array (OOA) estimation problems in the recently proposed orientational beamforming (OBF) system are investigated. We show that existing DOA estimation methods can be directly applied to estimate the DOA information in the OBF system. Therefore, this paper mainly focuses on the OOA estimation problem. Three OOA estimation methods are first proposed based on the assumption of accurate DOA estimation. In each method, the OOAs of different transmitting arrays are estimated individually by searching in the one-dimensional parameter space. If the power spectral density (PSD) of the source signal emitted from a transmitting array is known, the OOA of the transmitting array can be estimated by the proposed PSD-based least squares method. Otherwise, the electronical rotation-based OBF (ER-OBF) and single-source-array OBF (SSA-OBF) methods are proposed. In both methods, the OOA of a transmitting array is estimated by detecting the peak in its corresponding orientational beampattern obtained by electronically rotating the receiving array. The electronical rotation of the receiving array is achieved by appropriately delaying received signals according to the DOA of the source signal. Based on the proposed ER-OBF method, the SSA-OBF method further proposes to use a transform matrix to suppress the mutual interference between signals from different transmitting arrays. Simulation results show that the probability of successful estimation of the proposed methods increases as the signal bandwidth and array aperture size increase. Moreover, observing that the DOA estimation error impacts the performance of the proposed OOA estimation methods, a modified random search method is developed to achieve OOA estimation in the presence of DOA estimation error.

The emergence of reconfigurable intelligent surfaces (RIS) provides a novel perspective on channel interference suppression to enable the interference signals to mutually cancel out over the air. However, mostly existing research on interference suppression using RIS is based on the assumption of continuous reflection coefficients (CRC), which is not feasible due to hardware limitations in the real world. This paper explores the efficacy of RIS in interference suppression over-the-air with discrete reflection coefficients (DRC). First, the paper formulates an interference suppression ratio (ISR) maximization problem to optimize the DRC and proposes an algorithm based on semidefinite relaxation to solve the ISR optimization problem. Then, the upper and lower boundaries of ISR under the proposed algorithm are derived to provide more insight into its performance. The simulation shows that interference channel gain can be suppressed by 32 dB to 40 dB under DRC limitations.

This paper proposes a novel differential steering method (DSM) for distributed-drive articulated heavy vehicles (DAHVs) through differential control of wheel-side motors. The objective is to improve steering performance of DAHVs while negating the need for a conventional hydraulic steering method (HSM). A differential steering control framework that is combined with a composite nonlinear feedback (CNF) control strategy and active disturbance rejection control (ADRC) technique is proposed. Three main contributions are made in this paper: 1) A differential steering control method for DAHVs is proposed for the first time. It uses the yaw rate control of the front vehicle part to obtain a suitable direct yaw moment to guide the steering control of the rear vehicle part and achieve the vehicle steering process. This method can realize the collaborative control of a DAHV with two relatively independent vehicle parts. 2) The CNF control strategy is employed to design the nominal part of the differential steering controller to eliminates steady-state errors and guarantee transient steering performance of DAHV. 3) The ADRC technique is adopted to achieve disturbance estimation and compensation, so as to reduce the influence of disturbance on the differential steering process and improve the vehicle steering performance. The system's stability is proven by the Lyapunov approach. Moreover, a co-simulation model verified by field tests is utilized to demonstrate the effectiveness of the proposed control approaches.

This article proposes, and evaluates the benefit of, one particular hybrid satellite-high-altitude-platform-station (HAPS)-ground network, where one HAPS connected to one geo-satellite assists the ground base stations (BSs) at serving ground-level users. The paper assumes that the geo-satellite is connected to the HAPS using free-space-optical backhaul links. The HAPS, equipped with multiple antennas, aims at transmitting the geo-satellite data to the users via radio-frequency (RF) links using spatial-multiplexing. Each ground BS, on the other hand, is equipped with multiple antennas, but directly serves the users through the RF links. The paper then focuses on maximizing the network-wide throughput, subject to HAPS payload connectivity constraint, HAPS and BSs power constraints, and backhaul constraints, so as to jointly determine the user-association strategy of each user (i.e., user to geo-satellite via HAPS, or user to BS), and their associated beamforming vectors. We tackle such a mixed discrete-continuous optimization problem using an iterative approach, where the user-association is determined using a combination of integer linear programming and generalized assignment problems, and where the beamforming strategy is found using a weighted-minimum-mean-squared-error approach. The simulations illustrate the appreciable gain of our proposed algorithm, and highlight the prospects of augmenting the ground networks with beamforming-empowered HAPS for connecting the unconnected, and super-connecting the connected.

In this paper, we investigate user terminal (UT) fingerprint positioning for multi-cell massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in non-line-of-sight scenario. We first introduce a refined double beam-based channel model to the positioning problem and extract the energy coupling matrix in the refined beam domain as location-related fingerprint. By taking advantage of refined spatial and frequency beams, the new fingerprint (i.e., energy coupling matrix) contains plentiful and stationary multi-path information, such as power, angle of arrival (AoA), and delay of arrival (DoA), which are favorable to positioning. We then propose a novel deep learning-based fingerprint positioning method to locate the 2-dimension (2D) position of UTs, utilizing the multi-cell fingerprint as the input. In particular, we propose two deep neural network (DNN) architectures in this paper. The first DNN consists of convolutional neural networks (CNN) and multilayer perceptron, refining the elementary features from the multi-cell fingerprints and dividing the UTs into their belonging categories. In the second DNN, a new network architecture, Transformer, is applied to the positioning problem. The Transformer is directly based solely on self-attention mechanisms to sequences of fingerprint patches, which can perform outstandingly on the 2D position coordinates regression. Simulation results show that the proposed positioning methods can outperform the existing methods in terms of positioning error.

In the context of intelligent and networked transportation, vehicle platoon control technology is an essential measure for improving overall vehicle traffic efficiency, reducing driving energy consumption, increasing driver comfort and driving safety, and easing traffic congestion. However, in high-speed mobile states, vehicles face an uncertain communication environment, making it difficult to meet the demand for multi-vehicle cooperative control. To address the existing research deficiencies, this paper investigates a UAV-assisted multi-vehicle cooperative platoon system. The paper aims to improve the communication success probability between vehicles and UAV nodes while mitigating the velocity perturbations generated by the platoon during travel. Specifically, we design a platoon controller based on a discrete double integral model and a joint optimization model for resource scheduling of air-ground communication. This model can flexibly adjust the communication resource allocation of each platooning vehicle within each timeslot according to the communication link. Due to the complex coupling between the variables of the objective function and the constraints of the optimization problem, we design an optimization iterative solution method based on sequential quadratic programming (SQP). Finally, we verify the effectiveness of the proposed joint optimization method through simulation experiments compared to benchmark methods.

Due to its high capacity and the ability to operate in an unregulated spectrum, free space optical (FSO) communication is ideally suited for airborne backhauling. In this paper, we consider a relay-assisted airborne FSO system in which serial and parallel relaying are deployed together. We deploy relay selection in each hop and select the relay that provides the maximum instantaneous signal-to-noise ratio (SNR). To achieve this in practice, it is necessary to estimate the channel coefficient per hop and feed it back to the preceding transmitting/relaying node. Due to the semi-fixed hovering position of rotary-wing unmanned aerial vehicles (UAVs), this estimate will likely be outdated for the airborne channel. Therefore, the relay will likely be selected based on the outdated channel coefficient. In this paper, we first propose a model for outdated log-normal fading. Based on the outdated channel state information, we discuss how to optimally set the node transmit power by a proper selection of the pre-amplification factor to ensure signal reception above a targeted threshold at each hop. We finally present the end-to-end bit error rate (BER) performance of the airborne parallel multi-hop system under consideration.

The vehicle-to-vehicle (V2V) distance ranging system is a critical component of transportation automation, including the railroad industry. Although the mainline rail traffic is strictly regulated by sophisticated train control and dispatching systems, self-propelled rail vehicles, such as maintenance-of-way equipment and light rail vehicles, still rely on the safety discretion of human operators. To support both safety protection and autonomous operations for these vehicles, a fine-grained V2V ranging system is needed. This paper presents our design of a decentralized, high-resolution, short distance V2V ranging system based on ultra-wideband (UWB) impulse radio (IR) technology, implemented by commercial UWB sensors and customized algorithms. A field-testing case study has been conducted to validate the system performance on full-sized self-propelled rail vehicles. The test results quantified the concerned performance parameters of UWB technology for the chosen use case and proved both the operational concepts and the functional design in this study. Our methodology in algorithm design, field-testing, and analytical data metrics should provide implications for applying UWB to other evolving use cases of intelligent rail transportation. At the end of this paper, we evaluate the system capability using ROS simulation and conclude the lessons learned from the system design.

In the traditional physical layer encryption methods, both the delay and errors brought by the keys generation and interaction and those from the channel estimation are too high to be employed for the uplink multiuser massive multiple-input and multiple-output (MIMO) systems. Differently, this paper constructs a lightweight encryption scheme with the modulation random chaotic encryption (MRCE) signals in the uplink massive MIMO systems where the keys are not required to be known, a priori, at the base station (BS). Their generation is off-line and does not employ the channel state information, and there is no immediate interaction. Specially, as a deep learning based solution for detecting the MRCE signals in the uplink massive MIMO systems, the convolutional-neural-network aided nonlinear detection (CAD) algorithms are proposed in this paper. The simulation results showed their effectiveness. The anti-eavesdrop ability of the MRCE signals is verified even against eavesdroppers equipped with multiple antennas at high average signal to noise ratio (SNR). As shown in the simulation results, when the BS does not previously know the keys, the proposed CAD algorithms have a much better bit error rate (BER) performance and higher secrecy spectral efficiency (SSE) than the less efficient ones of their corresponding unassisted methods. The BER is closer to the theoretical lower bound of the optimum value obtained by the maximum likelihood method. They require lower average received SNR to converge to the theoretical maximum SSE given by the no error transmissions from the legitimate users to the BS. These performances also approach those of their corresponding unassisted algorithms without prior known keys. The proposed CAD algorithms have medium/strong robustness against the channel estimation error and medium/low polynomial computational complexity.

Multi-band operation in wireless networks can improve data rates by leveraging the benefits of propagation in different frequency ranges. Distinctive beam management procedures in different bands complicate band assignment because they require considering not only the channel quality but also the associated beam management overhead. Reinforcement learning (RL) is a promising approach for multi-band operation as it enables the system to learn and adjust its behavior through environmental feedback. In this paper, we formulate a sequential decision problem to jointly perform band assignment and beam management. We propose a method based on hierarchical RL (HRL) to handle the complexity of the problem by separating the policies for band selection and beam management. We evaluate the proposed HRL-based algorithm on a realistic channel generated based on ray-tracing simulators. Our results show that the proposed approach outperforms traditional RL approaches in terms of reduced beam training overhead and increased data rates under a realistic vehicular channel.

In this paper, we investigate the massive MIMO detection under the framework of mean-field variational inference (VI), which leads to a better detection trade-off between performance and complexity. First of all, by fully taking advantages of the favorable propagation characteristic of massive MIMO, the favorable-propagation-exploited variational inference (FPE-VI) algorithm is proposed for the low-complexity detection. Secondly, with respect to the system with $K$ transmitting and $N$ receiving antennas, the linear version of the FPE-VI detection is studied in detail, where its convergence is ensured when $N / K > {1}/ {\scriptstyle\left(\sqrt{2} - 1\right)^{2}}$. Thirdly, by examining the evidence lower bound (ELBO) of the proposed FPE-VI, further optimization via the application of discrete Gaussian distribution is presented for extra performance gain. Finally, all the related theoretical analysis and the improved performance-complexity trade-off of FPE-VI are demonstrated by numerical results.

With the proliferation of connected devices and the increasing demand for more precise and reliable positioning information, the fusion of 5G and GNSS localization systems holds immense potential to overcome the current limitations of standalone approaches. In order to tackle this challenge, in this paper we explore the potential of the fusion of 5G-NR and satellite localization systems to jointly enhance their accuracy and reliability. The designed solution, termed 5GNSS, is explained in detail, describing its five main building blocks and corresponding mathematical foundations. An extensive performance evaluation has been conducted, considering urban, suburban, and rural vehicular scenarios, as well as a varying number of visible satellites and 5G base stations. Our results show that 5GNSS can outperform standalone GNSS, 4G-LTE and 5G-NR positioning systems consistently across all these scenarios with an average error reduction in the $\sim$ 60-40% range. Based on these results, we conclude that future positioning systems would strongly benefit from incorporating 5GNSS-like fusion solutions.

Recently, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a novel technology that provides $360^{\circ}$ coverage and new degrees-of-freedom (DoFs). They are also capable of manipulating signal propagation and simultaneous wireless information and power transfer (SWIPT). This paper introduces a novel STAR-RIS-aided secure SWIPT system for downlink multiple input single output rate-splitting multiple access (RSMA) networks. The transmitter concurrently communicates with the information receivers (IRs) and sends energy to untrusted energy receivers (UERs). The UERs are also capable of wiretapping the IR streams. We assume that the channel state information (CSI) of the IRs is known at the information transmitter, but only imperfect CSI for the UERs is available at the energy transmitter. By exploiting RSMA, the base station splits the messages of the IRs into common and private parts. The former is encoded into a common stream that can be decoded by all IRs, while the private messages are individually decoded by their respective IRs. We find the precoders and STAR-RIS configuration that maximizes the achievable worst-case sum secrecy rate of the IRs under a total transmit power constraint, a sum energy constraint for the UERs, and subject to constraints on the transmission and reflection coefficients. The formulated problem is non-convex and has intricately coupled variables. To tackle this challenge, a suboptimal two-step iterative algorithm based on the sequential parametric convex approximation method is proposed. Simulations demonstrate that the RSMA-based algorithm implemented with a STAR-RIS enhances both the rate of confidential information transmission and the total spectral efficiency. Furthermore, our method surpasses the performance of both orthogonal multiple access (OMA) and non-OMA (NOMA).

The simultaneous optimisation of beam hopping and power allocation is a crucial technique for enhancing the performance of Multi-Beam Satellite (MBS) systems. However, the previous joint optimisation approaches cannot well handle with the issues of high-dimensional state space and discrete-continuous hybrid action space. In this paper, we propose a joint optimization approach based on parameterized reinforcement learning to simultaneously regulate beam hopping and power allocation for MBS systems (called DeepMBS). In DeepMBS, a multi-objective problem is firstly formulated to optimize system throughput and energy efficiency. Then, the optimization problem is modelled as a Markov Decision Process (MDP), and the original deep Q-network is extended with a parameterized action space to simultaneously determine the beam hopping (discrete action) and power allocation (continuous action). In addition, we design an empirical filtering mechanism to enhance the performance of DeepMBS. Finally, the results of extensive experiments demonstrate that the proposed DeepMBS can gain a better performance in terms of throughput and energy efficiency compared to the baseline algorithms. Furthermore, the proposed DeepMBS (EFM) algorithm demonstrates superior accuracy and sensitivity in capturing changes of communication demands.

With the rapid development of the Internet of Vehicles (IoV), the cognitive radio-based IoV (CIoV) network is widely used to overcome the lack of spectrum resources. In this paper, a novel joint spectrum sensing and resource allocation (JSS-RA) scheme in the CIoV system is investigated. The JSS-RA process is divided into two phases. In the spectrum sensing phase, our scheme considers the dynamic vehicular environment to adjust the decision threshold individually for each vehicle. In the resource allocation (RA) phase, we first propose a general utility-efficiency function. The various parameters contained in this function enable its transformation into many mainstream objective functions. Subsequently, the RA issue is formulated as a joint orthogonal frequency division multiplexing (OFDM) symbol and power optimization, which is also known as a mixed integer nonlinear programming (MINLP). We solve the original optimization by decomposing it into two subproblems. The theorems prove that the global optimum solution can be obtained by combining the results of two decomposed subproblems. Besides, the convergence rate of the proposed algorithm is discussed. Finally, numerical simulations verify the excellent performance and robustness of our JSS-RA scheme.

As user equipments (UEs) and infrastructure evolve, the network architecture becomes increasingly heterogeneous, presenting challenges in managing diverse communication technologies and varied network demands. Recognizing these challenges, the integration of unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication emerges as an effective strategy to enhance network capacity and address the complexities of a heterogeneous network. In this context, mobile edge computing (MEC), facilitated by servers at the network edge, significantly reduces data processing latency and alleviates backhaul congestion. This paper proposes a UAV and D2D-enabled heterogeneous MEC framework that supports two offloading modes for UEs: the first is to offload tasks to UAVs equipped with computing servers, and the second is to offload tasks to nearby idle UEs via D2D links. Unlike existing schemes, the selection of offloading modes is determined by the network load, which improves resource utilization. To further reduce computing latency, we formulate an optimization problem that jointly considers UAV trajectory planning, resource management, and task offloading policy. Since the problem is non-convex and challenging to solve in polynomial time, we decompose it into a joint resource optimization and an offload scheme optimization. Based on this, an optimization algorithm based on block descent and potential game is proposed to obtain the optimal solution. Simulation results indicate that compared to typical UAV-based MEC without D2D, our solution can reduce latency by about 20%. In comparison to equivalent resource allocation, the proposed algorithm can reduce latency by about 25%.

Quality of Service (QoS) plays a pivotal role in numerous delay-sensitive applications that range from general to specific such as the Internet of Medical Things (IoMT), Industrial Internet of Things (IIoT), Unnamed Aerial Vehicles (UAVs), Industrial Automation, and Cooperative Internet of Vehicles (C-IoV), etc. Every application has numerous contributions to human daily life activities, but here in this work, we focused on the C-IoV in the context of QoS metrics. Even though the literature suggested several techniques to address the QoS issues in this emerging technology, but we have not come across a single article that addresses this issue in a cooperative environment, considering the impact of communication congestion and contention by taking into account emergency vehicles and traditional vehicles. Given that, in this paper, we introduce a hybrid framework known as the Traffic Congestion and Priority-Aware Algorithm (TCPAA). This innovative paradigm leverages the capabilities of computer vision, Deep Neural Networks (DNN) and Dijkstra algorithm to strategically incorporate the transmission channels and network entities with an objective to improve the QoS metrics in emergency vehicles. Initially, we developed a dataset with computer vision algoritms “real-time (OpenCV ”Background Subtraction”) to evaluate and chose the best machine learning algorithms among random forest, support vector machine (SVM), k-means clustering, and DNN. Based on the result statistics, we select DNN, and classified vehicles into two classes: Emergency and traditional vehicles to train the model. Subsequently, we set standard for two type of communications such as regular and prioritized traffic. We incorporate a micro base station (µBS) in the network for prioritized traffic to facilitate congestion-free communication of emergency vehicles, while the Dijkstra algorithm is used to managed the communication of traditional vehicles. Considering the nature of operation of future autonomous vehicles, we managed most of the decisions processes at the client-side by categorizing the traffic based on the vehicle requirements. Through reliable client-side management, the high performance and accuracy of TC-PAA underscore its efficiency compared to established field-proven schemes. Adhering to reliability metrics such as latency, packet loss ratio, communication cost, data availability, and traffic priority, the proposed model improves QoS metrics in high-demanding areas of IoV networks.

Unmanned aerial vehicle (UAV) systems have gained widespread recognition due to their versatility and autonomy. Their deployment for disaster mitigation and management operations is seen as one of their most important applications over the past decade. In such UAV networks, routing plays a crucial role in determining network performance parameters such as network lifetime, data transmission latency, and packet delivery ratio. This paper presents a novel routing mechanism - Multi-Objective Markov Decision Based Routing (MOBMDP) for UAV networks carrying out search-based operations. MOBMDP models routing decisions in a Markov Decision Process (MDP) framework and uses Q-learning to take decisions. It compares routing paths using three metrics, viz., Remaining Energy of the Minimum Energy Node (REMEN), Power Distance ratio (PD), and Expected Delay (ED). Amongst these metrics, PD is a novel metric proposed by this work. PD simultaneously optimizes the energy efficiency and energy distribution in the network. Further, MOBMDP uses a novel reinforcement learning inspired method to estimate transmission delay in a given path. Intensive simulation studies compare MOBMDP to four state-of-the-art routing protocols. Results show a significant improvement in network lifetime, packet delivery ratio, energy efficiency, average data transmission delay, and error in delay estimation.

Industrial environments constitute a challenge in terms of radio propagation due to the presence of machinery and the mobility of the different agents, especially at mmWave bands. This paper presents an experimental evaluation of a FR2 5G network deployed in an operational factory scenario at 26 GHz. The experimental characterization, performed with autonomous mobile robots that self-navigate the industrial lab, leads to the analysis of the received power along the factory and the evaluation of reference path gain models. The proposed assessment deeply analyzes the physical layer of the communication network under operational conditions. Thus, two different network configurations are assessed by measuring the power received in the entire factory, providing a comparison between deployments. Additionally, beam management procedures, such as beam recovery, beam sweeping or beam switching, are analyzed since they are crucial in environments where mobile agents are involved. They aim for a zero interruption approach based on reliable communications. The results analysis shows that beam recovery procedures can perform a beam switching to an alternative serving beam with power losses of less than 1.6 dB on average. Beam sweeping analysis demonstrates the prevalence of the direct component in Line-of-Sight conditions despite the strong scattering component and large-scale fading in the environment.

Grant-free random access (GFRA) is now a popular protocol for large-scale wireless multiple access systems in order to reduce control signaling. Resource allocation in GFRA can be viewed as a form of frame slotted ALOHA, where a ubiquitous design assumption is device homogeneity. In particular, the probability that a device seeks to transmit data is common to all devices. Recently, there has been an interest in designing frame slotted ALOHA algorithms for networks with heterogeneous activity probabilities. These works have established that the throughput can be significantly improved over the standard uniform allocation. However, the algorithms for optimizing the probability a device accesses each slot require perfect knowledge of the active devices within each frame. In practice, this assumption is limiting as device identification algorithms in GFRA rarely provide activity estimates with zero errors. In this paper, we develop a new algorithm based on stochastic gradient descent for optimizing slot allocation probabilities in the presence of activity estimation errors. Our algorithm exploits importance weighted bias mitigation for stochastic gradient estimates, which is shown to provably converge to a stationary point of the throughput optimization problem. In moderate size systems, our simulations show that the performance of our algorithm depends on the type of error distribution. We study symmetric bit flipping, asymmetric bit flipping and errors resulting from a generalized approximate message passing (GAMP) algorithm. In these scenarios, we observe gains up to 40%, 66%, and 19%, respectively.

Orthogonal time frequency space (OTFS) modulation exhibits excellent performance in time-varying channels with severe Doppler shifts. Diversity analysis is an effective method in predicting error performance of an OTFS system. Recent works have studied OTFS diversity in a time-varying channel assuming constant delay-Doppler (DD) channel realization per frame. However, in practice, DD channel realizations may vary in a frame due to the rapid changes in channel geometry relative to a frame duration, leading to a block time-varying (BTV) channel. This paper analyzes the OTFS diversity in such BTV channels. Specifically, we derive the pairwise error probability of the system and analyze its diversity by finding the minimum rank of the pairwise error matrix and its associated multiplicity. We also compare it with other known schemes and demonstrate its improved diversity. Finally, simulations are performed to validate the analytical results.

The proliferation of electric vehicles (EVs) provides a promising resource for regulation services to maintain the frequency of power systems. However, two major problems hinder its implementation, including the inaccuracy of the EV charging model and the overlook of varieties in EV ownership. To facilitate real-world implementation, this paper tailors a market-based mechanism to incentivize EVs to provide frequency regulation services. Firstly, a detailed EV charging model considering the battery equivalent circuit and AC-DC conversion loss is proposed. The nonconvexities are approximated and convexified with accuracy guarantee. Then, based on the self-interest behavior of EVs, a non-cooperative game is adopted to model the competition in providing frequency regulation service. The aggregative game is used to approximate the original non-cooperative game. Thereby, the existence, uniqueness, and approximation accuracy of the aggregative equilibrium is theoretically proved. Moreover, the rolling look-ahead iterative frequency regulation service clearing mechanism for EV parking lot is designed based on the decentralized equilibrium searching algorithm of the aggregative game. Finally, the effectiveness, computational efficiency, and approximation accuracy of the proposed market-based mechanism are validated by a series of numerical tests with EV populations at 50, 100, 200, 300, 600, and 900.

Tracking mobile targets is a basic task for many applications, e.g., wildlife protection, area surveillance, battlefield reconnaissance, disaster rescue, etc. In these cases, multiple edge devices are usually deployed to collect images or videos from the target area. Two mainstream tracking methodologies have been proposed to recognize and track targets from such data sources cooperatively. The centralized strategies collect data from the edge devices and thereafter run mining and learning algorithms upon such data. On the contrary, the distributed strategies implement such algorithms in a distributed manner. However, these methods incur either high transmission costs or slow convergence speeds. To this end, this paper presents a novel cooperative tracking framework (i.e., FedTrack) based on adaptive federated learning. A dual reputation mechanism has been formulated, and subsequently, an adaptive node selection algorithm has been suggested to ascertain the nodes suitable for involvement in the training process. Furthermore, a strategy for selecting the aggregation node based on capability has been developed to enhance the efficiency of aggregation. As far as we know, FedTrack is the first federated learning framework for collaborative tracking. Experimental results demonstrate that FedTrack achieves comparable or even better accuracy than state-of-the-art methods, yet needs much fewer data transmission costs and much less time consumption.

Heterogeneous cellular network (HCN) is structured on a multi-tier architecture comprising of macro-base stations (MBSs) and small-base stations (SBSs). MBS and SBS are deployed to accommodate a large number of users for the purpose of achieving higher data rates, coverage, and improved outage performance. Recognized for its spectrum efficiency, non-orthogonal multiple access (NOMA) emerges as a viable multiple-access technique for sixth-generation (6G) wireless networks. To maximize power efficiency and reduce outage probability, the power amplifier at the base station is operated close to saturation, which results in nonlinear distortions. Thus, it is vital to understand the fundamental performance guarantees of practical NOMA-enabled HCN systems (HCN-NOMA) under nonlinear distortions introduced by high power amplifiers (HPA). In this paper, we employ an analytical performance evaluation of HCN-NOMA using stochastic geometric approach consisting of a multi-tier architecture with nonlinear distortions produced by HPA. We compare and highlight the impact of HPA operating in the linear and nonlinear regions on orthogonal multiple access (OMA) and NOMA in multi-tier HCN. The performance is assessed and compared in terms of outage probability, system throughput, and energy efficiency. It is observed that the performance is improved by up to 96.6% in HCN employing NOMA versus OMA while considering the nonlinear region of operation of HPA.

In intelligent omni-surface (IOS) aided cell-free networks, the coupled characteristic of IOS phase shifts exactly limits the potential benefits of full-dimensional service and design flexibility, which brings more formidable challenges and constraints of cooperative beamforming design for multiple base stations (BSs) and IOSs. This paper proposes an efficient cooperative beamforming design with coupled transmission and reflection phase shifts to exploit the capacity improvement potential of multiple IOSs for the cell-free system under the practical coupled constraints, aiming at maximizing the weighted sum-rate by jointly optimizing the transmit beamforming and the coefficients of IOSs. Specifically, in the case of the coupled constraints of IOS, the penalty dual decomposition (PDD) framework is employed to move the constraints for a tractable problem form, deriving close-form solutions to optimize the amplitude and the phase-shift coefficients alternatively. Additionally, due to the non-convexity and high complexity of the formulated maximization problem, we reformulate it into an equivalent one to facilitate decoupling variables, and then the subproblems are solved by adopting the block coordinate descent (BCD) method in the proposed cooperative design where the optimal BS transmit beamformers are obtained locally. Simulation results demonstrate the superiorities of the IOSs-aided cell-free system with the proposed effective design, as well as the impact of the coupled phase-shift constraints on the system performance.

In this paper, we study the training design for multiple-input multiple-output (MIMO) systems. Unlike the majority of existing works, we consider the most practical and general, yet challenging, scenario where the channel and noise covariance matrices are Kronecker-inseparable and general power constraints are imposed on the training signal. To effectively solve this challenging problem, we devise a novel and efficient iterative algorithm, in which the channel estimator and training signal are jointly optimized in an alternating manner by minimizing mean squared error (MSE) of the channel estimation. Simulation results demonstrate that the proposed scheme performs markedly better and more effective than the existing schemes.

With the development of vehicle applications such as intelligent transportation and autonomous driving, the application fields based on location services have increasingly higher requirements for vehicle positioning reliability and real-time accuracy. However, the existing single navigation source of vehicles makes it difficult to realize real-time and high-precision positioning in different scenarios. The current multi-source information fusion methods have the problems of low generalization ability, poor expansibility, and high computational complexity, so it is challenging to apply in the field of vehicle positioning. To solve the above problems, this paper proposes a vehicle heterogeneous multi-source information fusion positioning method (MIFP) based on information probability, which converts the multiple heterogeneous navigation sources into information probability models to realize the unification of the time-frequency parameter format and designs an information fusion algorithm to realize the rapid fusion based on the theory of relative entropy. Through simulation tests and experimental verification by comparing with mainstream information fusion methods, such as the UKF method, the FGA method, and the NNA method, the MIFP method has high positioning accuracy and strong real-time performance. It can effectively solve the problems of weak expansion ability and large calculation amounts of current vehicle fusion positioning models. In the case of interference or mutation, the MIFP method can also suppress the influence of sudden errors on vehicle positioning.

Vehicular Edge Computing (VEC) has emerged as a promising paradigm to enable low-latency Vehicle-to-Everything (V2X) services by bringing computing resources closer to vehicles. However, the high dynamicity of vehicular networks poses significant challenges in designing an optimal policy for delivering V2X services while ensuring security and timely service delivery. To address these challenges, this paper proposes a Blockchain-Enabled Vehicular Edge Computing (BEVEC) framework that employs a dual-layer verification process empowered with a permissioned blockchain to ensure data accuracy and integrity. A novel system utility function is designed to measure the performance of the BEVEC, which also serves as the basis for a consensus mechanism of the permissioned blockchain. To optimize this utility, a Deep Reinforcement Learning (DRL) algorithm is proposed to enable timely service delivery in BEVEC. Simulation-based results demonstrate the effectiveness of the proposed algorithm when compared to existing approaches. On average, it obtained an 18% reduction in latency, a 38% improvement in successful service delivery, and a 65% decrease in energy consumption.

Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms can improve data collection performance. This paper puts forth a new mean field flight resource allocation optimization to minimize age of information (AoI) of sensory data, where balancing the trade-off between the UAVs' movements and AoI is formulated as a mean field game (MFG). The MFG optimization yields an expansive solution space encompassing continuous state and action, resulting in significant computational complexity. To address practical situations, we propose, a new mean field hybrid proximal policy optimization (MF-HPPO) scheme to minimize the average AoI by optimizing the UAV's trajectories and data collection scheduling of the ground sensors given mixed continuous and discrete actions. Furthermore, a long short term memory (LSTM) is leveraged in MF-HPPO to predict the time-varying network state and stabilize the training. Numerical results demonstrate that the proposed MF-HPPO reduces the average AoI by up to 45% and 57% in the considered simulation setting, as compared to multi-agent deep Q-learning (MADQN) method and non-learning random algorithm, respectively.

In this paper, we focus on offloading of computationally demanding tasks with tight processing delay requirements (hundreds of ms) from user equipments (UEs) to multi-access edge computing servers while exploiting unmanned aerial vehicles (UAVs) as relays. Traditionally addressed problem jointly optimizing an offloading decision and an association of the UEs to the UAVs is, in our work, enriched by optimization of the relaying via UAVs through selection between half-duplex (HD) and full-duplex (FD) under a constraint on the maximum processing delay of individual tasks. To solve this complex problem, we also optimize the length of relaying time slots in HD and the transmission power allocation for the UEs and the UAVs in FD. Then, we transform the problem to one-to-many matching and propose a low-complexity greedy algorithm for the joint offloading decision and UEs association solved together with the duplex selection. We show that our proposal reduces the sum energy consumed by the UEs and UAVs for the tasks offloading by up to 40% compared to related works.

In this paper, we consider an reconfigurable intelligent surface (RIS)-assisted multiple-input–single-output (MISO) covert symbiotic radio (SR) communication system. RIS, as a secondary transmitter (STx), can enhance primary transmission from the primary transmitter (PTx) to the primary receiver (PRx). Simultaneously, STx transmits its own information to the secondary receiver (SRx). In addition, RIS-assisted covert communications have a broad development prospect, and covert communication is considered in which Willie eavesdrops passive signals from RIS (Alice) to SRx (Bob). By jointly optimizing active beamforming vector at PTx and passive beamforming matrix at RIS, the achievable rate of PRx is maximized subject to the covertness constraint and the signal-to-noise ratio (SNR) constraint for secondary transmission. The optimization problem is challenging because of the non-convex objective function and the coupling between variables. Thus, the deep unfolding algorithm based on gradient descent (DUAGD) is proposed for the beamforming design. Specifically, we first transform the optimization problem with constraints into the dual domain. Then inspired by gradient descent algorithm, deep unfolding unfolds the original iterative process into a multi-layer network structure. Results from simulations show that the proposed algorithm has fast convergence while maintaining performance.