In this paper, we propose intelligent reflecting surface (IRS) aided multi-antenna physical layer security. We present a power efficient scheme to design the secure transmit power allocation and the surface reflecting phase shift. It aims to minimize the transmit power subject to the secrecy rate constraint at the legitimate user. Due to the non-convex nature of the formulated problem, we propose an alternative optimization algorithm and the semidefinite programming (SDP) relaxation to deal with this issue. Also, the closed-form expression of the optimal secure beamformer is derived. Finally, simulation results are presented to validate the proposed algorithm, which highlights the performance gains of the IRS to improve the secure transmission.

This paper studies the impact of an intelligent reflecting surface (IRS) on computational performance in a mobile edge computing (MEC) system. Specifically, an access point (AP) equipped with an edge server provides MEC services to multiple internet of thing (IoT) devices that choose to offload a portion of their own computational tasks to the AP with the remaining portion being locally computed. We deploy an IRS to enhance the computational performance of the MEC system by intelligently adjusting the phase shift of each reflecting element. A joint design problem is formulated for the considered IRS assisted MEC system, aiming to optimize its sum computational bits and taking into account the CPU frequency, the offloading time allocation, transmit power of each device as well as the phase shifts of the IRS. To deal with the non-convexity of the formulated problem, we conduct our algorithm design by finding the optimized phase shifts first and then achieving the jointly optimal solution of the CPU frequency, the transmit power and the offloading time allocation by considering the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical evaluations highlight the advantage of the IRS-assisted MEC system in comparison with the benchmark schemes.

We consider an intelligent reflecting surface (IRS) wireless-powered NOMA Internet-of-Things (IoT) network, where multiple NOMA IoT devices can communicate with access point (AP) with the aid of IRS. Specifically, IoT devices can harvest energy from a nearby appropriative power station (PS) during the wireless energy transfer (WET) phase through the direct links as well as the reflecting links from IRS. Then, the harvested energy can achieve the wireless information transfer (WIT) during the uplink between IoT devices and AP by adopting NOMA protocol. A novel resource allocation scheme is proposed to maximized the sum throughput of the considered system by jointly optimizing the time allocation factor and phase shift matrices of WET and WIT. The optimization problem is non-convex due to multiple variables. We first transfer this problem into convex function by assuming one of the phase shift matrices fixed, and solve the transferred problem by obtaining closed-form solution. Then, the elements collaborative approximate (ECA) and the manifold space gradient descent (MSGD) algorithms are designed to optimize the phase shift matrix. ECA is iteratively optimizing one element and MSGD is focus on the derivation of a gradient descent over the manifold space. Numerical results indicate the sum throughput enhancement by IRS compared with no IRS, and highlight the advantages of IRS.

In this letter, we investigate the energy efficiency (EE) problem in a millimeter wave massive MIMO system with non-orthogonal multiple access (NOMA). Multiple two-user clusters are formulated according to their channel correlation and gain difference. Following this, we propose a hybrid analog/digital precoding scheme for the low radio frequency chains structure at the base station. On this basis, we formulate a power allocation problem aiming to maximize the EE under users' quality of service requirements and per-cluster power constraint. An iterative algorithm is proposed to obtain the optimal power allocation. Simulation results show that the proposed NOMA scheme achieves superior EE performance than conventional OMA scheme.

This paper investigates a wireless powered sensor network (WPSN), where multiple sensor nodes are deployed to monitor a certain external environment. A multi-antenna power station (PS) provides the power to these sensor nodes during wireless energy transfer (WET) phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center (FC) during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, the paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading based Stackelberg game, linear energy trading based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.

This work exploits the potential of a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) in improving the security in a multiple-input single-output (MISO) network, where three transmission protocols, namely, energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The objective is to maximize the weighted sum secrecy rate (WSSR) by jointly designing the beamforming (BF) and the transmitting and reflecting coefficients (TARCs). A path-following based technique is first developed to covert the non-convex problem and design the BF and the TARCs in an alternating manner. Then, for the ES scheme, the TARCs are solved by a penalty concave convex procedure (PCCP) method. Besides, the proposed method is extended to the MS by solving a mixed-integer problem. While for the TS scheme, a two-layer optimization method is proposed. Finally, the simulation results validate the superiority of STAR-RIS.