core challenge in physical-layer security is the difficulty of obtaining the channel state information (CSI) of potential eavesdroppers. The inherent sensing functionality of integrated sensing and communication (ISAC) systems offers a promising solution by enabling the estimation of key parameters, such as the eavesdropper’s angles of departure (AoDs). Capitalizing on this capability, we propose a sensing-assisted secure communication scheme for a movable antenna (MA)-aided ISAC system. The scheme comprises two stages: eavesdropper AoD sensing and secure communication. In the first stage, the base station (BS) optimizes the positions of its transmit and receive MAs to enhance sensing accuracy. We derive the closed-form Cramèr-Rao bound (CRB) for the estimated AoDs to fundamentally characterize how MA positions influence the estimation uncertainty. In the second stage, the BS ensures secure communication by designing a robust beamforming vector that accounts for the AoD uncertainty region and by further optimizing the transmit MAs’ positions to maximize the secrecy rate. To manage the end-to-end design, we formulate a joint optimization problem. This intractable non-convex problem is decomposed into two subproblems. For the first subproblem, we develop an alternating optimization (AO) algorithm to solve the CRB minimization problem. For the second subproblem, we solve the worst-case secrecy rate maximization problem using a method based on backward induction, convex hull construction, and AO. Finally, simulation results are provided to demonstrate the significant advantages of the proposed scheme compared to various benchmarks.

This study addresses the challenges of near-field interference suppression and resource allocation in extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, particularly under dense-user scenarios. We propose a quality-of-service (QoS)-aware joint user scheduling and power control scheme. Leveraging the spherical wave (SW) characteristics of near field channels, a dual-domain interference suppression strategy is developed by analyzing the spatial correlation of beam focusing vectors in terms of both angular separation and distance constraints. Based on this, a spatial correlation-based scheduling (SCS) algorithm is designed. By integrating this user selection strategy with a dynamic power allocation mechanism, the proposed approach optimizes the sum spectral efficiency while ensuring the user QoS. This framework is further extended to modular XL-MIMO systems. We show how modular deployment can enhance spatial resolution and develop an adapted QoS-aware user scheduling algorithm, called modular SCS (SCS-mod), for this architecture. Simulation results validate that the proposed algorithms significantly outperform existing schemes in terms of sum spectral efficiency and the number of scheduled users, especially under high user density and high transmission power conditions.

A space-air-ground integrated network consisting of a satellite, high altitude platforms (HAPs), unmanned aerial vehicles (UAVs), and terrestrial Internet of Things (IoT) devices is constructed to collect wide-area information. The IoT devices sense the environmental information, the UAVs fly to collect data, and the HAPs deliver the computation results to the satellite. In order to improve the information freshness, the age of information (AoI) of the system is minimized by the UAV trajectory design and network configuration under the cost and practical constraints. The optimization is decomposed into two stages, which are jointly conducted by the HAPs and UAVs. In the first stage, each UAV and IoT device cluster are paired, and the UAV obtains the minimum AoI along with the optimal destined position by deep reinforcement learning (DRL). Afterwards, the HAP performs the matching between the UAVs and the IoT device clusters by the Gale-Shapley algorithm. In the second stage, the HAPs complete the configuration of the coverage area and height of the HAPs and UAVs by the soft actor-critic DRL algorithm. The extensive simulation verifies the AoI deduction of the proposed scheme and depicts the regularities of network configuration and UAV trajectory design for the minimum AoI achievement.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where multiple ground devices offload portions of their latency-sensitive and mission-critical computational tasks to a UAV-carried MEC server for remote computing and compute the remaining portions locally. To meet the low-latency requirements of the MEC, ultra-reliable and low-latency communication (URLLC) is used to offload tasks from the devices to the UAV. We minimize the maximum computation latency among all devices by jointly optimizing the computing times and CPU frequencies of the devices and the UAV, the offloading bandwidths of the devices, and the three-dimensional location of the UAV. We propose an algorithm that decomposes the joint optimization problem into three subproblems, which optimize the UAV’s horizontal location, the UAV’s altitude, and the offloading bandwidths and computing CPU frequencies, respectively. In solving the subproblems, the data rate expression of the devices’ finite-blocklength offloading is accurately approximated by a tractable logarithmic function, and the successive convex approximation technique is applied to tackle the non-convex structure. Furthermore, a semi-closed-form solution to the subproblem that optimizes the bandwidths and CPU frequencies is derived to reduce the complexity. Simulation results show that the proposed algorithm can significantly reduce the system’s computation latency compared to the benchmark schemes.

In wireless sensor networks, unmanned aerial vehicles (UAVs) can be employed to collect data from sensor nodes (SNs) efficiently. In this article, we consider a dual-UAV-enabled (long-distance) data collection system, where one UAV is dispatched to collect data from distributed SNs, while the other UAV is employed to relay data from the data-collection UAV to a fusion center (FC) that locates far from the SNs. To shorten the time duration for the FC to collect all data, we propose to minimize the completion time of the data collection task by jointly optimizing the transmit power and bandwidth of all SNs and the UAVs, as well as the three-dimensional trajectories of the two UAVs. Instead of assuming the simplified line-of-sight UAV-ground channel model as in most existing works, we model the channels between the UAVs and SNs as well as that between the UAVs and FC by applying the practically more accurate elevation-angle-dependent Rician fading channel model. The resulting optimization problem is nonconvex and thus is difficult to solve in general. Nevertheless, we propose an algorithm to solve it efficiently by using the techniques of block coordinate descent, slack variable substitution, and successive convex approximation. Simulation results show that our proposed algorithm can achieve higher communication efficiency than other benchmark schemes and greatly reduce the task completion time for data collection.

Active intelligent reflecting surface (IRS) has been recently proposed to alleviate the product path loss attenuation inherent in the IRS-aided cascaded channel. In this paper, we study an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, an active IRS is deployed to assist a multi-antenna access point (AP) to convey information and energy simultaneously to multiple single-antenna information users (IUs) and energy users (EUs). Two joint transmit and reflect beamforming optimization problems are investigated with different practical objectives. The first problem maximizes the weighted sum-power harvested by the EUs subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while the second problem maximizes the weighted sum-rate of the IUs subject to individual energy harvesting (EH) constraints at the EUs. The optimization problems are non-convex and difficult to solve optimally. To tackle these two problems, we first rigorously prove that dedicated energy beams are not required for their corresponding semidefinite relaxation (SDR) reformulations and the SDR is tight for the first problem, thus greatly simplifying the AP precoding design. Then, by capitalizing on the techniques of alternating optimization (AO), SDR, and successive convex approximation (SCA), computationally efficient algorithms are developed to obtain suboptimal solutions of the resulting optimization problems. Simulation results demonstrate that, given the same total system power budget, significant performance gains in terms of operating range of wireless power transfer (WPT), total harvested energy, as well as achievable rate can be obtained by our proposed designs over benchmark schemes (especially the one adopting a passive IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the users for the effective operation of WPT/SWIPT.

In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of secondary transmitter (ST) and the IRS’s reflect beamforming, subject to the constraints of the minimum required signal-to-interference-plus-noise ratio at the primary receiver, the ST’s maximum transmit power, and the unit modulus of the IRS reflect beamforming vector. This joint optimization problem can be solved suboptimally by the non-convex optimization techniques, which however usually require complicated mathematical transformations and are computationally intensive. To address this challenge, we propose an algorithm based on the deep deterministic policy gradient (DDPG) method. To achieve a higher learning efficiency and a lower reward variance, we propose another algorithm based on the soft actor-critic (SAC) method. In these proposed algorithms, a reward impact adjustment approach is proposed to improve their learning efficiency and stability. Simulation results show that the two proposed algorithms can achieve comparable SU rate performance with much shorter running time, as compared to the existing non-convex optimization-based benchmark algorithm, and that the proposed SAC-based algorithm learns faster and achieves a higher average reward with lower variance, as compared to the proposed DDPG-based algorithm.

Unmanned aerial vehicle (UAV) communication is anticipated to be widely applied in the forthcoming fifth-generation wireless networks, due to its many advantages such as low cost, high mobility, and on-demand deployment. However, the broadcast and line-of-sight nature of air-to-ground wireless channels give rise to a new challenge on how to realize secure UAV communications with the destined nodes on the ground. This paper aims to tackle this challenge by applying the physical layer security technique. We consider both the downlink and uplink UAV communications with a ground node, namely, UAV-to-ground (U2G) and ground-to-UAV (G2U) communications, respectively, subject to a potential eavesdropper on the ground. In contrast to the existing literature on the wireless physical layer security only with the ground nodes at fixed or quasi-static locations, we exploit the high mobility of the UAV to proactively establish favorable and degraded channels for the legitimate and eavesdropping links, through its trajectory design. We formulate new problems to maximize the average secrecy rates of the U2G and G2U transmissions, by jointly optimizing the UAV's trajectory, and the transmit power of the legitimate transmitter over a given flight period of the UAV. Although the formulated problems are non-convex, we propose iterative algorithms to solve them efficiently by applying the block coordinate descent and successive convex optimization methods. Specifically, both the transmit power and UAV trajectory are optimized, with the other being fixed in an alternating manner, until the algorithms converge. The simulation results show that the proposed algorithms can improve the secrecy rates for both U2G and G2U communications, as compared to other benchmark schemes without power control and/or trajectory optimization.

The non-orthogonal multiple access (NOMA) technique can provide higher spectral efficiency and massive connectivities to wireless networks. The wireless power transfer (WPT) technique is a controllable and promising way to solve the energy scarcity problem of wireless devices. In this paper, we introduce the NOMA and WPT techniques into a multi-user wireless network, where multiple users need to transmit their information to an information receiver within a very limited spectrum and they suffer from the energy scarcity problem. We consider a harvest-then-transmit protocol by dividing each transmission block into two time-slots. In the first time-slot, a power station sends dedicated energy to the users via wireless energy beamforming. In the second time-slot, using their harvested energy in the previous time-slot, the users transmit their information to the information receiver in the NOMA manner. In this network, the throughput can be optimized by jointly optimizing the energy beamforming at the power station, the transmit powers of the users, as well as the time allocation between the two time-slots. We propose an algorithm to find the optimal solution of the throughput maximizing joint energy beamforming and resource allocation problem. Simulation results show that the proposed algorithm achieves higher throughput than the benchmark scheme.

In this paper, we investigate the effect of Multiple- User Multiple-Input Multiple-Output (MU-MIMO) enabled WiFi Access Point (AP) on the performance of traffic offloading in LTE-Unlicensed (LTE-U) networks. We first derive closed-form expressions for the downlink rates of both cellular user and offloaded user under limited Channel State Information (CSI) feedback, and obtain the sum-rate of the system. Then, we validate our analysis by numerical simulations and evaluate the system performance under different number of offloaded users and various CSI feedback lengths. The evaluation illustrates that there is a trade-off between the number of users offloaded to WiFi AP and the sum-rate of the system, and performance inflection point lies on the SNR conditions. Meanwhile, increasing CSI feedback length for the cellular Base Station (BS) or the WiFi AP alone helps increase the sum-rate of the system and rate of corresponding users, while it sacrifices the rate of users in the other network. These results suggest that adaptive utilization of antenna numbers on the MU-MIMO enabled AP based on SNR, and deciding CSI feedback length according to practical traffic demand of corresponding users are critical to achieve high rate performance for traffic offloading in LTE-U networks.

Unmanned aerial vehicle (UAV) communications has drawn significant interest recently due to many advantages such as low cost, high mobility, and on-demand deployment. This paper addresses the issue of physical-layer security in a UAV communication system, where a UAV sends confidential information to a legitimate receiver in the presence of a potential eavesdropper which are both on the ground. We aim to maximize the secrecy rate of the system by jointly optimizing the UAV's trajectory and transmit power over a finite horizon. In contrast to the existing literature on wireless security with static nodes, we exploit the mobility of the UAV in this paper to enhance the secrecy rate via a new trajectory design. Although the formulated problem is non-convex and challenging to solve, we propose an iterative algorithm to solve the problem efficiently, based on the block coordinate descent and successive convex optimization methods. Specifically, the UAV's transmit power and trajectory are each optimized with the other fixed in an alternating manner until convergence. Numerical results show that the proposed algorithm significantly improves the secrecy rate of the UAV communication system, as compared to benchmark schemes without transmit power control or trajectory optimization.

Simultaneous wireless information and power transfer (SWIPT) is a potential technique to solve the energy scarcity problem in wireless communication networks. Information transmission security is one of the key issues to be addressed in SWIPT. In this study, the authors consider guaranteeing the information transmission security of a multiple‐input multiple‐output non‐regenerative relay system with SWIPT in the physical layer by designing source and relay beamforming to maximise the security rate of the system. The problem is to design the source signal and artificial noise (AN) beamforming and the relay forwarding beamforming jointly, which is highly non‐convex. They first propose an alternating optimisation‐based algorithm, which designs the beamforming matrices at the source and relay separately and alternately. Next, they propose a joint design algorithm which designs the source signal and AN beamforming and the relay beamforming together. Finally, they propose a non‐iterative design algorithm with low complexity. The performances of the proposed design algorithms have been verified by computer simulations.

For conventional subcarrier pairing scheme in cooperative orthogonal frequency division multiplexing decode‐and‐forward multi‐relay networks, to avoid interference, each subcarrier pair (SP) is assigned to a single relay. Over a specific subcarrier, the destination receives signals transmitted from the relay. In this study, to better exploit the degrees of spatial freedom, the authors propose to assign each SP to multiple relays. Thus, over a specific subcarrier, the destination receives signals transmitted from multiple relays. Under the total network power constraint, to maximise the sum transmission rate, they propose a joint resource allocation scheme, in which they jointly optimised the four types of resources: assisting relays selection, transmission mode selection, subcarrier pairing and power allocation. They further propose a suboptimal algorithm which can significantly reduce the computational complexity of the aforementioned optimal allocation scheme with sacrificing little on the performance. It is shown from simulation results that the author's proposed schemes have significant performance improvement over the resource allocation schemes in the literature.

Cognitive radio is a potential technique to solve the spectrum shortage problem in wireless communications. Integrating wireless relaying into the cognitive radio networks can further improve the spectrum efficiency. In this study, a multiple‐input multiple‐output cognitive relay network is considered, where the primary network (PN) consists of one transmitter‐receiver pair and the secondary network consists of multiple active source‐destination pairs and non‐regenerative relays. All nodes in the networks are deployed with multiple antennas. How to avoid interference to the PN is an important task in the secondary network design. The precoders of the secondary sources and the receivers of the secondary destinations are designed in a well‐known zero‐forcing way. The secondary relays forward signals for the secondary source‐destination pairs by beamforming. With interference cancellation constraints and individual transmit power constraints, a relay beamforming scheme is proposed to maximise the total signal‐to‐interference‐plus‐noise ratio (SINR) at the secondary destinations. Then a maximising minimum SINR relay beamforming scheme is further proposed to provide max–min fairness among the secondary source‐destination pairs. The beamforming problems are formulated into the quadratically constrained quadratic fractional programming problems, and are solved by the semi‐definite relaxation technique. The performances of these beamforming schemes have been verified by computer simulations.

For amplify-and-forward multi-antenna relay networks with energy harvesting (EH) constraint, we study the optimal relay beamforming problem which maximizes the achievable rate from source to information-decoding receiver subject to the transmit power constraint at relay and the EH constraint at EH receiver. Because of the EH constraint, the beamforming problem is not convex. We propose the optimal beamforming scheme by converting the beamforming problem into a convex semidefinite programming with the rank-one relaxation and Charnes-Cooper transformation. We also propose a suboptimal closed-form beamforming scheme. It is shown from simulations that when the maximum allowable relay transmit power to noise power ratio is high, the performance of proposed suboptimal scheme approaches that of the optimal scheme.

The antenna subset selection technique balances the performance and hardware cost in the multiple-input multiple-output (MIMO) systems, and the problems on the antenna selection in MIMO relay systems have not been fully solved. This paper considers antenna selection on amplify-and-forward (AF) and decode-and-forward (DF) MIMO relay systems to maximise capacity. Since the optimal antenna selection algorithm has high complexity, two fast algorithms are proposed. The selection criterion of the algorithm for AF relay is to maximise a lower bound of the capacity, but not the exact capacity. This criterion reduces algorithmic complexity. The algorithm for DF relay is an extension of an existing antenna subset selection algorithm for one-hop MIMO systems. The authors show the derivations of the algorithms in detail, and analyse their complexity in terms of numbers of complex multiplications. Simulation results show that the proposed algorithms for both cases achieve comparable performance to the optimal algorithm under various conditions, and have decreased complexity.