This paper investigates uplink transmission rate enhancement in space-air-ground integrated networks (SAGIN) by jointly designing precoders for a multi-antenna ground user and an unmanned aerial vehicle (UAV). Assuming a stationary relative position between the UAV and the satellite, we propose two joint precoding designs to optimize the uplink transmission rate while considering the practical finite signaling. We introduce an alternating iteration optimization approach (AIOA) when accurate channel state information (CSI) of the Rician link from the ground user and UAV is available. Additionally, we account for the statistical CSI induced by multi-path effects in various terrestrial environments, and derive a new closed-form expression for the uplink transmission rate. Building on this, a convex optimization framework is formulated by vectoring the optimization matrix and introducing auxiliary variables to tackle the non-convexity of the problem. Then, the AIOA is further adopted to jointly optimize the ground user and UAV precoders, significantly reducing computational complexity. Simulation results confirm the efficiency of the proposed AIOAs improving uplink transmission rates in SAGIN.

Cooperated with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and unmanned aerial vehicle (UAV), a non-orthogonal multiple access (NOMA) covert communication network is conceived. However, open channels are more vulnerable to eavesdropping byWardens, overlying that the channel state information (CSI) may be unstable with UAV’s time-varying deployments. In this paper, a deep reinforcement learning (DRL)-based and instantaneous channel-deducted framework is investigated for resolving the cutting-edge maximization problem of covert communication rate, subjected to the UAV flight, QoS requirement, and communication covertness. Given the channels instability caused by time-varying UAV flight, we design a channel deduction network by integrating complex-domain multi-layer perceptron (CMixer) and recurrence-based bidirectional long-short term memory (BiLSTM) to exploit the nonlinear correlations of channels in time, spatial location, and antenna domains. Relying on the states with deducted channels, the Twin Delayed Deep Deterministic policy gradient (TD3) as a proactive and policy-based DRL algorithm is used to iteratively train an agent responsible for adaptive adjusting UAV deployment and STAR-RIS beamforming. Simulation results demonstrate the effectiveness of the proposed channel deduction scheme, covert communication mechanism, and their synthesis.

In this work, we focus on developing near-field beamforming schemes for covert wireless communications to maximize the communication quality while ensuring communication covertness, where three different antenna architectures are considered. Specifically, we first analyze the monotonicities of the throughput and the minimum detection error probability with respect to the signal-to-noise ratio (SNR) at a legitimate receiver and a warden. Then, the formulated optimization problem is transformed into a semi-definite programming (SDP) problem via vectorizing the matrix. Lastly, a maximum signal-to-leakage ratio (MSLR) scheme with a low complexity is proposed. Our results indicate that the proposed near-field beamforming schemes facilitate covert communication in both the distance and angle dimensions. The two hybrid antenna architectures lead to a slight performance loss, but significantly reduce the circuit budget. Meanwhile, the low-complexity algorithm greatly reduces the algorithm complexity, although it exhibits a noticeable disadvantage in its covert transmission performance. Furthermore, our results indicate that the designed near-field beamforming strategies are more beneficial in meeting the covertness constraints, highlighting a feasibility of covert communications in the near-field region.

In this work, we consider an intelligent reflecting surface (IRS)-assisted covert wireless communication system, in which the location of a warden Willie follows a binomial point process (BPP) distribution within a disk centered at O with a radius of R and the warden is outside a safety zone centered at C with a radius of r. The location of the safety zone can be anywhere (inside, outside, or having intersections) relative to the disk. We first analyze the detection performance of Willie for the cases with known channel state information (CSI) and unknown CSI, respectively, based on which we determine Willie's minimum detection error rate. Then, considering the geometric randomness of Willie's position inside the disk but outside the safety zone, the average minimum detection error rate is determined and adopted in the covertness constraint in the subsequent covert system design. Our examination first shows that the covert communication rate generally increases with R. However, more interestingly, whether the covert communication rate increases or decreases with r generally depends on the relative locations of the disk, the safety zone, and Alice. For example, when both the safety zone and Alice are on the same side of the disk center O, the covert rate increases with r, and vice versa.

For the sake of enhancing the covertness and sensing performance in the integrated sensing and covert communications (ISCC) system, we design a beamforming framework for reconfigurable intelligence surface (RIS) assisted ISCC. Specifically, the system intends to transmit information to a legitimate receiver (Bob) covertly and sense the target simultaneously while avoiding being detected by a warden (Willie). RIS can be applied to both traditional single-connected networks and a broad fully-connected networks. By jointly optimizing the beamforming vector of the communication and the autocorrelation matrix of the sensing, and the phase shift matrix of the RIS, both the convert rate and the target’s probing power are maximized. And the covertness and the constant-mode constraints are considered. A multi-strategy alternate optimization (MSAO) algorithm is proposed to solve the optimization problem based on quadratic constraint quadratic programming (QCQP) and semidefinite relaxation (SDR). Furthermore, we consider a more realistic application scenario where the legal party has imperfect channel state information of Willie. Simulation results show that deploying RIS in a generalized fully-connected mode can achieve better transmission of beampatterns and increase upper limit of covert communications rate than conventional single-connected mode.

This paper studies a convincingly secure transmission framework under an allowable outage probability for practical finite-alphabet inputs, where a full-duplex receiver (Bob) is taken into account to emit the artificial noise for deteriorating the eavesdropper’s decoding performance. We develop a secure-reliable mechanism to take the place of prior secrecy rate (SR) maximization strategy, where a closed form expression is invoked for substituting the non-closed SR expression upon exploiting the multi-exponential decay fitting approach. Hence, the intractable expectation operation over a large number of noise samples is circumvented. Moreover, a pair of critical probabilities of the reliable transmission and secure outage are first analyzed, and then a low-complexity optimization scheme is formulated. Apart from designing the transmission scheme for classically secure networks consisting of a transmitter, Bob and an eavesdropper, we further carry out an investigation on conceiving a secure-reliable strategy against multiple Eves for improving the system’s extensibility. To this end, analytical expressions of both the reliability outage and secrecy outage probabilities for multiple-Eve scenarios are also derived. Furthermore, a pragmatic iterative solution is conceived for addressing the corresponding max-min optimization problem. Finally, the simulation results validate the significance of our considered secure-reliable transmission in terms of the average SR performance attained.

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}(V)$] to indicate that the system throughput goes up as the queue backlog increases, where$V$is a control parameter between the system throughput and the queue backlog. However, we find that, as$V$goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e.,$\mathcal {O}(V)$). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}((\log (V))^2)$] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only$\mathcal {O}((\log (V))^2)$queue backlog.

In this paper, we propose anew dynamic IRS beamformingframework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming, namelyuser-adaptiveIRS beamforming,UL-adaptiveIRS beamforming, andstatic IRS beamforming, by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

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 work examines the performance gain achieved by deploying an intelligent reflecting surface (IRS) for delay-constrained covert communications. To this end, we formulate the joint design of the transmit power and the IRS reflection coefficients, including its phase shifts and reflection amplitudes, to maximize the communication quality subject to a covertness constraint. We first prove that perfect covertness is achievable with the aid of the IRS even for a single-antenna transmitter, which is impossible without the IRS. Then, we develop a penalty-based successive convex approximation (PSCA) algorithm to tackle the design optimization problem. Considering the high complexity of the PSCA algorithm, we further propose a low-complexity two-stage algorithm, where closed-form expressions for the transmit power and the IRS's reflection coefficients are derived. Our examination shows that significant performance gain can be achieved by deploying an IRS into covert communications.

This work considers unmanned aerial vehicle (UAV) networks for collecting data covertly from ground users. The full-duplex (FD) 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 (ATR) from all the SUs to the UAV.

Technically, the security performance of generalized spatial modulation (GenSM) networks can be enhanced by dynamically adjusting the precoder allocated to the legitimate signal as communication channel varies. For this purpose, our paper proposes a secure transmission strategy upon designing both digital and analog precoders for hybrid GenSM systems, where an eavesdropper is taken into account. The concept of the hybrid GenSM system has arose to improve the spatial multiplexing (SMX) gain for remedying the shortcoming of the limited number of radio frequency chains in traditional GenSM systems. However, this may lead to a great deal of security degradation since the SMX gain of the unintended receiver will be also improved. To this end, we develop a secrecy enhancement scheme by devising both analog and digital precoders for hybrid GenSM networks. Specifically, we derive an efficiently closed-form alternative to the original secrecy rate (SR) expression for reducing the excessive computational complexity of the joint optimization problem over the hybrid precoder. Then, by using this alternative as our cost function, an iterative algorithm is proposed. In particular, we elaborately conceive a pair of concave maximization problems in order to optimize the digital and analog precoders, respectively. Our proposed strategy not only utilizes semi-positive definite relaxing technique over the analog precoder but also invokes a lower bound of the alternative to further simplify the optimization over the vectored digital precoder. Subsequently, both the convergence and computational complexity of the proposed alternating iteration algorithm are analyzed. Compared to existing designs, our proposed strategy strikes a compelling role in balancing the SR performance and complexity. Finally, our simulation results confirm the efficiency of the proposed algorithm in terms of the SR performance achieved.

Covert communications can hide the very existence of wireless transmissions and thus are able to address privacy issues in numerous applications of the emerging Internet of Things (IoT). In this article, we adopt channel inversion power control (CIPC) to achieve covert communications in Rayleigh fading wireless networks, where a transmitter can possibly hide itself from a warden while transmitting information to a receiver. The CIPC can guarantee a constant signal power at the receiver, which removes the requirement that the receiver has to know the channel state information in order to coherently decode the transmitter's signal. Specifically, we examine the performance of the achieved covert communications in terms of the effective covert rate (ECR), which quantifies the amount of information that the transmitter can reliably convey to the receiver subject to the warden's total error probability being no less than some specific value. The noise uncertainty at the warden serves as the enabler of covert communications. For fairness, we also consider noise uncertainty at the legitimate receiver. Our examination shows that increasing the noise uncertainty at the warden and the receiver simultaneously may not continuously improve the ECR achieved in the considered system model.

Secure wireless information and power transfer based on directional modulation is conceived for amplify-and-forward relaying networks. Explicitly, we first formulate a secrecy rate maximization (SRM) problem, which can be decomposed into a twin-level optimization problem and solved by a one-dimensional (1D) search and semidefinite relaxation (SDR) technique. Subsequently, in order to reduce the search complexity, we formulate an optimization problem based on maximizing the signal-to-leakage-AN-noise-ratio (Max-SLANR) criterion, and transform it into a SDR problem. In addition, the relaxation is proved to be tight according to the classic Karush-Kuhn-Tucker (KKT) conditions. Finally, to reduce the computational complexity, a successive convex approximation (SCA) scheme is proposed to find a near-optimal solution. The complexity of the SCA scheme is much lower than that of the SRM and the Max-SLANR schemes. Simulation results demonstrate that the performance of the SCA scheme is very close to that of the SRM scheme in terms of its secrecy rate and bit error rate, but much better than that of the zero forcing scheme.