Power Minimization for the IRS-Aided SWIPT Secure Communication System with Imperfect Cascaded Channel

Intelligent reflecting surface(IRS)-assisted simultaneous wireless information and power transfer(SWIPT)is a promising technology for prolonging the lifetime of the users and improving system performance.However,it is challenging to obtain the perfect channel state information(CSI)at the base station(BS)due to the lack of radio frequency(RF)chains at the IRS,which becomes even harder when eavesdroppers(Eves)exist.In this paper,we study the power issues in the IRS-aided SWIPT system under the imperfect CSI of BS-IRS-Eves links.We aim to minimize the transmitting power at the BS by jointly designing transmit beamforming and reflective beamforming with subject to the rate outage probability constraints of the Eves,the energy harvesting constraints of the energy receivers(ERs),and the minimum rate constraints of the information receivers(IRs).We use Bernstein-type inequality to solve the rate outage probability constraints.Afterward,the formulated nonconvex problem can be efficiently tackled by employing alternating optimization(AO)combined with semidefinite relaxation(SDR)and penalty convex-concave procedure(CCP).Numerical results demonstrate the effectiveness of the proposed scheme.

Reflecting Surface-Aided Device-to-Device Communications System

Intelligent reflecting surface(IRS)is a promising technology for its capability of reflecting the incident signal towards the desired user.IRS can improve the efficiency of wireless communication systems.This paper introduces IRS into a device-to-device(D2D)communications system to improve the throughput of the D2D network.We adopt the block coordinate descent al-gorithm and semidefinite relaxation technique to optimize the beamforming vector,power allocation and phase shift matrix.Simulation results demonstrate that IRS is able to enhance the throughput of the D2D communications system,and the proposed algorithm significantly outper-forms the other benchmark algorithms.

Distributed Intelligent Reflecting Surfaces-Aided Device-to-Device Communications System

Intelligent reflecting surface(IRS)is a revolutionizing and promising technology to improve the high transmission rate of the wireless communication systems.Specifically,an IRS consists of a great amount of low-cost passive reflecting elements,which reflect the incident signals to the desired user by collaboratively using passive beamforming.This paper introduces IRSs into a device-to-device(D2D)underlying cellular system to enhance transmission rate performance of the D2D pairs.We formulate an optimization problem of maximizing the transmission rate of the D2D pairs while satisfying the minimum required rate of the cellular users.We address this problem by jointly optimizing the reuse indicator,received beamforming,power allocation,and phase shift matrices.Block coordinate descent(BCD)algorithm is adopted to decouple the original problem into four subproblems.Closed form solutions are obtained by solving the sub-problems of optimizing the received beamforming and power allocation.Then,Kuhn-Munkres(KM)algorithm and minimization-majorization(MM)algorithm are adopted to solve the sub-problems of optimizing the reuse indicator and phase shift matrices,respectively.Simulation results demonstrate that IRSs can effectively improve the transmission rate of the D2D pairs and our proposed distributed IRSs scheme outperforms the other benchmark schemes.

Statistical characteristics of spray autoignition of transient kerosene jet in cross flow

To study statistical characteristics of the random spray autoignition,aviation kerosene was injected transiently into non-vitiated air crossflow in a flow reactor with optical accesses.The operating conditions were relevant to gas turbine combustor:the air crossflow pressure and temperature were in the range of 1.4-1.7 MPa and 830-947 K,respectively,and the jet-tocrossflow momentum flux ratios were 20,50 and 80.Statistical distributions of random ignition delay times with adequate convergence were estimated based on histograms.The dependences of the distributions on reactor pressure,temperature,and jet-to-crossflow momentum flux ratio were studied.The results show that the resulting distributions appear more concentrated with the increase of air temperature or jet-to-crossflow momentum flux ratio.And then the correlations for the mean and standard deviation of the ignition delay time sample data were developed based on the present results.Compared with the correlations of ignition delay time of homogeneous premixed gas-phase kerosene/air mixture reported in the literature,the results show a greater significance pressure dependence and lower temperature sensitivity of the ignition delay time of nonpremixed kerosene spray.

Multi-UAV Trajectory Design and Power Control Based on Deep Reinforcement Learning

In this paper,multi-unmanned aerial vehicle(multi-UAV)and multi-user system are studied,where UAVs are served as aerial base stations(BS)for ground users in the same frequency band without knowing the locations and channel parameters for the users.We aim to maximize the total throughput for all the users and meet the fairness requirement by optimizing the UAVs’trajectories and transmission power in a centralized way.This problem is non-convex and very difficult to solve,as the locations of the user are unknown to the UAVs.We propose a deep reinforcement learning(DRL)-based solution,i.e.,soft actor-critic(SAC)to address it via modeling the problem as a Markov decision process(MDP).We carefully design the reward function that combines sparse with non-sparse reward to achieve the balance between exploitation and exploration.The simulation results show that the proposed SAC has a very good performance in terms of both training and testing.

Energy Efficiency of Distributed Antenna Systems with D2D Communications Under Imperfect CSI

In this paper,we investigate the distributed antenna systems(DAS)based on device to device(DASD2D)communications under the imperfect channel state information(CSI).Our aim is to maximize the energy efficiency(EE)of the D2D users equipment(DUE)under the constraints of the maximum transmission power of D2D pairs and the quality of service(QoS)requirements of the cellular user equipment(CUE).The worst-case design is considered so that the QoS of the CUE can be guaranteed for every realization of the CSI error in the ellipsoid region.The EE objective function of the optimization problem is non-convex and non-linear,and thus this problem cannot be solved by the traditional optimization methods.To solve this problem,first we transform it to an EE maximization problem without uncertain parameters by exploiting the Markov and Cauchy-Schwartz inequality.Then using the fractional programming theory and difference of convex functions optimization method,the robust EE maximization algorithms based on the hard and soft protection method are developed to maximize the system’s EE performance,respectively.However,these two algorithms are designed at the cost of the reduced EE of the DUE.Therefore,in order to further improve the EE performance and make a trade-off between the EE performance and the robustness,the iterative update algorithms for the total power constraint and average interference constraint are developed to maximize the system’s EE performance,respectively.Simulation results demonstrate the effectiveness of the four proposed EE algorithms and illustrate the trade-off between the EE performance and robustness for the iterative update algorithms.