The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes.

This paper focuses on reducing the effect of systematic uncertainties on the near-field or far-field source direction-finding accuracy by introducing a calibration emitter, which suffers the same uncertainties as the actual source. We propose a modified polar representation (MPR)-based closed-form algebraic algorithm, i.e., the improved successive unconstrained minimization (SUM), to eliminate the common errors of time difference of arrival (TDOA) measurements, thereby refining the source direction-finding accuracy. The simulations show that the proposed algorithm can approach the Cramer–Rao Lower Bound´ (CRLB); and demonstrate the positive effect of the calibration emitter on the direction-finding performance under the condition of varying measurement error, source range, and sensor position error.

This paper focuses on improving the sensor selection performance in the time difference of arrival (TDOA)-based localization scenario with the presence of source estimation error. In existing schemes, a coarse source position is first estimated and regarded as the actual counterpart for selecting optimal sensors. However, if the estimated position deviates from the actual position, the localization accuracy determined by the selected sensor subset will severely degrade. To solve the issue, we devise a distance-related weighted average Cramér Rao lower bound (WA-CRLB) to include the spatial information of the actual position by scattering sampling points around the estimated source position according to its distribution. Then, we formulate a Boolean vector-based sensor selection optimization problem to minimize WA-CRLB and propose a modified iterative swapping greedy (MISG) algorithm. Simulation results show that the proposed MISG algorithm achieves higher localization robustness with the increase of TDOA measurement error strength, and has lower computational complexity compared with the previous semi-definite relaxation (SDR)-based algorithms.

In this paper, we propose a covert wireless uplink transmission strategy in space-air-ground integrated vehicular networks, where the source vehicle transmits its own message over the channel that being used by the host communication system, to avoid being detected by the warden. It is obvious that the data transmission efficiency of the covert communication system is limited due to the co-channel interference. To improve the data transmission efficiency, we consider that the covert communication system adopts improper Gaussian signaling (IGS). We formulate a joint transmit power and IGS factor optimization problem to minimize the outage probability of the covert communication system. The minimum error detection probability of the warden is first analyzed with noise uncertainty, which is used to measure the system covertness. Under the constraints of the quality of service (QoS) of host communication system and the covertness requirement, the optimal transmit power is first derived with proper Gaussian signaling (PGS) scheme. Then, with the approximate outage probability derived under IGS scheme, the optimization problem is solved by jointly designing the transmit power and IGS factor. Finally, we provide extensive numerical results to validate the proposed covert transmission strategy, and demonstrate that the IGS scheme is beneficial in improving the data transmission efficiency in terms of outage probability compared to PGS scheme.

In this paper, we propose an improper Gaussian signaling (IGS) empowered covert communication strategy in unmanned air vehicle (UAV) assisted communication systems. The ground user (Alice) covertly transmits confidential messages to the UAV amounted based station (Bob) by superimposing over an overt channel, which is licensed to an existing communication system. To alleviate the inner-system interference caused by the superimposed waveforms, we design the IGS as the waveform of the covert communication system and the proper Gaussian signaling (PGS) as the waveform of the existing communication system. We derive closed-form expressions for the outage prob-ability of the overt and covert transmission links, respectively. Moreover, we formulate a joint transmit power and IGS factor optimization problem to maximize the outage performance of the covert communication system constraining the covertness requirement and quality of service of the existing communication system. The optimal solution is derived by leveraging the mono-tonic properties of objective function and constraints. Finally, simulation results are provided to verify the effectiveness of the proposed covert communication strategy.

Covert communication achieved by using the full-duplex (FD) receiver has wide applications. Specifically, the FD receiver can emit artificial noise to prevent the signal of transmitter from detecting by the illegitimate warden. In this paper, we investigate the robust joint power and position optimization (JPPO) for the full-duplex receiver (i.e., Bob) in the presence of the uncertainties of the warden (i.e., Willie). We first analyze the effect of the warden's position uncertainty on the performance of covert communication. Then, by using robust optimization technique, we maximize the effective covert throughput of transceivers by optimizing the power and position of the Bob constraining the sufficient covertness requirement. Theoretical analysis indicate that the effective covert throughput between Alice and Bob is inversely proportional to the distance between them within the deployable zone (DZ) satisfying the covert condition, and the optimal position of Bob is on the boundary of the DZ near Alice. Finally, simulation results verified our conjecture.

As a promising architecture to provide ubiquitous and ultra-reliable network connectivity, space-air-ground integrated network (SAGIN) has attracted substantial research interests. In this paper, we propose a dynamic spectrum access control (DSAC) protocol for the SAGIN. Specifically, the proposed DSAC protocol uses sequences to represent the spectrum access decisions for authorized users at different time slots. Through iterative and orthogonal sequence transformation, the DSAC protocol can generate orthogonalized sequences to guarantee successful spectrum access for authorized users. In addition, the non-collision probability of the data packets accessing the shared spectrum under the guidance of DSAC protocol is analyzed, based on which a closed-form expression of the system throughput is further derived. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed protocol is effective in access control and throughput improvement in the SAGIN when compared with the existing random access protocol.

Accurate location estimation of Internet-of-Things (IoT) devices within an Area of Interest (AoI) is a challenging issue, especially in a global navigation satellite system (GNSS)-constrained environment. In this article, we present a space-air integrated localization network (SAILN) architecture to exploit the advantages of the unmanned-aerial-vehicle (UAV)-based localization through joint position and power optimization (JPPO) strategies. In SAILN, UAVs can utilize their flexible movement to obtain the line-of-sight (LOS) path with a high probability, thereby providing the potential IoT devices in the AoI with supplementary localization information. The JPPO of UAVs aims to improve the regional localization accuracy for the entire AoI, considering the no-fly-zone (NFZ) and the total energy constraint. We propose the average localization accuracy increment (ALAI) of the sampling points in the AoI as the metric to measure the performance of SAILN compared with that of only satellites, which is regarded as the objective to formulate the JPPO problems for UAV operations in both static and dynamic SAILN. The intractable problems can be resolved by the pure genetic algorithm (PGA) that has a low computational cost and unique features suiting the JPPO of UAVs. Then, by taking advantage of the ALAI convexity to the UAVs' power, we propose a power reallocation-based two-step algorithm (PRTSA) to further explore an improved JPPO solution. Simulation results validate that the proposed PRTSA can obtain a higher localization accuracy for the entire AoI than the PGA and the other straightforward baselines.

In this paper, we propose a multiobjective optimization framework to jointly optimize power and position of full-duplex (FD) receiver in the covert communication. By introducing a legitimate FD receiver (Bob) with random transmit power, the signal of a legitimate transmitter (Alice) can be transmitted covertly since an eavesdropper (Willie) is confronted with interference uncertainty and makes an incorrect decision for signal detection. Therefore, we optimize the position and the transmit power range of Bob in order to maximize the achievable transmission rate from Alice to Bob and average covert probability at Willie simultaneously. Due to the presence of multiple optimization objectives, the nondominated sorting genetic algorithm II (NSGA-II) is utilized to explore the Pareto front and to give a set of solutions that reveal different tradeoffs between the two conflicting objectives. Simulation results are provided to reveal the Pareto front and to illustrate the effect of transmit power of Alice and Bob on the Pareto front.

In this paper, we propose covert localization to improve the security of wireless localization networks, which can prevent the legitimate transmission of localization signals between anchors and agent from being detected by the illegitimate warden. Specifically, we first establish a framework of covert localization and demonstrate its feasibility when the warden suffers noise uncertainty. Then, with two specific noise uncertainty distributions, we derive the fundamental limit of localization accuracy, i.e., covert squared position error bound (CSPEB), which is the achievable localization accuracy for the agent while ensuring covertness for the warden. Theoretical analysis of CSPEB demonstrates the impact of different factors on the localization accuracy. Besides, in an energy-constrained scenario, we formulate a power allocation problem to refine anchors' power to minimize the CSPEB for a given total power budget and develop an algorithm based on the semidefinite program (SDP). Simulation results verify our theoretical analysis by evaluating the effect of several representative factors on the CSPEB and show the superiority of the SDP-based power allocation algorithm to the other baseline methods.

Unmanned aerial vehicles (UAV) deployment is of pivotal importance in the promising space-air integrated localization network (SAILN), which is a typical partially controllable network and supports 3- dimensional (3D) localization. To improve the localization accuracy for specific area or user, several UAVs need to be deployed. This paper proposes an iterative UAV deployment strategy for SAILN, which can minimize the localization error by determining accurate 3D coordinate information (elevation and azimuth angles, distance) for all the supplementary UAVs. Specifically, based on the analysis of accuracy increment when a new UAV is added into SAILN, the genetic algorithm (GA) is leveraged to find its optimal geometric position in a constrained area that can maximize the accuracy increment. Then, UAVs are iteratively added until the desired number, i.e., the quantity budget of deployed UAVs, is achieved. Simulation results demonstrate that the proposed UAV deployment strategy provides considerably better localization accuracy compared with uniform angular arrays (UAA) and random deployment (RD).

This paper investigates the sensor selection problem for time difference of arrival (TDOA) localization in wireless sensor networks. Specifically, a multi-objective optimization problem is formulated in which a Boolean vector is involved to find the best tradeoff between the localization accuracy and the energy consumption. The ε- constraints method is introduced to convert the original multi- objective optimization problem to a tractable single-objective problem. To solve the converted sensor selection problem, we propose the polyblock outer approximation (POA) algorithm based on discrete monotonic optimization (DMO) in order to find the global optimal solution, which however can not be obtained by the traditional semidefinite relaxation (SDR) approach. Further, for the sake of practical implementation, we propose another two suboptimal algorithms, namely, POA-based accelerated cutting (POA-AC) algorithm and POA-based monotonic cutting (POA-MC) algorithm. Simulation results validate that the localization accuracy for sensors selected by the POA-AC algorithm and POA-MC algorithm is greater than the semidefinite relaxation (SDR) solution and achieves the same results as that by the exhaustive search method.

This paper investigates the resource allocation in millimeter wave ultra dense networks, in which the beam assignment and sub-band allocation are jointly considered. Motivating to maximize the sum rate of the network conceived, the optimization problem is formulated as a mixed integer non-linear programing (MINLP) problem, which involves allocating the novel three-dimensional resource blocks (RBs) defined in beam (B), time (T), and frequency (F) dimension, respectively. To tackle the formulated MINLP problem, we propose the low-complexity resource allocation scheme, including the so-called best option first (BOF) beam assignment algorithm, and the many-to-one matching with externalities (M2O-ME) sub-band allocation algorithm. In particular, the BOF beam assignment algorithm is first carried out to coordinate the RBs in terms of T- and B-dimension. Then, with the aid of the mechanism of many-to-one with externalities, the M2O-ME sub-band algorithm is implemented to find the optimal sub-band allocation (i.e. RB allocation in F-dimension) solution. Finally, our simulation results show that the proposed resource allocation scheme can significantly outperform the existing schemes in terms of sum rate of the networks. Therefore, we can conclude that the proposed resource allocation scheme can be considered as a promising candidate for practical ultra dense small-cell networks with mmWave capability.

We focus on the 3-dimensional (3D) source localization passively by using TDOA and AOA in the presence of sensor errors. Determining the position from the TDOA and AOA measurements is not an easy task because the relationship between them is nonlinear. We present a new WLS solution that the TDOA equation is simpler than relevant literature (Yin and Wan, A Simple and Accurate TDOA-AOA Localization Method Using Two Stations). However, the bias of the WLS solution is larger. Hence, we propose a bias reduced method by imposing a quadratic constraint so that the expectation of cost function can attain the minimum value at the true position. The simulation illustrates the method is effective and the performance of this method can achieve the Cramer-Rao Lower Bound (CRLB) when the noises are Gaussian and in small region.