STAR-RIS-Assisted-Full-Duplex Jamming Design for Secure Wireless Communications System
- URL: http://arxiv.org/abs/2309.04566v1
- Date: Fri, 8 Sep 2023 19:36:02 GMT
- Title: STAR-RIS-Assisted-Full-Duplex Jamming Design for Secure Wireless Communications System
- Authors: Yun Wen, Gaojie Chen, Sisai Fang, Zheng Chu, Pei Xiao, Rahim Tafazolli,
- Abstract summary: We propose a novel secure communication scheme to protect critical and sensitive devices from eavesdroppers.
We aim to maximize the FD beam shift coefficients for the ESRIS, as mode selection, as to eavesdropper amplitudes and phase shift coefficients for the MSRIS.
- Score: 38.44290756174189
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STARRIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ESRIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional selfinterference cancellation technology.
Related papers
- Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless
3D UAV Connectivity Using DRL Algorithms [8.296140341710462]
We study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems.
We propose two novel algorithms to address this problem.
arXiv Detail & Related papers (2023-11-14T06:43:36Z) - Hybrid Knowledge-Data Driven Channel Semantic Acquisition and
Beamforming for Cell-Free Massive MIMO [6.010360758759109]
This paper focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications.
We propose a hybrid knowledge-data driven method for channel semantic acquisition and multi-user beamforming in cell-free massive multiple-input multiple-output (MIMO) systems.
arXiv Detail & Related papers (2023-07-06T15:35:55Z) - Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning
Approach [66.53364438507208]
An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated.
Non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH)
An advanced LSTM based algorithm is developed to predict users' dynamic communication state.
A DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix RIS.
arXiv Detail & Related papers (2023-04-11T13:16:28Z) - Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS
Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and
Imperfect CSI [0.0]
We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts.
Our approach addresses the challenge of imperfect channel state information (CSI) and hardware impairments by considering a practical RIS amplitude model.
arXiv Detail & Related papers (2022-10-10T09:37:53Z) - Phase Shift Design in RIS Empowered Wireless Networks: From Optimization
to AI-Based Methods [83.98961686408171]
Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks.
To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources.
This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS.
arXiv Detail & Related papers (2022-04-28T09:26:14Z) - Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep
Reinforcement Learning [78.50920340621677]
Simultaneous transmitting and reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of wireless network.
In this paper, the energy efficiency (EE) problem for a non-orthogonal multiple access (NOMA) network is investigated.
A deep deterministic policy-based algorithm is proposed to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the gradient matrices at the STAR-RIS.
arXiv Detail & Related papers (2021-11-30T15:01:19Z) - Optimization-driven Deep Reinforcement Learning for Robust Beamforming
in IRS-assisted Wireless Communications [54.610318402371185]
Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver.
We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming.
We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
arXiv Detail & Related papers (2020-05-25T01:42:55Z) - Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems
Exploiting Deep Reinforcement Learning [21.770491711632832]
The reconfigurable intelligent surface (RIS) has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems.
In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL)
The proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks.
arXiv Detail & Related papers (2020-02-24T04:28:44Z) - RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment
and Passive Beamforming Design [116.88396201197533]
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS)
The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency.
A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset.
A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
arXiv Detail & Related papers (2020-01-28T14:37:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.