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IEEE Wireless Communications Letters

Enhancing Secrecy Capacity With Non-Orthogonal Artificial Noise Based on Pilot Information Codebook

Yebo Gu, Tao Shen, Jian Song, Qingbo Wang

Published Jan 29, 2024• 10 citations
Publisher

Abstract

Contemporary research has highlighted that the design possibilities for Orthogonal Artificial Noise (AN) are circumscribed by the inherent limitations in channel degrees of freedom, yielding a scant assortment of design alternatives. In this letter, Non-Orthogonal Artificial Noise (NORAN) emerges as a viable substitute. Its applications have been recognized in relay communication systems such as those involving Unmanned Aerial Vehicles (UAVs) and Intelligent Reflecting Surfaces (IRS). Despite this, the mathematical exploration of the Secrecy Capacity (SC) function within wireless communication systems incorporating NORAN has been scant. This letter pioneers the examination of the concave-convex nature of the SC function in such systems. The findings reveal that, subject to certain transmit power conditions, the SC function for NORAN is inherently convex, implying a decrement in SC upon NORAN’s deployment, which negates its intended effect. In response to this conundrum, this discourse introduces Pilot-based Codebook Non-Orthogonal Artificial Noise (PCAN), wherein the codebook capitalizes on pilot signals exchanged between the receiver and sender as an encryption key. The receiver, with the aid of the pilot signals, retrieves the forthcoming PCAN from the codebook, thereby neutralizing the PCAN’s influence during the reception of information. This innovation equates the SC functions of PCAN and AN within the same channel framework. Additionally, this study delves into the optimization conundrum of PCAN, considering scenarios of both perfect and imperfect Channel State Information (CSI). Through theoretical analysis, it is established that with an ideal CSI and an signal-to-noise ratio (SNR) surpassing zero, the quintessential power allocation for PCAN equates to half of the total transmission power. Contrariwise, under imperfect CSI parameters, PCAN fails to deliver efficacy.

DOI
10.1109/lwc.2024.3357652

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Contemporary research has highlighted that the design possibilities for Orthogonal Artificial Noise (AN) are circumscribed by the inherent limitations in channel degrees of freedom, yielding a scant assortment of design alternatives. In this letter, Non-Orthogonal Artificial Noise (NORAN) emerges as a viable substitute. Its applications have been recognized in relay communication systems such as those involving Unmanned Aerial Vehicles (UAVs) and Intelligent Reflecting Surfaces (IRS). Despite this, the mathematical exploration of the Secrecy Capacity (SC) function within wireless communication systems incorporating NORAN has been scant. This letter pioneers the examination of the concave-convex nature of the SC function in such systems. The findings reveal that, subject to certain transmit power conditions, the SC function for NORAN is inherently convex, implying a decrement in SC upon NORAN’s deployment, which negates its intended effect. In response to this conundrum, this discourse introduces Pilot-based Codebook Non-Orthogonal Artificial Noise (PCAN), wherein the codebook capitalizes on pilot signals exchanged between the receiver and sender as an encryption key. The receiver, with the aid of the pilot signals, retrieves the forthcoming PCAN from the codebook, thereby neutralizing the PCAN’s influence during the reception of information. This innovation equates the SC functions of PCAN and AN within the same channel framework. Additionally, this study delves into the optimization conundrum of PCAN, considering scenarios of both perfect and imperfect Channel State Information (CSI). Through theoretical analysis, it is established that with an ideal CSI and an signal-to-noise ratio (SNR) surpassing zero, the quintessential power allocation for PCAN equates to half of the total transmission power. Contrariwise, under imperfect CSI parameters, PCAN fails to deliver efficacy.
Non-Orthogonal Artificial Noise (NORAN) has garnered significant attention in modern communication systems as a substitute for traditional Artificial Noise (AN), due to its independence from channel degrees of freedom. However, the design of NORAN leads to a reduction in the channel capacity of legitimate channels, thereby limiting its application in practical systems. Pilot-based Codebook Non-Orthogonal Artificial Noise (PCAN) leverages pilot signals exchanged between the receiver and transmitter as encryption keys. This allows the receiver to retrieve the upcoming PCAN from the codebook via the pilot signals, effectively transitioning from NORAN to AN in form, demonstrating high adaptability. Consequently, this paper investigates the deployment of PCAN in Massive MIMO systems to achieve secure communication. Considering the eavesdropper as an illegitimate user, whose channel state information matrix is unknown to the legitimate receiver and does not follow a simple distribution pattern, this study employs random matrix theory to model the channel of an unknown eavesdropper. It uses the successive convex approximation algorithm to analyze the optimal power allocation strategy and the maximum number of eavesdropper antennas that the system can tolerate under these conditions. The findings indicate that as the number of eavesdropper antennas increases, the eavesdropper channel modeling based on random matrix theory increasingly approximates the actual channel characteristics of the eavesdropper. Notably, when the number of eavesdropper antennasNE=128N_{E} = 128NE​=128, the consistency between the modeled channel and the actual channel reaches 87.2%, and the optimal power allocation factor at this point isθ=0.5\theta = 0.5θ=0.5. However, as the number of eavesdropper antennas continues to increase, the effectiveness of PCAN becomes more pronounced. Nevertheless, when the number of eavesdropper antennas approaches infinity, the efficacy of PCAN significantly decreases, failing to adequately ensure the system’s security.
In recent research, non-orthogonal artificial noise (NORAN) has been proposed as an alternative to orthogonal artificial noise (AN). However, NORAN introduces additional noise into the channel, which reduces the capacity of the legitimate channel (LC). At the same time, selecting a NORAN design with ideal security performance from a large number of design options is also a challenging problem. To address these two issues, a novel NORAN based on a pilot information codebook is proposed in this letter. The codebook associates different suboptimal NORANs with pilot information as the key under different channel state information (CSI). The receiver interrogates the codebook using the pilot information to obtain the NORAN that the transmitter will transmit in the next moment, in order to eliminate the NORAN when receiving information. Therefore, NORAN based on pilot information codebooks can improve the secrecy capacity (SC) of the communication system by directly using suboptimal NORAN design schemes without increasing the noise in the LC. Numerical simulations and analyses show that the introduction of NORAN with a novel design using pilot information codebooks significantly enhances the security and improves the SC of the communication system.
This letter investigates the use of pilot-based codebook artificial noise (PCAN) in Integrated Sensing and Communication (ISAC) systems to enhance physical layer security. ISAC technology integrates communication and sensing through shared resources, emphasizing the need for secure strategies. PCAN allows the receiver to extract information from the pilot codebook, effectively removing interference and increasing secrecy capacity. The non-convex secrecy capacity optimization problem is addressed by decomposing it into simpler subproblems. A semidefinite relaxation (SDR) and alternating optimization algorithm is proposed to maximize secrecy capacity while maintaining sensing accuracy. Simulation results show significant improvements, particularly at low and moderate Signal-to-Noise Ratio (SNR), with consistent gains at high SNRs. This work underscores the role of optimization techniques in enhancing secure communication and balancing secrecy with sensing performance in ISAC systems.
Pilot-Based Codebook Artificial Noise (PCAN), which plays a crucial role in ensuring secure communication, is an emerging physical layer security technique. PCAN faces a significant challenge due to the unknown eavesdropper channel. To address this issue, this letter re-models the eavesdropper channel using random matrix theory. By applying the$\eta $-transform and Shannon-transform, the asymptotic expression for the eavesdropper channel capacity is derived under the condition of an unknown eavesdropper channel. The simulation result shows that when the number of transmitter antennas equals the number of eavesdropper antennas and the system dimension is large, the model can effectively characterize the eavesdropper channel capacity. Within this framework, the secrecy capacity is optimized using the Difference of Convex functions - Successive Convex Approximation (DC-SCA) algorithm, providing a new approach to improve the performance of PCAN.
The broadcast nature of wireless channels introduces significant security vulnerabilities in information transmission, particularly when the eavesdropper is close to the legitimate destination. In such scenarios, the eavesdropping channel often exhibits high spatial correlation with, or even superior quality to, the legitimate channel. This makes it challenging for traditional power optimization methods to effectively suppress the eavesdropping rate. To address this challenge, this paper proposes an optimization method for the secrecy capacity of unmanned aerial vehicle (UAV) relaying based on the dynamic adjustment of the power allocation factor. By injecting artificial noise (AN) during signal forwarding and combining it with real-time channel state information, the power allocation factor can be dynamically adjusted to achieve precise jamming of the eavesdropping link. We consider a four-node communication model consisting of a source, a UAV, a legitimate destination, and a passive eavesdropper, and formulate a joint optimization problem to maximize the secrecy rate. Due to the non-convexity of the original problem, we introduce relaxation variables and apply successive convex approximation (SCA) to reformulate it into an equivalent convex optimization problem. An analytical solution for the power allocation factor is derived using the water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm with AN assistance is proposed to achieve global optimization of the system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm substantially suppresses the eavesdropping channel capacity while enhancing transmission efficiency, thereby significantly improving both secrecy performance and overall communication reliability.
The broadcast nature of wireless channels introduces significant security risks during information transmission. When the eavesdropper is in close proximity to the legitimate receiver, their channel state information (CSI) may be highly correlated or even superior to that of the legitimate channel, rendering traditional power optimization methods ineffective in suppressing eavesdropping rates. To address this issue, this paper proposes an artificial noise (AN)-assisted unmanned aerial vehicle (UAV) relay system. By leveraging the UAV relay to inject artificial noise while transmitting signals and dynamically adjusting the power allocation factor based on real-time CSI and the UAV's position, precise interference control over the eavesdropping channel is achieved. This study establishes a fournode communication system comprising a source, relay, legitimate receiver, and eavesdropper, formulating a joint optimization problem aimed at maximizing secrecy capacity. Given the nonconvex nature of the optimization problem, this paper employs slack variables and the successive convex approximation (SCA) method to equivalently transform the original problem into a convex problem. A closed-form analytical solution for the power allocation factor is derived using the staircase water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm based on artificial noise assistance is proposed to achieve global optimization of system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm reduces the eavesdropping capacity and improves the information transmission efficiency, significantly enhancing both system security and transmission performance.