As one of the indoor communication technologies, visible light communication (VLC) has drawn great attention for its advantages such as ultra-wide unlicensed spectrum, power saving and low complexity. The nature of the visible light propagation is an open channel, which is vulnerable to wiretapping. This paper investigates a secure VLC mechanism enabled by multiple light fixtures acting as friendly jammers. The goal of the friendly jammers is to diminish the capability of the eavesdropper to infer the undisclosed information, on the premise of causing minimal impact on the legitimate receiver. For this reason, an algorithm based on reinforcement learning is proposed to dynamically optimize the friendly jamming policy in realistic nonstationary environments. In order to resolve the difficult problem of the dimensional curse and to effectively represent the continuous state and action spaces, an algorithm based on deep reinforcement learning is devised, which utilizes deep convolutional neural networks to accelerate the convergence rate of the learning process. A differentiable neural dictionary is introduced to make full use of the experiences in similar anti-eavesdropping scenarios to improve the learning capability. Simulation results demonstrate that, the proposed schemes can achieve a higher secrecy rate and a lower bit error rate than some state-of-the-art schemes.
As one of the indoor communication technologies, visible light communication (VLC) has drawn great attention for its advantages such as ultra-wide unlicensed spectrum, power saving and low complexity. The nature of the visible light propagation is an open channel, which is vulnerable to wiretapping. This paper investigates a secure VLC mechanism enabled by multiple light fixtures acting as friendly jammers. The goal of the friendly jammers is to diminish the capability of the eavesdropper to infer the undisclosed information, on the premise of causing minimal impact on the legitimate receiver. For this reason, an algorithm based on reinforcement learning is proposed to dynamically optimize the friendly jamming policy in realistic nonstationary environments. In order to resolve the difficult problem of the dimensional curse and to effectively represent the continuous state and action spaces, an algorithm based on deep reinforcement learning is devised, which utilizes deep convolutional neural networks to accelerate the convergence rate of the learning process. A differentiable neural dictionary is introduced to make full use of the experiences in similar anti-eavesdropping scenarios to improve the learning capability. Simulation results demonstrate that, the proposed schemes can achieve a higher secrecy rate and a lower bit error rate than some state-of-the-art schemes.
The inherent broadcast characteristics of the visible light communication (VLC) channel makes VLC downlinks susceptible to unauthorized terminals in many actual VLC scenarios, such as offices and shopping centers. This paper considers a multiple-input-single-output (MISO) VLC scenario with multiple light fixtures acting as the transmitter, a VLC receiver as the legitimate user, and an eavesdropper attempting to intercept the undisclosed information. To improve the confidentiality of VLC links, a physical-layer anti-eavesdropping framework is proposed to obscure the unauthorized eavesdroppers and diminishes their capability of inferring the information through smart beamforming over the MISO VLC wiretap channel. To cope with the intractable problem of finding the theoretically optimal solution of the secrecy rate and utility for the MISO VLC wiretapping channel, a reinforcement learning (RL)-based VLC beamforming control scheme is proposed to achieve the optimal beamforming policy against the eavesdropper. Furthermore, a deep RL-based VLC beamforming control scheme is proposed to handle the curse of dimensionality for both observation space and action space and avoid the quantization error of the RL-based algorithm. Simulation results show that the proposed learning-based VLC beamforming control schemes can significantly decrease the bit error rate of the legitimate receiver and increase the secrecy rate and utility of the anti-eavesdropping MISO VLC system, compared with the benchmark strategy.
Due to the broadcast nature of visible light communication (VLC), the secrecy protection is a crucial issue. In this paper, aiming at the communication scenario of a point-to-point VLC transmission link, an anti-eavesdropping model utilizing friendly jamming is constructed, which is composed of a light-emitting diode (LED) transmitter, a legitimate receiver equipped with a photodiode (PD), and a potential eavesdropper trying to obtain the undisclosed information transmitted. In addition, the system is equipped with a friendly jammer composed of multiple LEDs. A friendly jamming scheme based on reinforcement learning (RL) is proposed, which adopts different friendly jamming actions with the dynamic change of the environment. The simulation results show that compared with the benchmark state-of-the-art method, the proposed scheme can significantly reduce the bit error rate (BER) of the legitimate receiver and improve the overall performance of the anti-eavesdropping VLC system.
Despite offering higher security than radio frequency (RF) channels, the broadcast nature of the visible light communication (VLC) channel makes VLC links inherently susceptible to eavesdropping by unauthorized users. In this work, we consider the physical-layer security of VLC links aided by friendly jamming. The jammer has multiple light sources, but does not have access to the data transmitted. The eavesdropper's reception is degraded by a jamming signal that causes no interference to the legitimate receiver. Due to the limited dynamic range of typical light-emitting diodes (LEDs), both the data and jamming signals are subject to amplitude constraints. Therefore, we begin with deriving a closed-form secrecy rate expression for the corresponding wiretap channel, and adopt secrecy rate as the performance measure. Then, we formulate a linear programming problem to maximize the secrecy rate when the eavesdropper's channel is accurately known to the jammer. Finally, we consider robust beamforming to maximize the worst-case secrecy rate when information about the eavesdropper's channel is uncertain due to location uncertainty. The robust scheme makes use of simple linear programming, making real-time implementation feasible in a variety of real-world scenarios.
This paper analyzes the secrecy rate and designs the secure beamforming for an indoor visible light communication (VLC) network consisting of one transmitter, one legitimate receiver, one eavesdropper, and several friendly jammers. In the VLC network, both the main and wiretapping channels have a uniformly distributed signal input and corrupted with additive white Gaussian noise. The imperfect channel state information (CSI) is considered by using the ellipsoidal approximation method. Under the amplitude constraints of the data and the interfering signal, the secrecy rate for the VLC network is analyzed. A beamforming optimization problem is proposed to maximize the secrecy rate with the given constraints. It is shown that the optimization problem is non-convex. In this paper, it is transformed into a solvable quasi-convex linear search problem. Numerical results demonstrate the performance gain of the optimal beamformer compared to the zero-forcing scheme. Moreover, the secrecy performance under different CSI uncertainties is also provided.
Visible light communication (VLC) is a technology that uses unlicensed light spectrum resources and high spatial reuse rates for communication. Because it does not occupy any resource allocation in wireless communication, it has fully alleviated the problem of spectrum scarcity in radio frequency (RF) communication and gradually become a new development direction. However, owing to the inherent broadcasting nature of the VLC channel, the VLC link is vulnerable to eavesdropping by unexpected or unauthorized users in spacious public places. Therefore, enhancing the security of the VLC system has attracted extensive attention. This paper studies the security optimization scheme of the VLC system based on the space shift keying (SSK) technology in the free space optical environment called the SSK-VLC system. The antenna selection (AS) technology and artificial noise (AN) cancellation method are adopted to enhance the confidentiality of the SSK-VLC system. In this paper, we presume that the SSK-VLC system includes three parts: a transmitter containing multiple light-emitting diodes, a legitimate receiver, and an eavesdropper, respectively. By using the designed AS and AN method, the transmitted valid information can be demodulated at the legitimate receiver, and at the same time, the received information by the eavesdropper will be disturbed. The simulation results prove that the proposed optimization scheme can further improve the security performance, including the secrecy rate (SR) and the bit error ratio (BER), compared with the traditional SSK-VLC scheme.
Visible light communication (VLC) is a burgeoning field in wireless communications as it considers illumination and communication simultaneously. The broadcast nature of VLC makes it necessary to consider the security of underlying transmissions. A physical-layer security (PLS) scheme by introducing jamming LEDs is considered in this paper. The secrecy rate of an indoor VLC system with multiple LEDs, one legitimate receiver, and multiple eavesdroppers is investigated. Three distributions of input signal are assumed, i.e., truncated generalized normal distribution (TGN), uniform distribution, and exponential distribution. The results show that jamming can improve the secrecy performance efficiently. This paper also demonstrates that when the numbers of LEDs transmitting information-bearing signal and jamming signal are equal, the average secrecy rate can be maximized.