As a revolutionary hardware technology that can reconfigure the propagation environment, reconfigurable intelligent surfaces (RISs) have been regarded as a promising solution to enhance wireless networks. In this paper, we consider a multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, which is assisted by several RISs. In order to improve energy efficiency and reduce hardware cost, we consider that the energy transmitter (ET) in the WPT system is equipped with a constant-envelope analog beamformer, instead of a digital beamformer. Focusing on user fairness, we study a minimum received power maximization problem by jointly optimizing the ET beamforming and the RIS phase shifts, subject to the constant-envelope constraints. We iteratively solve this non-convex max-min problem by leveraging both the successive convex approximation (SCA) method and the alternating direction method of multipliers (ADMM) algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm and show attractive performance gain brought by RISs.

In this paper, we investigate the reconfigurable intelligent surface (RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) system under frequency-selective channels, and propose a low-complexity alternating optimization (AO) based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate. First, with fixed RIS phase shifts, we devise the optimal closed-form transmit beamforming vectors corresponding to different subcarriers. Then, with given active beamforming vectors, near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming (FP) combined with manifold optimization (MO) or majorization-minimization (MM) framework. Additionally, we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization (SSGM) criterion requiring lower complexity. Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation (SDR) algorithm, and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity. These results demonstrate the effectiveness of the proposed algorithms.

In this paper, a reconfigurable intelligent surface (RIS)-assisted MIMO wireless secure communication system is considered, in which a base station (BS) equipped with multiple antennas exploits statistical channel state information to communicate with a legitimate multi-antenna user, in the presence of an eavesdropper, also equipped with multiple antennas. We firstly obtain an analytical expression of the ergodic secrecy rate based on the results of large-dimensional random matrix theory. Then, a jointly alternating optimization algorithm with the method of Taylor series expansion and the projected gradient ascent method is proposed to design the transmit covariance matrix at the BS, as well as the diagonal phase-shifting matrix to maximize the ergodic secrecy rate. Simulations are conducted to demonstrate the accuracy of the derived analytical expressions, as well as the superior performance of our proposed algorithm.

This paper considers a secure multigroup multicast multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS). Specifically, we aim to minimize the transmit power at Alice via jointly optimizing the transmit beamformer, artificial noise (AN) vector and phase shifts at the IRS subject to the secrecy rate constraints as well as the unit modulus constraints of IRS phase shifts. To tackle the optimization problem, we first transform it into a semidefinite relaxation (SDR) problem, and then alternately update the transmit beamformer and AN matrix as well as the phase shifts at the IRS. In order to reduce the high computational complexity, we further propose a low-complexity algorithm based on second-order cone programming (SOCP). We decouple the optimization problem into two sub-problems and optimize the transmit beamformer, AN vector and the phase shifts alternately by solving two corresponding SOCP sub-problem. Simulation results show that the proposed SDR and SOCP schemes require half or less transmit power than the scheme without IRS, which demonstrates the advantages of introducing IRS and the effectiveness of the proposed methods.

Reconfigurable intelligent surface (RIS) can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel, enjoying the improved performance. The accurate acquisition of the instantaneous channel state information (CSI) in the cascaded RIS chain makes an indispensable contribution to the performance gains. However, it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel. In this work, a channel estimation scheme for the RIS-assisted wireless communication system is proposed, which is demonstrated to perform well in a time-variant scenario. The cascaded RIS channel is modeled as a state-space model based upon the mobility situations. In addition, to fully exploit the time correlation of channel, Kalman filter is employed by taking the prior information of channels into account. Further, the optimal reflection coefficients are derived according to the minimum mean square error (MMSE) criterion. Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.

Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multiple-output (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks.

Large intelligent surface (LIS) is considered as a new solution to enhance the performance of wireless networks[1]. LIS comprises low-cost passive elements which can be well controlled. In this paper, a LIS is invoked in the vehicular networks. We analyze the system performance under Weibull fading. We derive a novel exact analytical expression for outage probability in closed form. Based on the analytical result, we discuss three special scenarios including high SNR case, low SNR case, as well as weak interference case. The corresponding approximations for three cases are provided, respectively. In order to gain more insights, we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference, threshold and fading parameters which leads to 0 diversity order. Furthermore, we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds. Similar to the outage performance, three special cases are studied and the asymptotic expressions are provided in simple forms. A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope. Finally, we give the energy efficiency of LIS-assisted vehicular network. Numerical results are presented to verify the accuracy of our analysis. It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase shift scheme significantly.