Secrecy Efficiency Maximization in Intelligent Reflective Surfaces Assisted UAV Communications

This paper focuses on the secrecy efficiency maximization in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communication.With the popularization of UAV technology,more and more communication scenarios need UAV support.We consider using IRS to improve the secrecy efficiency.Specifically,IRS and UAV trajectories work together to counter potential eavesdroppers,while balancing the secrecy rate and energy consumption.The original problem is difficult to solve due to the coupling of optimization variables.We first introduce secrecy efficiency as an auxiliary variable and propose relaxation optimization problem,and then prove the equivalence between relaxation problem and the original problem.Then an iterative algorithm is proposed by applying the block coordinate descent(BCD)method and the inner approximationmethod.The simulation results show that the proposed algorithm converges fast and is superior to the existing schemes.In addition,in order to improve the robustness of the algorithm,we also pay attention to the case of obtaining imperfect channel state information(CSI).

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Distributed IRS-Aided DF Relaying Systems:Performance Analysis and Optimization

Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning massive reflecting elements.This paper considers a distributed IRS aided decode-and-forward(DF)relaying system over Nakagami-m fading channels.Based on a tight approximation for the distribution of the received signalto-noise ratio(SNR),we first derive exact closed-form expressions of the outage probability,ergodic capacity,and energy efficiency for the considered system.Moreover,we propose the optimal IRS configuration considering the energy efficiency and pilot overhead.Finally,we compare the performance between the distributed IRS-aided DF relaying and multi-IRS-only systems,and verify the analytical results by using monte carlo simulations.

Transmit Power Minimization for IRS-Assisted NOMA-UAV Networks

The flexibility of unmanned aerial vehicles(UAVs)allows them to be quickly deployed to support ground users.Intelligent reflecting surface(IRS)can reflect the incident signal and form passive beamforming to enhance the signal in the specific direction.Motivated by the promising benefits of both technologies,we consider a new scenario in this paper where a UAV uses non-orthogonal multiple access to serve multiple users with IRS.According to their distance to the UAV,the users are divided into the close users and remote users.The UAV hovers above the close users due to their higher rate requirement,while the IRS is deployed near the remote users to enhance their received power.We aim at minimizing the transmit power of UAV by jointly optimizing the beamforming of UAV and the phase shift of IRS while ensuring the decoding requirement.However,the problem is non-convex.Therefore,we decompose it into two sub-problems,including the transmit beamforming optimization and phase shift optimization,which are transformed into second-order cone programming and semidefinite programming,respectively.We propose an iterative algorithm to solve the two sub-problems alternatively.Simulation results prove the effectiveness of the proposed scheme in minimizing the transmit power of UAV.

Intelligent Reflecting Surface Assisted Transmission Optimization Strategies in Wireless Networks

Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although the intelligent reflecting surface(IRS)can be adopted to create effective virtual links to address the mmWave blockage problem,the conventional solutions only adopt IRS in the downlink from the Base Station(BS)to the users to enhance the received signal strength.In practice,the reflection of IRS is also applicable to the uplink to improve the spectral efficiency.It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission.In this paper,we first design a Low-Energy Adaptive Clustering Hierarchy(LEACH)clustering protocol for clustering and data collection.Then,the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices,the uplink and downlink transmission times,the active beamforming at the BS,and the IRS dynamic beamforming.To solve this non-convex optimization problem,we propose an alternating optimization(AO)-based joint solution algorithm.Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system,and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.

Intelligent Reflecting Surface-Aided Secure and Covert Communications

This work employs intelligent reflecting surface(IRS)to enhance secure and covert communication performance.We formulate an optimization problem to jointly design both the reflection beamformer at IRS and transmit power at transmitter Alice in order to optimize the achievable secrecy rate at Bob subject to a covertness constraint.We first develop a Dinkelbach-based algorithm to achieve an upper bound performance and a high-quality solution.For reducing the overhead and computational complexity of the Dinkelbach-based scheme,we further conceive a low-complexity algorithm in which analytical expression for the IRS reflection beamforming is derived at each iteration.Examination result shows that the devised low-complexity algorithm is able to achieve similar secrecy rate performance as the Dinkelbach-based algorithm.Our examination also shows that introducing an IRS into the considered system can significantly improve the secure and covert communication performance relative to the scheme without IRS.

Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading

The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system model.Specifically,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference power.Furthermore,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ratio.Subsequently,the analytical expression for the outage probability of the system in Rayleigh channels is derived and analyzed.The study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through simulation.The proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link transmission.The simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.

基于深度强化学习的IRS辅助NOMA-MEC通信资源分配优化

为了解决无法与边缘服务器建立直连通信链路的盲区边缘用户卸载任务的问题,设计了一个基于深度强化学习(deep reinforcement learning, DRL)的智能反射面(intelligent reflecting surface, IRS)辅助非正交多址(non-orthogonal multiple access, NOMA)通信的资源分配优化算法,以获得由系统和速率和能源效率(energy efficiency, EE)加权的最大系统收益,从而实现绿色高效通信。通过深度确定性策略梯度(deep deterministic policy gradient, DDPG)算法联合优化传输功率分配和IRS的反射相移矩阵。仿真结果表明,使用DDPG算法处理移动边缘计算(mobile edge computing, MEC)的通信资源分配优于其他几种对比实验算法。

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

Intelligent reflecting surface for sum rate enhancement in MIMO systems

The research for the Intelligent Reflecting Surface(IRS)which has the advantages of cost and energy efficiency has been studied.Channel capacity can be effectively increased by appropriately setting the phase value of IRS elements according to the channel conditions.However,the problem of obtaining an appropriate phase value of IRs is difficult to solve due to the non-convex problem.This paper proposes an iterative algorithm for the alternating optimal solution in the Single User Multiple-Input-Multiple-Output(SU-MIMO)systems.The proposed iterative algorithm finds an alternating optimal solution that is the phase value of IRS one by one.The results show that the proposed method has better performance than that of the randomized IRS systems.The number of iterations for maximizing the performance of the proposed algorithm depends on the channel state between the IRS and the receiver.

Impact of surface-reflected seismic waves on the seismic isolation performance of circular tunnel isolation layers

Seismic isolation is an effective strategy to mitigate the risk of seismic damage in tunnels.However,the impact of surface-reflected seismic waves on the effectiveness of tunnel isolation layers remains under explored.In this study,we employ the wave function expansion method to provide analytical solutions for the dynamic responses of linings in an elastic half-space and an infinite elastic space.By comparing the results of the two models,we investigate the seismic isolation effect of tunnel isolation layers induced by reflected seismic waves.Our findings reveal significant differences in the dynamic responses of the lining in the elastic half-space and the infinitely elastic space.Specifically,the dynamic stress concentration factor(DSCF)of the lining in the elastic half-space exhibits periodic fluctuations,influenced by the incident wave frequency and tunnel depth,while the DSCF in the infinitely elastic space remain stable.Overall,the seismic isolation application of the tunnel isolation layer is found to be less affected by surface-reflected seismic waves.Our results provide valuable insights for the design and assessment of the seismic isolation effect of tunnel isolation layers.

Intelligent Reflecting Surfaces Enabled Cognitive Internet of Things Based on Practical Pathloss Model

In this paper,we aim to unlock the potential of intelligent reflecting surfaces(IRSs)in cognitive internet of things(loT).Considering that the secondary IoT devices send messages to the secondary access point(SAP)by sharing the spectrum with the primary network,the interference is introduced by the IoT devices to the primary access point(PAP)which profits from the IoT devices by pricing the interference power charged by them.A practical path loss model is adopted such that the IRSs deployed between the IoT devices and SAP serve as diffuse scatterers,but each reflected signal can be aligned with its own desired direction.Moreover,two transmission policies of the secondary network are investigated without/with a successive interference cancellation(SIC)technique.The signal-to-interference plus noise ratio(SINR)balancing is considered to overcome the nearfar effect of the IoT devices so as to allocate the resource fairly among them.We propose a Stackelberg game strategy to characterize the interaction between primary and secondary networks.For the proposed game,the Stackelberg equilibrium is analytically derived to optimally obtain the closed-form solution of the power allocation and interference pricing.Numerical results are demonstrated to validate the performance of the theoretical derivations.

Three-Component Model for Bidirectional Reflection Distribution Function of Thermal Coating Surfaces

We present a bidirectional reflection distribution function （BRDF） model for thermal coating surfaces based on a three-component reflection assumption, in which the specular reflection is given according to the microfacet theory and Snell＇s law, the multiple reflection is considered Nth cosine distributed, and the volume scattering is uniformly distributed in reflection angles according to the experimental results. This model describes the reflection characteristics of thermal coating surfaces more completely and reasonably. Simulation and measurement results of two thermal coating samples SR107 and S781 are given to validate that this three-component model significantly improves the modeling accuracy for thermal coating surfaces compared with the existing BRDF models.

An Alternative Method to Study Wave Scattering by Semi-infinite Inertial Surfaces

A new method to solve the boundary value problem arising in the study of scattering of two-dimensional surface water waves by a discontinuity in the surface boundary conditions is presented in this paper. The discontinuity arises due to the floating of two semi-infinite inertial surfaces of different surface densities. Applying Green＇s second identity to the potential functions and appropriate Green＇s functions, this problem is reduced to solving two coupled Fredholm integral equations with regular kernels. The solutions to these integral equations are used to determine the reflection and the transmission coefficients. The results for the reflection coefficient are presented graphically and are compared to those obtained earlier using other research methods. It is observed from the graphs that the results computed from the present analysis match exactly with the previous results.

Deep Reinforcement Learning for IRS-Assisted UAV Covert Communications

Covert communications can hide the existence of a transmission from the transmitter to receiver.This paper considers an intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)covert communication system.It was inspired by the high-dimensional data processing and decisionmaking capabilities of the deep reinforcement learning(DRL)algorithm.In order to improve the covert communication performance,an UAV 3D trajectory and IRS phase optimization algorithm based on double deep Q network(TAP-DDQN)is proposed.The simulations show that TAP-DDQN can significantly improve the covert performance of the IRS-assisted UAV covert communication system,compared with benchmark solutions.

Performance Analysis of Intelligent Reflecting Surface Assisted Wireless Communication System

In this paper,we investigate the end-to-end performance of intelligent reflecting surface(IRS)-assisted wireless communication systems.We consider a system in which an IRS is deployed on a uniform planar array(UPA)configuration,including a large number of reflecting elements,where the transmitters and receivers are only equipped with a single antenna.Our objective is to analytically obtain the achievable ergodic rate,outage probability,and bit error rate(BER)of the system.Furthermore,to maximize the system’s signal-to-noise ratio(SNR),we design the phase shift of each reflecting element and derive the optimal reflection phase of the IRS based on the channel state information(CSI).We also derive the exact expression of the SNR probability density function(p.d.f.)and show that it follows a non-central Chi-square distribution.Using the p.d.f.,we then derive the theoretical results of the achievable rate,outage probability,and BER.The accuracy of the obtained theoretical results is also verified through numerical simulation.Itwas shown that the achievable rate,outage probability,and BER could be improved by increasing the number of reflecting elements and choosing an appropriate SNR regime.Furthermore,we also find that the IRS-assisted communication system achieves better performance than the existing end-to-end wireless communication.

Exploiting the Direct Link in IRS Assisted NOMA Networks with Hardware Impairments

Hardware impairments(HI)are always present in low-cost wireless devices.This paper investigates the outage behaviors of intelligent reflecting surface(IRS)assisted non-orthogonal multiple access(NOMA)networks by taking into account the impact of HI.Specifically,we derive the approximate and asymptotic expressions of the outage probability for the IRS-NOMA-HI networks.Based on the asymptotic results,the diversity orders under perfect self-interference cancellation and imperfect self-interference cancellation scenarios are obtained to evaluate the performance of the considered network.In addition,the system throughput of IRS-NOMA-HI is discussed in delay-limited mode.The obtained results are provided to verify the accuracy of the theoretical analyses and reveal that:1)The outage performance and system throughput for IRS-NOMA-HI outperforms that of the IRS-assisted orthogonal multiple access-HI(IRS-OMA-HI)networks;2)The number of IRS elements,the pass loss factors,the Rician factors,and the value of HI are pivotal to enhancing the performance of IRS-NOMAHI networks;and 3)It is recommended that effective methods of reducing HI should be used to ensure system performance,in addition to self-interference cancellation techniques.