No Arabic abstract
Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based on $mathcal{S}$-procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI.
This paper addresses robust beamforming design for rate splitting multiple access (RSMA)-aided multiple-input single-output (MISO) visible light communication (VLC) networks. In particular, since the channel capacity of VLC is yet unknown, we derive the first theoretical bound of channel capacity of RSMA-aided VLC networks, i.e., achievable rates with closed-form expressions. For the perfect channel state information (CSI) scenario, we investigate the beamforming design to minimize the transmitted power of RSMA-aided VLC networks under the quality of service (QoS) constraint of each user and the optical power constraints, and propose a constrained-convex-concave programming (CCCP)-based beamforming design algorithm to obtain high-quality beamformers. Moreover, for the imperfect CSI scenario, we propose a robust CCCP-based beamforming design scheme for RSMA-aided VLC networks by exploiting semidefinite relaxation (SDR) technique and S-lemma. Numerical results show that the proposed RSMA schemes offer a significant spectral efficiency gain over the existing space-division multiple access (SDMA) scheme and non-orthogonal multiple access (NOMA) scheme.
In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for low-power Internet-of-Things (IoT) devices, where the IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmission energy minimization problem by a joint design of time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. Due to the high coupling of optimization variables, this problem is a non-convex optimization problem, which is difficult to solve directly. In order to solve the above-mentioned challenging problems, the alternating optimization (AO) is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is applied to solve the non-convex rank-one constraint in solving the IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the effectiveness of our proposed algorithm in reducing HAP transmission energy compared to other benchmarks.
Intelligent reflecting surface (IRS) is a promising technology to support high performance wireless communication. By adaptively configuring the reflection amplitude and/or phase of each passive reflecting element on it, the IRS can reshape the electromagnetic environment in favour of signal transmission. This letter advances the existing research by proposing and analyzing a double-IRS aided wireless communication system. Under the reasonable assumption that the reflection channel from IRS 1 to IRS 2 is of rank 1 (e.g., line-of-sight channel), we propose a joint passive beamforming design for the two IRSs. Based on this, we show that deploying two cooperative IRSs with in total K elements can yield a power gain of order O(K^4), which greatly outperforms the case of deploying one traditional IRS with a power gain of order O(K^2). Our simulation results validate that the performance of deploying two cooperative IRSs is significantly better than that of deploying one IRS given a sufficient total number of IRS elements. We also extend our line-of-sight channel model to show how different channel models affect the performance of the double-IRS aided wireless communication system.
Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on the primary user (PU), and the severe path loss and high directivity in mmWave communications make it vulnerable to blockage events, we introduce an intelligent reflecting surface (IRS) into mmWave CRNs. This paper investigates the robust secure beamforming (BF) design in the IRS-assisted mmWave CRNs. By using a uniform linear array (ULA) at the cognitive base station (CBS) and a uniform planar array (UPA) at the IRS, and supposing that the imperfect channel state information (CSI) of wiretap links is known, we formulate a constrained problem to maximize the worst-case achievable secrecy rate (ASR) of the secondary user (SU) by jointly designing the transmit BF at the CBS and reflect BF at the IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization algorithm is proposed. As for the transmit BF at the CBS, we propose a heuristic robust transmit BF algorithm to attain the BF vectors analytically. As for the reflect BF at the IRS, by means of an auxiliary variable, we transform the non-convex problem into a semi-definite programming (SDP) problem with rank-1 constraint, which is handled with the help of an iterative penalty function, and then obtain the optimal reflect BF through CVX. Finally, the simulation results indicate that the ASR performance of our proposed algorithm has a small gap with that of the optimal solution with perfect CSI compared with the other benchmarks.
This paper investigates the application of intelligent reflecting surface (IRS) in an underlay cognitive radio network (CRN), where a multi-antenna cognitive base station (CBS) utilizes spectrum assigned to the primary user (PU) to communicate with a secondary user (SU) via IRS in the presence of multiple coordinated eavesdroppers. To achieve the trade-off between the secrecy rate (SR) and energy consumption, we propose a secrecy energy efficiency (SEE) maximization scheme by jointly design the transmit beamforming at CBS and the reflect beamforming at IRS under the SR constraint of SU, the transmit power constraint of CBS, the limited interference temperature of PU and the unit modulus constraint of IRS. The problem is challenging to solve due to the coupled optimization variables and unit modulus constraint, for which an iterative alternating optimization algorithm is proposed. As for optimizing the reflect beamforming, we introduce an auxiliary variable and convert the original non-convex problem into a semi-definite programming with rank-1 constraint, and then propose an iterative penalty function based algorithm to implement the optimal reflect beamforming. As for optimizing the transmit beamforming, we convert the original problem into an equivalent subtractive form, which is further transformed into a convex function by employing the difference of two-convex functions method. Furthermore, we provide a second-order-cone-programming approximation approach to reduce the computational complexity. The effectiveness and superiority of our proposed algorithm are verified in the simulation results.