Secrecy Rate Maximization in Multi-IRS Millimeter Wave Networks
arXiv:2010.01113 · doi:10.1016/j.phycom.2021.101436
Abstract
This paper investigates the problem of increasing the security at the physical layer of a Millimeter Wave (mmWave) network equipped with several Intelligent Reflecting Surfaces (IRSs). In this network, multiple IRSs help the Base Station (BS) to reach the signal to the desired user and at the same time maintain the security of the network i.e. securing the signal from receiving by the unallowable eavesdropper. The target of the proposed scheme is to maximize the secrecy rate by jointly optimizing the active beamforming at the BS and passive beamforming at the IRSs. This leads to a non-convex optimization problem which we solve by decomposing into two sub-problems. The sub-problems alternatively solve the active and passive beamforming design problems using the Semi-Definite Relaxation (SDR) technique. Finally, simulations are done to assess the performance of the proposed algorithm. These results show the superiority of using multiple IRSs in the enhancement of the secrecy rate in the wireless networks that operate in the mmWave frequency bands.
arXiv comment20 pages, 6 figures
References in corpus (8)
- Spatially Sparse Precoding in Millimeter Wave MIMO Systems
- Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
- Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
- Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces
- Secure Wireless Communication via Intelligent Reflecting Surface
- MISO Wireless Communication Systems via Intelligent Reflecting Surfaces
- Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design
- Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication