In response to the problems in unmanned aerial vehicle (UAV) communications such as limited antenna number, exposure of the line-of-sight (LoS) for eavesdroppers, and limited onboard energy, this paper proposes a secure UAV communication scheme based on movable antennas (MAs). This scheme jointly designs the user scheduling factors, the UAV beamforming, the small-scale adjustment of MA positions, and the large-scale planning of UAV trajectories to maximize the system’s minimum secrecy rate under uncertain eavesdropper regions. To solve the non-convex optimization problem, a block coordinate descent (BCD) algorithm is used to decompose the original problem into four subproblems for iterative solving, followed by an analysis of the algorithm’s convergence and computational complexity. Simulation results reveal that the UAV follows the strategy of “maximum-speed-flight-and-then-low-speed-hovering” to serve each user in sequence. In this way, it can quickly pass through the uncertain region of the eavesdroppers and minimize the distance to the served user during the flight period. The proposed scheme leverages millimeter-level MA position reconstruction and wide-range UAV trajectory design. When the system’s minimum secrecy rate is maintained at 3.5 bps/Hz, the proposed scheme can securely support approximately 20% more users than the fixed-position antenna (FPA) scheme, while achieving more than 40% reductions in both transmit power and antenna count.

This paper focuses on a movable antenna (MA) enabled downlink multi-user communication system, where a base station (BS) aims to transmit messages to multiple users with a malicious jammer and multiple eavesdroppers posing threats. Both the BS and the users feature MAs, while the jammer and eavesdroppers make use of fixed-position antennas (FPAs). In accordance with the BS’s transmission power budget, designated MA moving regions, and minimum spacing between transmitting MAs, the system’s minimum secrecy rate is maximized through the joint design of transmit beamforming and both transmitting and receiving MA locations. To solve the non-trivial problem, a block coordinate descent (BCD) algorithm employing successive convex approximation (SCA) is implemented by iteratively designing the transmit beamforming, the specific transmitting MA position, and the receiving MA positions. Herein, auxiliary variables, first- and/or second-order Taylor expansions, and approximate construction methods for non-convex terms are introduced to handle each subproblem. Numerical results indicate that repositioning the transmitting MAs is more effective in combating eavesdropping attacks, while relocating the receiving MAs is more beneficial for countering jamming attacks. In comparison to traditional FPA schemes, the proposed scheme attains a notably greater system’s minimum secrecy rate.

This article investigates an uncrewed aerial vehicle (UAV) relay network in the urban Internet of Things (IoT) environment designed to facilitate communications for ground terminals (GTs) by transmitting their data to a remote base station (BS) using the decode and forward (DF) protocol, where the orthogonal frequency division multiple access (OFDMA) is adopted for the GTs’ transmissions. To address the challenges posed by building obstructions on radio propagation, we incorporate the multiple reconfigurable intelligent surfaces (RISs) to improve the quality of the GTs-UAV and UAV-BS links. The primary objective is to maximize the sum rate of all the GTs through the joint optimization of UAV trajectory, GTs transmit power, subchannel allocation, and multi-RIS phase shifts. To this end, we reformulate the optimization problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL) approach for addressing our formulated problem, called the soft actor critic-based subchannel allocation and phase shift optimization with trajectory design (SAC-CAPSTD) algorithm. By continuously interacting with the environment, the proposed system refines its policy to determine the optimal UAV flight trajectory and the subchannel allocation strategy for GTs within their transmit power constraints. Concurrently, a discrete phase shift optimization method is implemented to adjust the phase shift for each RIS element. Finally, numerical results confirm that the proposed SAC-CATDPS algorithm can significantly achieve higher sum rate of all the served GTs and exhibit better convergence performance compared with the benchmark DRL-based algorithms.

This article explores the energy efficiency (EE) of a mobile-edge computing (MEC) architecture for Internet of Things (IoT) networks, in which multiple IoT devices (IoTDs) perform local computations while further offloading part of their tasks to the base station (BS)-enabled MEC server utilizing the time division multiple access (TDMA) protocol. To address the radio propagation issues caused by obstructions, we utilize an uncrewed aerial vehicle (UAV)-mounted active reconfigurable intelligent surface (RIS), referred to as a flying-RIS (FRIS), to reflect and even amplify the incident signal from IoTDs to the BS. Since tasks can be executed in parallel on both IoTDs and the BS, the assignment of service timeslots, the UAV trajectory, and the resource allocation for both IoTDs and FRIS should be jointly optimized to maximize system EE. To this end, we reformulate the optimization problem as a markov decision process (MDP) and introduce a deep reinforcement learning (DRL) approach for addressing the formulated problem, called the proximal policy optimization (PPO) based resource allocation with trajectory design and FRIS reflection matrix optimization (PPO-RATDFRO) algorithm. By continuously interacting within the constructed environment, the proposed system iteratively refines its policy to determine the FRIS trajectory and reflection matrix, along with the service timeslots and computation resources for each IoTD. Finally, simulation results demonstrate that the proposed PPO-RATDFRO algorithm significantly enhances EE for all served IoTDs, compared to various benchmark algorithms.

This paper studies the optimization of reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks. In this framework, a rotary-wing UAV is dispatched to collect data form multiple ground IoT devices, leveraging the signal enhancement capabilities of an RIS to improve communication efficiency. We aim to enhance the overall energy efficiency (EE), taking into account both the system transmission rate and UAV propulsion power consumption, while satisfying the UAV initial/final position constraints, transmit power limits, and RIS unit-modules phase shift requirements. To achieve this goal, we first establish a closed-form analytical model for the EE of RIS-assisted UAV-enabled IoT wireless systems. Based on this model, we subsequently formulate the EE maximization (EEM) problem by jointly optimizing time allocation, transmit power control, RIS reflection coefficients, and UAV trajectory. Due to the non-convex nature of the proposed EEM problem, we decompose it into three subproblems: 1) RIS phase shift optimization with given resource allocation and UAV trajectory, 2) time and power allocation under fixed UAV trajectory and RIS configuration, and 3) UAV trajectory design based on provided resource allocation and RIS parameters. Particularly, the optimal phase shift solution is derived in a closed form to ensure phase alignment of signals from multiple transmission paths, while the non-convex resource allocation subproblem is solved through introducing relaxation variables. For UAV trajectory design, a hybrid optimization strategy combing Dinkelbach’s method and successive convex approximation approach is adopted to transform the non-convex EE optimization problem into a convex one. Numerical simulations demonstrate that the proposed EEM scheme obtains superior EE performance compared to benchmarks with fixed trajectory or without RIS deployment.

This paper focuses on a reconfigurable intelligent surface (RIS) enhanced wireless powered mobile edge computing network. With the assistance of an RIS, multiple passive devices (PDs) first capture energy from the radiated signals of an energy station (ES), and then use a portion of the captured energy for uplink task offloading following a hybrid time- and frequency- division multiple access (HTFDMA) protocol and another portion for local task computing. Considering that the ES and the PDs are not affiliated with the same service suppliers, we propose a partial offloading (PO) scheme that formulates an energy trading framework with price incentives through a Stackelberg game to replenish energy for PO. Specifically, the PDs as a leader receive the benefits of total computation bits with the charging payments as losses by adjusting the charging price, bandwidth and time allocation, task offloading phase shifts, PDs’ transmit power, and central processing unit frequency. Meanwhile, the ES as a follower receives the benefits of charging payments with the charging costs as losses by adapting the ES’s transmit power, power transfer phase shifts, and ES’s beamforming. Following backward induction method, the follower-level decision problem is first handled via an alternating optimization algorithm with majorization minimization. Then, based on the obtained follower-level solutions, the leader-level decision problem is tackled by introducing auxiliary variables and applying Lagrangian dual and block coordinate descent algorithm. Simulations exhibit that invoking RIS results in utility gains for both the PDs and the ES, especially when there are more RIS elements, ES antennas, and PDs.

Intelligent reflecting surface (IRS)-assisted communication systems rely heavily on the precise estimation of channel state information (CSI) for their effective operation. However, obtaining the accurate CSI is challenging due to the substantial pilot overhead required in such systems. In this article, we propose a spatial attention (SA)-based method to estimate the cascaded downlink channel in an IRS-assisted millimeter-wave (mmWave) multiuser multiple input single output (MU-MISO) system that operates with the frequency division duplex (FDD) protocol. In order to estimate the channel information, we formulate it as a nonlinear prediction problem and propose a residual network-based channel estimation (RN-CE) framework that reduces the pilot overhead. To implement the RN-CE, we utilize the values of the least square (LS) estimators as coarse estimates for neural network training. Additionally, we develop a spatial attention-based RN-CE (SA-RN-CE) that integrates a lightweight SA module capable of extracting internal connections among spatial features. Simulation results demonstrate that both RN-CE and SA-RN-CE outperform the LS algorithm, minimum mean squared error (MMSE) algorithm, and convolutional neural network (CNN)-based ChannelNet algorithm in terms of normalized mean square error (NMSE). It has been found that by balancing the NMSE and pilot overhead, the length of the pilot signals used for training the networks in RN-CE and SA-RN-CE can be compressed to 1/8 of its original length, while still maintaining a minimal impact on NMSE, which is particularly advantageous in cascaded channels where pilot training overhead is typically extensive.

This paper focuses on a reconfigurable intelligent surface (RIS) assisted multi-cluster wireless powered communication network (WPCN) in the presence of hardware impairments (HIs). With the aid of an RIS, passive devices (PDs) grouped into multiple clusters first jointly collect the energy radiated by an energy source (ES), and then transmit useful information to an access point through a hybrid time- and frequency- division multiple access (HTF) scheme. Specifically, the PDs from different clusters occupy different transmission time to avoid inter-cluster interference, while the PDs within the same cluster occupy different bandwidth to avoid intra-cluster interference. Due to non-ideal hardware, the impact of the phase shift (PS) error at the RIS and the HIs at the transceivers are revealed. Then, the closed-form expression for average system sum rate (ASSR) is derived. Assuming that the ES and the PDs are deployed by different service providers, a Stackelberg game (SG) is utilized to model energy trading based on price incentive. In the established SG, the PDs act as a leader to maximize the difference between the ASSR benefit and energy payment by optimizing energy price, RIS PSs in the information tansfer stage, as well as time and bandwidth allocation. Meanwhile, the ES acts as a follower to maximize the difference between energy payment and energy cost by optimizing transmit power, energy beamforming, and RIS PSs in the energy transfer stage. The follower and leader level problems are solved through algorithms such as majorization-minimization, alternating optimization, Lagrange dual approach, Karush-Kuhn-Tucker condition, block coordinate descent, and one-dimensional search. Simulation results verify that compared to the benchmark schemes, the proposed HTF scheme achieves higher PDs’ utility value and lower ES’s energy consumption, and strikes a balance between utility and overhead.

This paper integrates a reconfigurable intelligent surface (RIS) and an unmanned aerial vehicle (UAV) into a wireless powered communication network. With the assistance of the RIS, multiple passive Internet of Things devices (IoTDs) gather energies from the signals of an energy station (ES) to power them to communicate with the UAV in time division multiple access mode. Given that the ES and the IoTDs are deployed by different service providers, an energy trading mechanism with price incentive is established through hierarchical Stackelberg game. By paying for the wireless charging service provided by the ES, the IoTDs aim to maximize the difference between achievable rate benefit and monetary payment, while the ES aims to maximize the difference between monetary payment and energy cost. To evaluate the impact of fairness, we propose sum-rate maximization (SRM) scheme and minimum-rate maximization (MRM) scheme, which take the sum-rate and minimum-rate of the IoTDs as achievable rate benefits, respectively. Specifically, in the follower game problem, an alternating optimization algorithm with majorization-minimization is utilized to alternately optimize energy beamforming and energy phase shifts (PSs) before optimal ES transmit power is achieved in closed form. In the leader game problem, a block coordinate descent algorithm with successive convex approximation is utilized to alternately optimize energy price, time allocation, and UAV trajectory after information PSs and transmit power of the IoTDs are achieved in closed form. Numerical results show that the combination of RIS and UAV significantly enhances the IoTD utility. Compared with the MRM scheme, the SRM scheme obtains higher IoTD utility at the cost of rate fairness and energy consumption.

This paper integrates an intelligent reflecting surface (IRS) into a wireless powered Internet-of-Things (IoT) network, where IoT devices need to harvest energy from an energy station (ES) before transmitting their monitoring data to an access point. An IRS is invoked to improve energy and spectral efficiency by changing propagation environment. Considering that the ES and IoT devices come from different operators, IoT devices need to provide monetary payment in exchange for the ES’s charging before implementing nonlinear energy harvesting. We build this energy interaction via Stackelberg game under three multiple access schemes, i.e., IRS-assisted time division multiple access (IRS-TDMA), IRS-assisted non-orthogonal multiple access (IRS-NOMA), and IRS-assisted frequency division multiple access (IRS-FDMA). To solve the common follower game among the three schemes, we first employ an alternating optimization (AO) algorithm with Majorization-Minimization (MM) to alternately optimize the energy beamforming and energy phase shifts, and then derive the optimal ES transmit power. For the leader game of IRS-TDMA, the optimal time allocation and optimal information phase shifts are first derived in closed form through Lagrange dual method and triangular inequality, respectively. On this basis, an AO algorithm is developed to optimize energy price and energy transfer time alternately. Similar procedures are also used to solve the leader games for IRS-NOMA and IRS-FDMA. Simulation results show that IRS-NOMA and IRS-FDMA achieve the same utilities for the ES and IoT devices. Due to IRS time selectivity, IRS-TDMA is more energy and spectral efficient than IRS-NOMA and IRS-FDMA, and this advantage is more pronounced in higher numbers of IRS elements and IoT devices.

This paper investigates an intelligent reflecting surface (IRS) aided secure wireless powered communication network (WPCN), where a transmitter first harvests energy from a power station (PS), and then uses the collected energy to transmit information to multiple internet of things (IoT) devices in the form of multicast in the presence of multiple eavesdroppers. An IRS is deployed to enhance the efficiency of wireless energy transfer (WET) and secure wireless information transfer (WIT). Considering that the PS and transmitter belong to different service providers, we model this energy interaction through a Stackelberg game and propose an IRS-aided energy trading and secure communication (IRS-ETSC) scheme, in which the transmitter needs to pay an energy price as an incentive for the PS energy service. Specifically, the transmitter as the leader can control the energy price, WET time, two-stage phase shifts, and beamforming vector, while the PS as the follower can adjust the transmit power. To solve the non-convex leader game problem, we propose a two-step approach to decompose the original problem into two subproblems. The first subproblem can be solved independently by an efficient alternating optimization (AO) based algorithm, in which the closed-form optimal beamforming vector and energy phase shifts are alternately optimized. Then, the second subproblem is relaxed by semidefinite relaxation (SDR) and solved by an iterative algorithm based on block coordinate descent (BCD), where the optimal energy price, optimal WET time, and suboptimal information phase shifts can be obtained by golden section method and successive convex approximation (SCA) respectively. Both subproblems can converge to a stationary point. Numerical results show that compared with the traditional non-IRS scheme, the proposed IRS-ETSC scheme achieves utility improvement for both the PS and transmitter.

This paper investigates an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN) with a focus on multicast transmissions in a game-theoretic way, where a transmitter first harvests energy from a power station (PS) and then transmits the information to multiple Internet of Things (IoT) devices in a multicast form. An IRS is deployed to assist the wireless energy transfer (WET) and wireless information transfer (WIT) processes. Considering that the PS and the transmitter belong to different service providers, we propose two schemes based on Stackelberg game, namely IRS-aided transmitter dominant energy trading (IRS-TDET) with the transmitter as the leader and IRS-aided PS dominant energy trading (IRS-PDET) with the PS as the leader. To solve the non-convex optimization problem of the leader-level game in the IRS-TDET scheme, we first derive the closed-form optimal phase shifts in the WET stage, and then use the semidefinite relaxation approach to solve the max-min problem about the phase shifts of the WIT stage. Finally, a low-complexity alternating optimization algorithm is developed to solve the simplified non-convex optimization problem with regard to the energy price and WET time. Numerical results show that the performance of proposed IRS-TDET and IRS-PDET is superior to that of the corresponding non-IRS aided schemes, and the deployment of IRS can effectively improve the utilities of both the PS and the transmitter. In addition, it is preferred to deploy the IRS near the transmitter in WPCN, and the transmitter can be deployed near the PS or the IoT devices to obtain higher utility.