Reconfigurable intelligent surface (RIS)-enhanced secure wireless system has captured widespread concern recently. However, there is still a lack of research on the evaluation of security threats from the illegal RIS (IRIS) deployed by malicious eavesdroppers. In this paper, we investigate the signal leakage brought by IRIS in typical RIS-enhanced millimeter wave (mmWave) multiple-input multiple-output (MIMO) wiretap system. We firstly illustrate the detriment and challenges of IRIS with respect to system secrecy rate (SR) competition. The existence of IRIS aggravates the difficulty in detecting behaviors of the eavesdropper, making it infeasible to globally solve the SR competition problem. Therefore, we propose an artificial noise (AN)-based interference scheme to moderate the security degradation caused by IRIS. Specifically, the transmit precoder, receiver combiner and RIS discrete phase shifts are jointly designed by exploiting the interpolation search method and sparsity of mmWave channels for the sake of generating more AN. Simulation results demonstrate the non-negligible SR degradation caused by IRIS and validate the effective security enhancement of the proposed scheme. Besides, we emphatically analyze specific system factors that should be weighed to better combat IRIS especially when IRIS is powerful.

As a new development of Internet of Things (IoT), Internet of Video Things (IoVT) emerges to provide novel services based on video sensing, transmission, storage and analysis. However, IoVT also imposes massive computation and transmission on mobile edge computing (MEC) systems with a large amount of offloaded video data. To address this issue, in this paper, we propose a non-orthogonal multiple access (NOMA) assisted multi-MEC IoVT system. Although NOMA-assisted MEC systems have been proposed in existing studies, non-negligible inter-device interference caused by NOMA transmission has not been investigated, which is much more serious in multi-MEC IoVT systems. To combat them, we perform joint optimization on grouping and offloading in the proposed NOMA-assisted multi-MEC IoVT system. To achieve optimal performance, a utility minimization problem is formulated where the definition of utility leverages critical performance metrics including energy consumption and delay. We prove that this problem can be modeled as an exact potential game. Then, a selection algorithm is proposed to find the optimal strategies for each IoVT device by obtaining the Nash equilibrium. Specifically, power allocation for IoVT devices can be handled by a computation resource minimization problem. Simulation results demonstrate that the proposed algorithm achieves significant performance gains compared with existing schemes, i.e., at least 34.8% reduction in total utility and 14.5% less power consumption.

As a promising technology with disruptive innovation, user-centric network can meet the data traffic demand and user service demand in the future mobile networks. It completely transforms the traditional cell-centric paradigm into user-centric paradigm, requiring the deployment of a large number of access points (APs). However, the massive deployment of APs leads to issues such as high hardware cost, huge power consumption and complex interference management. Reconfigurable intelligent surfaces (RIS), as another promising technology, can expand signal coverage, suppress interference, reduce hardware cost and power consumption. Inspired by this, we propose a novel RIS assisted user-centric network that uses RIS to replace some low utilization APs to reduce cost as well as power consumption, and deploy more RIS to achieve energy-efficient user-centric communication. In order to take full advantages of RIS, we jointly optimize passive beamforming at RIS and power control at AP to maximize the energy efficiency of the network. Since this problem is intractable and non-convex, an effectively alternating optimization algorithm, capitalizing on fractional programming and successive lower-bound maximization is proposed. The simulation results verify that the proposed algorithm outperforms reference algorithms in terms of energy efficiency and sum rate.

Intelligent reflecting surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider a multi-IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impact of IRS on user association in multi-BS mmWave systems, we formulate a sum rate maximization problem by jointly optimizing passive beamforming at IRS, power allocation and user association. This leads to an intractable non-convex problem, for which to tackle we propose a computationally affordable iterative algorithm, capitalizing on alternating optimization, sequential fractional programming (SFP) and forward-reverse auction (FRA). In particular, passive beamforming at IRS is optimized by utilizing the SFP method, power allocation is solved through means of standard convex optimization method, and user association is handled by the network optimization based FRA algorithm. Simulation results demonstrate that the proposed algorithm can achieve significant performance gains, e.g., it can provide up to 147% higher sum rate compared with the benchmark and 116% higher energy efficiency compared with amplify-and-forward relay.

In millimeter wave (mmWave) systems, the advanced lens antenna array can effectively reduce the radio frequency chains cost. However, the mmWave signal is still vulnerable to blocking obstacles and suffers from severe path loss. To address this problem, we propose an intelligent reflecting surface (IRS) enhanced multi-user mmWave communication system with lens antenna array. Moreover, we attempt to optimize energy efficiency in the proposed system. An energy efficiency maximization problem is formulated where the transmit beamforming at base station and the reflect beamforming at IRSs are jointly considered. To solve this non-convex problem, we propose an algorithm based on the alternating optimization technique. In the proposed algorithm, the transmit beamforming is handled by the sequential convex approximation method and the reflect beamforming is optimized based on the quadratic transform method. Our simulation results show that the proposed algorithm can achieve significant energy efficiency improvement under various scenarios.

In this paper, we investigate an intelligent reflect surface (IRS) assisted multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to alleviate the blockage effect and enhance the performance of the mmWave system. Considering the impact of IRS on user association, we formulate a sum rate maximization problem by jointly optimizing the passive beamforming at IRS and user association, which is an intractable non-convex problem. Then an alternating optimization algorithm is proposed to solve the problem efficiently. In the proposed algorithm, passive beamforming at IRS is optimized by utilizing the fractional programming method and user association is solved through the network optimization based auction algorithm. We provide numerical comparisons between the proposed algorithm and different reference algorithms. Simulation results demonstrate that the proposed algorithm can achieve significant gains in the sum rate of all users.