3D Beamforming in Reconfigurable Intelligent Surfaces-assisted Wireless Communication Networks

Reconfigurable Intelligent Surfaces (RIS) or Intelligent Reflecting Surfaces (IRS) are metasurfaces that can be deployed in various places in wireless environments to make these environments controllable and reconfigurable. In this paper, we investigate the problem of using 3D beamforming in RIS-empowered wireless networks and propose a new scheme that provides more degrees of freedom in designing and deploying the RIS-based networks. In the proposed scheme, a base station (BS) equipped with a full dimensional array of antennas optimizes its radiation pattern in the three-dimensional space to maximize the received signal to noise ratio at a target user. We also study the effect of angle of incidence of the received signal by the RIS on its reflecting properties and find a relation between this angle and the BS antenna array's tilt and elevation angles. The user receives the signal from a reflected path from the RIS as well as from a direct path from the BS which both depend on the BS antenna array's tilt and elevation angles. These angles and also the RIS element's phase shifts are jointly numerically optimized. Our simulation results show that using RIS-assisted 3D beamforming with optimized phase shifts and radiation angles can considerably improve the performance of wireless networks

Attacking Massive MIMO Cognitive Radio Networks by Optimized Jamming

Massive multiple-input multiple-output (MaMIMO) and cognitive radio networks (CRNs) are two promising technologies for improving spectral efficiency of next-generation wireless communication networks. In this paper, we investigate the problem of physical layer security in the networks that jointly use both technologies, named MaMIMO-CRN. Specifically, to investigate the vulnerability of this network, we design an optimized attacking scenario to MaMIMO-CRNs by a jammer. For having the most adversary effect on the uplink transmission of the legitimate MaMIMO-CRN, we propose an efficient method for power allocation of the jammer. The legitimate network consists of a training and a data transmission phase, and both of these phases are attacked by the jammer using an optimized power split between them. The resulting power allocation problem is non-convex. We thus propose three different efficient methods for solving this problem, and we show that under some assumptions, a closed-form solution can also be obtained. Our results show the vulnerability of the MaMIMO-CRN to an optimized jammer. It is also shown that increasing the number of antennas at the legitimate network does not improve the security of the network