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In this paper, we design constant modulus probing waveforms with good correlation properties for large-scale collocated multi-input multi-output (MIMO) radar systems. The main content is as follows: First, we formulate the design problem as a fourth-order polynomial minimization problem with unimodulus constraints. Then, by analyzing the geometric properties of the unimodulus constraints through Riemannian geometry theory and embedding them into the search space, we transform the original non-convex optimization problem into an unconstrained problem on a Riemannian manifold for solution. Second, we convert the objective function into the form of a large but finite number of loss functions and employ a customized R-SVRG algorithm to solve it. Third, we prove that the customized R-SVRG algorithm is theoretically guaranteed to converge if appropriate parameters are chosen. Numerical examples demonstrate the effectiveness of the proposed R-SVRG algorithm.

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden's detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution, and Alice's transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice's perspective, the jammer's perspective, and the global perspective. Our analysis reveals that the minimum jamming power should not always be zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. In addition, we prove that the minimum jamming power should be the same as Alice's covert transmit power, depending on the covertness and average jamming power constraints. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert throughput under the same covertness and average jamming power constraints.

In this paper, an intelligent reflecting surface (IRS)-and-unmanned aerial vehicle (UAV)-assisted two-way amplify-and-forward (AF) relay network in maritime Internet of Things (IoT) is proposed, where ship1 ($\text{S}_1$) and ship2 ($\text{S}_2$) can be viewed as data collecting centers. To enhance the message exchange rate between $\text{S}_1$ and $\text{S}_2$, a problem of maximizing minimum rate is cast, where the variables, namely AF relay beamforming matrix and IRS phase shifts of two time slots, need to be optimized. To achieve a maximum rate, a low-complexity alternately iterative (AI) scheme based on zero forcing and successive convex approximation (LC-ZF-SCA) algorithm is presented. To obtain a significant rate enhancement, a high-performance AI method based on one step, semidefinite programming and penalty SCA (ONS-SDP-PSCA) is proposed. Simulation results show that by the proposed LC-ZF-SCA and ONS-SDP-PSCA methods, the rate of the IRS-and-UAV-assisted AF relay network surpass those of with random phase and only AF relay networks. Moreover, ONS-SDP-PSCA perform better than LC-ZF-SCA in aspect of rate.

Different with conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit the signals to the receiver. In this paper, to serve more ground users and increase the deployment flexibility, we investigate an unmanned aerial vehicle equipped with a STAR-RIS (STAR-RIS-UAV) aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV aided coordinated multipoint cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficients matrices are optimized. Specifically, instead of adopting the alternating optimization, we design an iteration method to optimize all variables for both ES and MS protocols at the same time. Simulation results reveal that STAR-RIS-UAV aided wireless communication system has a much higher sum rate than the system with conventional RIS or without RIS. Furthermore, the proposed structure is more flexible than a fixed STAR-RIS and could greatly promote the sum rate., Comment: 10 pages, 6 figures

Recently, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been introduced into wireless communication systems to enhance the performance of air-ground transmission. To make a good balance between performance, cost, and power consumption, a hybrid-IRS-and-UAV-assisted directional modulation (DM) network is investigated in this paper, where the hybrid IRS consists of passive and active reflecting elements. To maximize the achievable rate, three optimization algorithms, called maximum signal-to-noise ratio (SNR)-fractional programming (FP) (Max-SNR-FP), maximum SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR), and maximum SNR-majorization-minimization (MM) (Max-SNR-MM), are proposed to jointly design the beamforming vector and phase shift matrix (PSM) of hybrid IRS by alternately optimizing one and giving another. The Max-SNR-FP method employs the successive convex approximation and FP methods to derive the beamforming vector and hybrid IRS PSM. The Max-SNR-EAR method adopts the maximum signal-to-leakage-noise ratio method and the criteria of phase alignment and EAR to design them. In addition, the Max-SNR-MM method utilizes the MM criterion to derive the IRS PSM. Simulation results show that the rates harvested by the proposed three methods are slightly lower than those of active IRS with higher power consumption, which are 35 percent higher than those of no IRS and random phase IRS, while passive IRS achieves only about 17 percent rate gain over the latter. Moreover, compared to Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods make an obvious complexity degradation at the price of a slight performance loss., Comment: arXiv admin note: text overlap with arXiv:2301.02831

Angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), it has higher accuracy and is not sensitive to time synchronization of the distributed sensors. However, there are few works focused on three-dimensional (3-D) scenario. Furthermore, although maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra high. It is hard to employ it in practical applications. This paper proposed two multiplane geometric center based methods for 3-D AOA in UAV positioning. The first method could estimate the source position and angle measurement noise at the same time by seeking a center of the inscribed sphere, called CIS. Firstly, every sensor could measure two angles, azimuth angle and elevation angle. Based on that, two planes are constructed. Then, the estimated values of source position and angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramer-Rao lower bound (CRLB) and have lower complexity than MLE., Comment: 7 pages, 5 figures

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity.

To provide important prior knowledge for the DOA estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via massive MIMO receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, square root of maximum eigenvalue times minimum eigenvalue (SR-MME) and geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of sample covariance matrix to construct feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM), and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70\% with massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and Minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

Due to the power consumption and high circuit cost in antenna arrays, the practical application of massive multiple-input multiple-output (MIMO) in the sixth generation (6G) and future wireless networks is still challenging. Employing low-resolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choice with acceptable performance loss.In this paper, the combination of the mixed-ADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in 6G. By adopting the additive quantization noise model, the exact closed-form expression of the Cram\'{e}r-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efficiency is also investigated in our paper. The numerical results reveal that the HAD structure with mixed-ADCs can significantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2-4 bits of resolution may be a good choice in practical massive MIMO systems., Comment: 12 pages, 7 figures

In this paper, we investigate the optimal design of a wireless-powered covert communication (WP-CC) system, in which a probabilistic accumulate-then-transmit (ATT) protocol is proposed to maximize the communication covertness subject to a quality-of-service (QoS) requirement on communication. Specifically, in the considered WP-CC system, a full-duplex (FD) receiver transmits artificial noise (AN) to simultaneously charge an energy-constrained transmitter and to confuse a warden's detection on the transmitter's communication activity. With the probabilistic ATT protocol, the transmitter sends its information with a prior probability, i.e., $p$, conditioned on the available energy being sufficient. Our analysis shows that the probabilistic ATT protocol can achieve higher covertness than the traditional ATT protocol with $p=1$. In order to facilitate the optimal design of the WP-CC system, we also derive the warden's minimum detection error probability and characterize the effective covert rate from the transmitter to the receiver to quantify the communication covertness and quality, respectively. The derived analytical results facilitate the joint optimization of the probability $p$ and the information transmit power. We further present the optimal design of a cable-powered covert communication (CP-CC) system as a benchmark for comparison. Our simulation shows that the proposed probabilistic ATT protocol (with a varying $p$) can achieve the covertness upper bound determined by the CP-CC system, while the traditional ATT protocol (with $p=1$) cannot, which again confirms the benefits brought by the proposed probabilistic ATT in covert communications., Comment: 12 pages, 6 figures

Intelligent reflection surface (IRS) is emerging as a promising technique for future wireless communications. Considering its excellent capability in customizing the channel conditions via energy-focusing and energy-nulling, it is an ideal technique for enhancing wireless communication security and privacy, through the theories of physical layer security and covert communications, respectively. In this article, we first present some results on applying IRS to improve the average secrecy rate in wiretap channels, to enable perfect communication covertness, and to deliberately create extra randomness in wireless propagations for hiding active wireless transmissions. Then, we identify multiple challenges for future research to fully unlock the benefits offered by IRS in the context of physical layer security and covert communications. With the aid of extensive numerical studies, we demonstrate the necessity of designing the amplitudes of the IRS elements in wireless communications with the consideration of security and privacy, where the optimal values are not always $1$ as commonly adopted in the literature. Furthermore, we reveal the tradeoff between the achievable secrecy performance and the estimation accuracy of the IRS's channel state information (CSI) at both the legitimate and malicious users, which presents the fundamental resource allocation challenge in the context of IRS-aided physical layer security. Finally, a passive channel estimation methodology exploiting deep neural networks and scene images is discussed as a potential solution to enabling CSI availability without utilizing resource-hungry pilots. This methodology serves as a visible pathway to significantly improving the covert communication rate in IRS-aided wireless networks., Comment: 16 pages, 6 figures

This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the UAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. In the considered system, the UAV's trajectory is critical to the covert communication performance, since the AN transmitted by the UAV also generates interference to Bob. To maximize the system performance, we formulate an optimization problem to jointly design the UAV's trajectory and Alice's transmit power. The formulated optimization problem is non-convex and is normally solved by a conventional iterative (CI) method, which requires multiple approximations based on Taylor expansions and an initialization on the UAV's trajectory. In order to eliminate these requirements, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. By analyzing the covertness constraint, the GM method decouples the joint optimization into optimizing the UAV's trajectory and Alice's transmit power separately. Our examination shows that the GM method can significantly outperform the CI method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the CI method.

This work examines the performance gain achieved by deploying an intelligent reflecting surface (IRS) in covert communications. To this end, we formulate the joint design of the transmit power and the IRS reflection coefficients by taking into account the communication covertness for the cases with global channel state information (CSI) and without a warden's instantaneous CSI. For the case of global CSI, we first prove that perfect covertness is achievable with the aid of the IRS even for a single-antenna transmitter, which is impossible without an IRS. Then, we develop a penalty successive convex approximation (PSCA) algorithm to tackle the design problem. Considering the high complexity of the PSCA algorithm, we further propose a low-complexity two-stage algorithm, where analytical expressions for the transmit power and the IRS's reflection coefficients are derived. For the case without the warden's instantaneous CSI, we first derive the covertness constraint analytically facilitating the optimal phase shift design. Then, we consider three hardware-related constraints on the IRS's reflection amplitudes and determine their optimal designs together with the optimal transmit power. Our examination shows that significant performance gain can be achieved by deploying an IRS into covert communications.

This paper jointly optimizes the flying location and wireless communication transmit power for an unmanned aerial vehicle (UAV) conducting covert operations. This is motivated by application scenarios such as military ground surveillance from airborne platforms, where it is vital for a UAV's signal transmission to be undetectable by those within the surveillance region. Specifically, we maximize the communication quality to a legitimate ground receiver outside the surveillance region, subject to: a covertness constraint, a maximum transmit power constraint, and a physical location constraint determined by the required surveillance quality. We provide an explicit solution to the optimization problem for one of the most practical constraint combinations. For other constraint combinations, we determine feasible regions for flight, that can then be searched to establish the UAV's optimal location. In many cases, the 2-dimensional optimal location is achieved by a 1-dimensional search. We discuss two heuristic approaches to UAV placement, and show that in some cases they are able to achieve close to optimal, but that in other cases significant gains can be achieved by employing our developed solutions., Comment: 12 pages, 6 figures

Artificial Intelligence (AI) solutions for wireless location estimation are likely to prevail in many real-world scenarios. In this work, we demonstrate for the first time how the Cramer-Rao upper bound on localization accuracy can facilitate efficient neural-network solutions for wireless location estimation. In particular, we demonstrate how the number of neurons for the network can be intelligently chosen, leading to AI location solutions that are not time-consuming to run and less likely to be plagued by over-fitting. Experimental verification of our approach is provided. Our new algorithms are directly applicable to location estimates in many scenarios including the Internet of Things, and vehicular networks where vehicular GPS coordinates are unreliable or need verifying. Our work represents the first successful AI solution for a communication problem whose neural-network design is based on fundamental information-theoretic constructs. We anticipate our approach will be useful for a wide range of communication problems beyond location estimation., Comment: This work has been submitted to the IEEE for possible publication

Covert communication prevents legitimate transmission from being detected by a warden while maintaining certain covert rate at the intended user. Prior works have considered the design of covert communication over conventional low-frequency bands, but few works so far have explored the higher-frequency millimeter-wave (mmWave) spectrum. The directional nature of mmWave communication makes it attractive for covert transmission. However, how to establish such directional link in a covert manner in the first place remains as a significant challenge. In this paper, we consider a covert mmWave communication system, where legitimate parties Alice and Bob adopt beam training approach for directional link establishment. Accounting for the training overhead, we develop a new design framework that jointly optimizes beam training duration, training power and data transmission power to maximize the effective throughput of Alice-Bob link while ensuring the covertness constraint at warden Willie is met. We further propose a dual-decomposition successive convex approximation algorithm to solve the problem efficiently. Numerical studies demonstrate interesting tradeoff among the key design parameters considered and also the necessity of joint design of beam training and data transmission for covert mmWave communication., Comment: Submitted for possible journal publication

We propose to use channel inversion power control (CIPC) to achieve one-way ultra-reliable and low-latency communications (URLLC), where only the transmission in one direction requires ultra reliability and low latency. Based on channel reciprocity, our proposed CIPC schemes guarantee the power of received signal that is used to decode the information to be a constant value $Q$, by varying the transmit signal and power, which relaxes the assumption of knowing channel state information (CSI) at the user. Thus, the CIPC schemes eliminate the overhead of CSI feedback, reduce communication latency, and explore the benefits of multiple antennas to significantly improve transmission reliability. We derive analytical expressions for the packet loss probability of the proposed CIPC schemes, based on which we determine a closed interval and a convex set for optimizing $Q$ in CIPC with imperfect and perfect channel reciprocity, respectively. Our results show that CIPC is an effective means to achieve one-way URLLC. The tradeoff among reliability, latency, and required resources (e.g., transmit antennas) is further revealed, which provides novel principles for designing one-way URLLC systems., Comment: Submitted to IEEE Transactions on Communications. arXiv admin note: text overlap with arXiv:1909.00908

In this letter, we consider the requirement of information freshness in covert communications for the first time. With artificial noise (AN) generated from a full-duplex (FD) receiver, we formulate a covertness maximization problem under the average age of information (AoI) constraint to optimize the transmit probability of information signal. In particular, the transmit probability not only represents the generation rate of information signal but also represents the prior probability of the alternative hypothesis in covert communications, which builds up a bridge between information freshness and communication covertness. Our analysis shows that the best transmit probability is not always 0.5, which differs from the equal prior probabilities assumption in most related works on covert communications. Furthermore, the limitation of average AoI enlarges the transmit probability at the cost of the covertness reduction and leads to a positive lower bound on the information transmit power for non-zero covertness., Comment: 5 pages, 3 figures

In this paper, we analyze pilot contamination (PC) attacks on a multi-cell massive multiple-input multiple-output (MIMO) network with correlated pilots. We obtain correlated pilots using a user capacity-achieving pilot sequence design. This design relies on an algorithm which designs correlated pilot sequences based on signal-to-interference-plus-noise ratio (SINR) requirements for all the legitimate users. The pilot design is capable of achieving the SINR requirements for all users even in the presence of PC. However, this design has some intrinsic limitations and vulnerabilities, such as a known pilot sequence and the non-zero cross-correlation among different pilot sequences. We reveal that such vulnerabilities may be exploited by an active attacker to increase PC in the network. Motivated by this, we analyze the correlated pilot design for vulnerabilities that can be exploited by an active attacker. Based on this analysis, we develop an effective active attack strategy in the massive MIMO network with correlated pilot sequences. Our examinations reveal that the user capacity region of the network is significantly reduced in the presence of the active attack. Importantly, the SINR requirements for the worst-affected users may not be satisfied even with an infinite number of antennas at the base station., Comment: Accepted to appear in IEEE Transactions on Communications. DOI and other information will be added later

This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs. Specifically, a full-duplex (FD) UAV collects data from each scheduled SN on the ground and generates artificial noise (AN) to prevent the scheduled SN's confidential information from being wiretapped by other unscheduled SNs. We first derive the reliability outage probability (ROP) and secrecy outage probability (SOP) of a considered fixed-rate transmission, based on which we formulate an optimization problem that maximizes the minimum average secrecy rate (ASR) subject to some specific constraints. We then transform the formulated optimization problem into a convex problem with the aid of first-order restrictive approximation technique and penalty method. The resultant problem is a generalized nonlinear convex programming (GNCP) and solving it directly still leads to a high complexity, which motivates us to further approximate this problem as a second-order cone program (SOCP) in order to reduce the computational complexity. Finally, we develop an iteration procedure based on penalty successive convex approximation (P-SCA) algorithm to pursue the solution to the formulated optimization problem. Our examination shows that the developed joint design achieves a significant performance gain compared to a benchmark scheme., Comment: 13 pages, 6 figures

Covert communication hides the transmission of a message from a watchful adversary while ensuring reliable information decoding at the receiver, providing enhanced security in wireless communications. In this letter, covert communication in the presence of a multi-antenna adversary and under delay constraints is considered. Under the assumption of quasi-static wireless fading channels, we analyze the effect of increasing the number of antennas employed at the adversary on the achievable throughput of covert communication. It is shown that in contrast to a single-antenna adversary, a slight increase in the number of adversary's antennas drastically reduces the covert throughput, even for relaxed covertness requirements., Comment: Accepted to appear in IEEE Transactions on Vehicular Technology as Correspondence

In this work, we consider one-way ultra-reliable and low-latency communication (URLLC), where only the transmission in one direction requires URLLC and the transmission in the opposite direction does not. In order to meet the low-latency requirement of the one-way URLLC, we propose to use a truncated channel inversion power control (CIPC) to eliminate the requirement and the associated overhead of the training-based channel estimation at the receiver, while utilizing the multi-antenna technique at the transmitter to enhance the communication reliability. We first derive the transmission outage probability achieved by the truncated CIPC by considering the impact of a finite blocklength and a maximum transmit power constraint. Then, we determine the optimal constant power of the received signals in the truncated CIPC, which minimizes the transmission outage probability. Our examination shows that the proposed truncated CIPC is an effective means to achieve the one-way URLLC, where the tradeoff among reliability, latency, and required resources (e.g., the required number of transmit antennas, or the required maximum transmit power) is revealed., Comment: 6 pages, 3 figures, accepted by IEEE GlobeCOM 2019 Workshop on Future Wireless Access for Industrial IoT

In this work, for the first time, we tackle channel estimation design with pilots in the context of covert wireless communication. Specifically, we consider Rayleigh fading for the communication channel from a transmitter to a receiver and additive white Gaussian noise (AWGN) for the detection channel from the transmitter to a warden. Before transmitting information signals, the transmitter has to send pilots to enable channel estimation at the receiver. Using a lower bound on the detection error probability, we first prove that transmitting pilot and information signals with equal power can minimize the detection performance at the warden, which is confirmed by the minimum detection error probability achieved by the optimal detector based on likelihood ratio test. This motivates us to consider the equal transmit power in the channel estimation and then optimize channel use allocation between pilot and information signals in covert wireless communication. Our analysis shows that the optimal number of the channel uses allocated to pilots increases as the covertness constraint becomes tighter. In addition, our examination shows that the optimal percentage of all the available channel uses allocated to channel estimation decreases as the total number of channel uses increases., Comment: 6 pages, 3 figures

Ultra-reliable and low-latency communication (URLLC) is one category of service to be provided by next-generation wireless networks. Motivated by increasing security concerns in such networks, this article focuses on physical layer security (PLS) in the context of URLLC. The PLS technique mainly uses transmission designs based on the intrinsic randomness of the wireless medium to achieve secrecy. As such, PLS is of lower complexity and incurs less latency than traditional cryptography. In this article, we first introduce appropriate performance metrics for evaluating PLS in URLLC, illustrating the tradeoff between latency, reliability, and security. We then identify the key challenging problems for achieving PLS for URLLC, and discuss the role that channel state information can have in providing potential solutions to these problems. Finally, we present our recommendations on future research directions in this emerging area., Comment: 6 pages, 4 figures; Accepted by IEEE Wireless Communications

This work considers unmanned aerial vehicle (UAV) networks for collecting data covertly from ground users. The full-duplex UAV intends to gather critical information from a scheduled user (SU) through wireless communication and generate artificial noise (AN) with random transmit power in order to ensure a negligible probability of the SU's transmission being detected by the unscheduled users (USUs). To enhance the system performance, we jointly design the UAV's trajectory and its maximum AN transmit power together with the user scheduling strategy subject to practical constraints, e.g., a covertness constraint, which is explicitly determined by analyzing each USU's detection performance, and a binary constraint induced by user scheduling. The formulated design problem is a mixed-integer non-convex optimization problem, which is challenging to solve directly, but tackled by our developed penalty successive convex approximation (P-SCA) scheme. An efficient UAV trajectory initialization is also presented based on the Successive Hover-and-Fly (SHAF) trajectory, which also serves as a benchmark scheme. Our examination shows the developed P-SCA scheme significantly outperforms the benchmark scheme in terms of achieving a higher max-min average transmission rate from all the SUs to the UAV.

Low probability of detection (LPD) communication has recently emerged as a new transmission technology to address privacy and security in wireless networks. Recent studies have established the fundamental limits of LPD communication in terms of the amount of information bits that can be conveyed from a transmitter to a receiver subject to a constraint on a warden's detection error probability. The established information-theoretic metric enables analytical studies on the design and performance of LPD communication under various channel conditions. In this article, we present the key features of LPD communication and discuss various important design considerations. Firstly, we clarify the differences between LPD communication and the well-known physical-layer security. Then, from an information-theoretic point of view, we discuss the optimal signalling strategies for transmitting the message-carrying signal and artificial-noise signal for LPD communication. Finally, we identify the key challenges in the design of practical LPD communication systems and point out future research directions in this context. This article provides guidelines for designing practical LPD communication strategies in wireless systems and networks., Comment: 7 pages, 5 figures, A slightly different version has been accepted by IEEE Wireless Communications

Location information will play a very important role in emerging wireless networks such as Intelligent Transportation Systems, 5G, and the Internet of Things. However, wrong location information can result in poor network outcomes. It is therefore critical to verify all location information before further utilization in any network operation. In recent years, a number of information-theoretic Location Verification Systems (LVSs) have been formulated in attempts to optimally verify the location information supplied by network users. Such LVSs, however, are somewhat limited since they rely on knowledge of a number of channel parameters for their operation. To overcome such limitations, in this work we introduce a Machine Learning based LVS (ML-LVS). This new form of LVS can adapt itself to changing environments without knowing the channel parameters. Here, for the first time, we use real-world data to show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate this improved performance within the context of vehicular networks using Received Signal Strength (RSS) measurements at multiple verifying base stations. We also demonstrate the validity of the ML-LVS even in scenarios where a sophisticated adversary optimizes her attack location., Comment: 5 pages, 3 figures

Location information claimed by devices will play an ever-increasing role in future wireless networks such as 5G, the Internet of Things (IoT). Against this background, the verification of such claimed location information will be an issue of growing importance. A formal information-theoretic Location Verification System (LVS) can address this issue to some extent, but such a system usually operates within the limits of idealistic assumptions on a-priori information on the proportion of genuine users in the field. In this work we address this critical limitation by using a Neural Network (NN) showing how such a NN based LVS is capable of efficiently functioning even when the proportion of genuine users is completely unknown a-priori. We demonstrate the improved performance of this new form of LVS based on Time of Arrival measurements from multiple verifying base stations within the context of vehicular networks, quantifying how our NN-LVS outperforms the stand-alone information-theoretic LVS in a range of anticipated real-world conditions. We also show the efficient performance for the NN-LVS when the users' signals have added Non-Line-of-Site (NLoS) bias in them. This new LVS can be applied to a range of location-centric applications within the domain of the IoT., Comment: 6 Pages, 5 figures

This work considers covert communications in the context of unmanned aerial vehicle (UAV) networks, aiming to hide a UAV for transmitting critical information out of a scenario that is monitored and where communication is not allowed. Specifically, the UAV as a transmitter intends to transmit information to a legitimate receiver (Bob) covertly in order to avoid being detected by a warden (Willie) with location uncertainties at Bob and/or Willie. In order to enhance the considered covert communication performance, we prefer to jointly optimize the UAV's trajectory and transmit power in terms of maximizing the average covert transmission rate from the UAV to Bob subject to transmission outage constraint and covertness constraint. The formulated optimization problem is difficult to tackle directly due to the intractable constraints. As such, we first employ conservative approximation to transform a constraint into a deterministic form and then apply the first-order restrictive approximation to transform the optimization problem into a convex form. By applying the successive convex approximation (SCA) technique, an efficient iterative algorithm is developed to solve the optimization problem. Our examination shows that the developed joint trajectory and transmit power optimization scheme achieves significantly better covert communication performance as compared to a benchmark scheme.

Covert communications hide the transmission of a message from a watchful adversary while ensuring a certain decoding performance at the receiver. In this work, a wireless communication system under fading channels is considered where covertness is achieved by using a full-duplex (FD) receiver. More precisely, the receiver of covert information generates artificial noise with a varying power causing uncertainty at the adversary, Willie, regarding the statistics of the received signals. Given that Willie's optimal detector is a threshold test on the received power, we derive a closed-form expression for the optimal detection performance of Willie averaged over the fading channel realizations. Furthermore, we provide guidelines for the optimal choice of artificial noise power range, and the optimal transmission probability of covert information to maximize the detection errors at Willie. Our analysis shows that the transmission of artificial noise, although causes self-interference, provides the opportunity of achieving covertness but its transmit power levels need to be managed carefully. We also demonstrate that the prior transmission probability of 0.5 is not always the best choice for achieving the maximum possible covertness, when the covert transmission probability and artificial noise power can be jointly optimized., Comment: 13 pages, 11 figures, Accepted for publication in IEEE Transactions on Wireless Communications

In this work, we consider delay-constrained covert communications with the aid of a full-duplex (FD) receiver. Without delay constraints, it has been shown that the transmit power of artificial noise (AN) at the FD receiver should be random in order to enhance covert communications. In this work, we show that transmitting AN with a fixed power indeed improve covert communications with delay constraints, since in a limited time period the warden cannot exactly learn its received power. This explicitly shows one benefit of considering practical delay constraints in the context of covert communications. We analyze the optimal transmit power of AN for either fixed or globally optimized transmit power of covert information, based on which we also determine the specific condition under which transmitting AN by the FD receiver can aid covert communications and a larger transmit power of AN always leads to better covert communication performance.

With non-orthogonal multiple access(NOMA), we tackle the maximization of the secrecy rate for the strong user subject to a maximum allowable secrecy outage probability, while guaranteeing a constraint on the transmission rate to the weak user. For the first time, the dependence between the eavesdropper's ability to conduct successive interference cancellation and her channel quality is considered. We determine the optimal power allocation and the redundancy rate, based on which the cost of security in terms of the reduction in the strong user's secrecy rate is examined and the benefits of NOMA for secure transmissions are explicitly revealed.

In this work, we examine the optimality of Gaussian signalling for covert communications with an upper bound on $\mathcal{D}(p_{_1}||p_{_0})$ or $\mathcal{D}(p_{_0}||p_{_1})$ as the covertness constraint, where $\mathcal{D}(p_{_1}||p_{_0})$ and $\mathcal{D}(p_{_0}||p_{_1})$ are different due to the asymmetry of Kullback-Leibler divergence, $p_{_0}(y)$ and $p_{_1}(y)$ are the likelihood functions of the observation ${y}$ at the warden under the null hypothesis (no covert transmission) and alternative hypothesis (a covert transmission occurs), respectively. Considering additive white Gaussian noise at both the receiver and the warden, we prove that Gaussian signalling is optimal in terms of maximizing the mutual information of transmitted and received signals for covert communications with an upper bound on $\mathcal{D}(p_{_1}||p_{_0})$ as the constraint. More interestingly, we also prove that Gaussian signalling is not optimal for covert communications with an upper bound on $\mathcal{D}(p_{_0}||p_{_1})$ as the constraint, for which as we explicitly show skew-normal signalling can outperform Gaussian signalling in terms of achieving higher mutual information. Finally, we prove that, for Gaussian signalling, an upper bound on $\mathcal{D}(p_{_1}||p_{_0})$ is a tighter covertness constraint in terms of leading to lower mutual information than the same upper bound on $\mathcal{D}(p_{_0}||p_{_1})$, by proving $\mathcal{D}(p_{_0}||p_{_1}) \leq \mathcal{D}(p_{_1}||p_{_0})$., Comment: 12 pages, 6 figures; Accepted by IEEE Transactions on Wireless Communications

In this work, we propose a novel beamforming design to enhance physical layer security of a non-orthogonal multiple access (NOMA) system with the aid of artificial noise (AN). The proposed design uses two scalars to balance the useful signal strength and interference at the strong and weak users, which is a generalized version of the existing beamforming designs in the context of physical layer security for NOMA. We determine the optimal power allocation among useful signals and AN together with the two optimal beamforming scalars in order to maximize the secrecy sum rate (SSR). Our asymptotic analysis in the high signal-to-noise ratio regime provides an efficient and near-optimal solution to optimizing the beamforming scalars and power allocation coefficients. Our analysis indicates that it is not optimal to form a beam towards either the strong user or the weak user in NOMA systems for security enhancement. In addition, the asymptotically optimal power allocation informs that, as the transmit power increases, more power should be allocated to the weak user or AN signals, while the power allocated to the strong user keeps constant. Our examination shows that the proposed novel beamforming design can significantly outperform two benchmark schemes.

In this paper, a novel joint design of beamforming and power allocation is proposed for a multi-cell multiuser multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network. In this network, base stations (BSs) adopt coordinated multipoint (CoMP) for downlink transmission. We study a new scenario where the users are divided into two groups according to their quality-of-service (QoS) requirements, rather than their channel qualities as investigated in the literature. Our proposed joint design aims to maximize the sum-rate of the users in one group with the best-effort while guaranteeing the minimum required target rates of the users in the other group. The joint design is formulated as a non-convex NP-hard problem. To make the problem tractable, a series of transformations are adopted to simplify the design problem. Then, an iterative suboptimal resource allocation algorithm based on successive convex approximation is proposed. In each iteration, a rank-constrained optimization problem is solved optimally via semidefinite program relaxation. Numerical results reveal that the proposed scheme offers significant sum-rate gains compared to the existing schemes and converges fast to a suboptimal solution., Comment: 14 pages, 8 figures. This paper has been accepted by IEEE Transactions on Wireless Communications. The citation information: X. Sun, N. Yang; S. Yan; Z. Ding; D.W.K. Ng; C. Shen, and Z. Zhong, "Joint Beamforming and Power Allocation in Downlink NOMA Multiuser MIMO Networks," to appear in IEEE Transactions on Wireless Communications, 2018. [Online] https://ieeexplore.ieee.org/document/8375979/

This work examines the possibility, performance limits, and associated costs for a self-sustained relay to transmit its own covert information to a destination on top of forwarding the source's information. Since the source provides energy to the relay for forwarding its information, the source does not allow the relay's covert transmission and is to detect it. Considering the time switching (TS) and power splitting (PS) schemes for energy harvesting, where all the harvested energy is used for transmission at the self-sustained relay, we derive the minimum detection error probability $\xi^{\ast}$ at the source, based on which we determine the maximum effective covert rate $\Psi^{\ast}$ subject to a given covertness constraint on $\xi^{\ast}$. Our analysis shows that $\xi^{\ast}$ is the same for the TS and PS schemes, which leads to the fact that the cost of achieving $\Psi^{\ast}$ in both the two schemes in terms of the required increase in the energy conversion efficiency at the relay is the same, although the values of $\Psi^{\ast}$ in these two schemes can be different in specific scenarios. For example, the TS scheme outperforms the PS scheme in terms of achieving a higher $\Psi^{\ast}$ when the transmit power at the source is relatively low. If the covertness constraint is tighter than a specific value, it is the covertness constraint that limits $\Psi^{\ast}$, and otherwise it is upper bound on the energy conversion efficiency that limits $\Psi^{\ast}$., Comment: This work has been submitted to the IEEE for possible publication

We propose a location-aware pilot allocation algorithm for a massive multiple-input multiple-output (MIMO) network with high-mobility users, where the wireless channels are subject to Rician fading. Pilot allocation in massive MIMO is a hard combinatorial problem and depends on the locations of users. As such, it is highly complex to achieve the optimal pilot allocation in real-time for a network with high-mobility users. Against this background, we propose a low-complexity pilot allocation algorithm, which exploits the behavior of line-of-sight (LOS) interference among the users and allocate the same pilot sequence to the users with small LOS interference. Our examination demonstrates that our proposed algorithm significantly outperforms the existing algorithms, even with localization errors. Specifically, for the system considered in this work, our proposed algorithm provides up to 37.26% improvement in sum spectral efficiency (SE) and improves the sum SE of the worst interference-affected users by up to 2.57 bits/sec/Hz, as compared to the existing algorithms., Comment: Accepted to appear in IEEE Transactions on Vehicular Technology

In this work, we adopt channel inversion power control (CIPC) to achieve covert communications aided by a full-duplex receiver. Specifically, the transmitter varies the power and phase of transmitted signals as per the channel to the receiver, such that the receiver can decode these signals without knowing the channel state information. This eliminates the required feedback from the transmitter to the receiver, which aids hiding the transmitter from a warden. The truncated CIPC and conventional CIPC schemes are proposed and examined, where for truncated CIPC covert transmission ceases when the channel quality from the transmitter to the receiver is low, while for conventional CIPC covert transmission always occurs regardless of this channel quality. We examine their performance in terms of the achieved effective covert throughput (ECT), which quantifies the amount of information that the transmitter can reliably convey to the receiver, subject to the constraint that the warden's detection error probability is no less than some specific value. Our examination shows that the truncated CIPC scheme can outperform the conventional CIPC scheme due to this constraint.

In this paper, we study covert communication in wireless networks consisting of a transmitter, Alice, an intended receiver, Bob, a warden, Willie, and a Poisson field of interferers. Bob and Willie are subject to uncertain shot noise due to the ambient signals from interferers in the network. With the aid of stochastic geometry, we analyze the throughput of the covert communication between Alice and Bob subject to given requirements on the covertness against Willie and the reliability of decoding at Bob. We consider non-fading and fading channels. We analytically obtain interesting findings on the impacts of the density and the transmit power of the concurrent interferers on the covert throughput. That is, the density and the transmit power of the interferers have no impact on the covert throughput as long as the network stays in the interference-limited regime, for both the non-fading and the fading cases. When the interference is sufficiently small and comparable with the receiver noise, the covert throughput increases as the density or the transmit power of the concurrent interferers increases.

In this work, an adaptive and robust null-space projection (AR-NSP) scheme is proposed for secure transmission with artificial noise (AN)-aided directional modulation (DM) in wireless networks. The proposed scheme is carried out in three steps. Firstly, the directions of arrival (DOAs) of the signals from the desired user and eavesdropper are estimated by the Root Multiple Signal Classificaiton (Root-MUSIC) algorithm and the related signal-to-noise ratios (SNRs) are estimated based on the ratio of the corresponding eigenvalue to the minimum eigenvalue of the covariance matrix of the received signals. In the second step, the value intervals of DOA estimation errors are predicted based on the DOA and SNR estimations. Finally, a robust NSP beamforming DM system is designed according to the afore-obtained estimations and predictions. Our examination shows that the proposed scheme can significantly outperform the conventional non-adaptive robust scheme and non-robust NSP scheme in terms of achieving a much lower bit error rate (BER) at the desired user and a much higher secrecy rate (SR). In addition, the BER and SR performance gains achieved by the proposed scheme relative to other schemes increase with the value range of DOA estimation error., Comment: 9 pages, 12 figures, IoT