Despite the narrowbeam nature of Terahertz (THz) communications, the physical layer security in the THz band is challenging when eavesdroppers are inside the beam radiation sector. In this paper, a deep neural network (DNN)-based self-interference cancellation (SIC)-free receiver artificial noise (AN) assisted mechanism is proposed to address the in-beam security challenge, by considering randomly distributed eavesdroppers in THz secure communications. By exploiting the different temporal broadening effects of the AN signals at distinct distances, the SIC can be saved with a proper signal detection design rather than using conventional high-complexity cancellation techniques. To combat the non-linearity and non-convexity of the optimization problem, the system parameters including carrier frequency, power of transmission signal and AN signal power, and frame time are designed by an efficient deep neural network (DNN) algorithm to minimize the secrecy outage probability. Numerical results demonstrate that the maximum secrecy rate of our proposed DNN-powered SIC-free receiver AN scheme is up to 3.3 bps/Hz over 10 m transmission when the eavesdropper is in close proximity. Moreover, the secrecy outage probability is less than 0.5% when the eavesdropper density is 0.001 per square meter, which is approximately 33% lower than that of conventional transmitter AN schemes.
Despite the narrowbeam nature of Terahertz (THz) communications, the physical layer security in the THz band is challenging when eavesdroppers are inside the beam radiation sector. In this paper, a deep neural network (DNN)-based self-interference cancellation (SIC)-free receiver artificial noise (AN) assisted mechanism is proposed to address the in-beam security challenge, by considering randomly distributed eavesdroppers in THz secure communications. By exploiting the different temporal broadening effects of the AN signals at distinct distances, the SIC can be saved with a proper signal detection design rather than using conventional high-complexity cancellation techniques. To combat the non-linearity and non-convexity of the optimization problem, the system parameters including carrier frequency, power of transmission signal and AN signal power, and frame time are designed by an efficient deep neural network (DNN) algorithm to minimize the secrecy outage probability. Numerical results demonstrate that the maximum secrecy rate of our proposed DNN-powered SIC-free receiver AN scheme is up to 3.3 bps/Hz over 10 m transmission when the eavesdropper is in close proximity. Moreover, the secrecy outage probability is less than 0.5% when the eavesdropper density is 0.001 per square meter, which is approximately 33% lower than that of conventional transmitter AN schemes.
In this chapter, we described the fundamental system model, challenges, and technical solutions to THz physical layer security. Despite the good security performance provided by the high directivity of THz wireless communications, THz physical layer security still faces security challenges, including eavesdroppers inside beam sector, eavesdroppers in close proximity and THz covertness problem. In light of these challenges, three THz-specific security schemes were described. First, the DA-APM proposes to transmit signals at sides of molecular absorption peaks to enhance the wireless security and covertness. This scheme effectively combats eavesdroppers inside the beam sector and can even enhance the covertness of THz wireless communications. Second, the SIC-free RAN scheme takes the advantage of the distance-dependent temporal broadening effect to jam Eve in close proximity while not jamming Bob. This method handles the eavesdroppers in close proximity with a lower-complexity receiver AN mechanism. Third, we described the WSA and beamforming scheme to combat near eavesdropping without the aid of other nodes. The size rationality of practical THz WSA is justified.Despite the fruitful research progress on THz physical layer security, there are still open challenges remaining to be tackled, which are described as follows. First, since the optimal molecular absorption coefficients depend on the propagation distance, it is necessary to adjust the carrier frequency as the distance between Alice and Bob varies. How to efficiently and practically tune the carrier frequency to use proper sub-bands needs investigation. Second, the hybrid beamforming structure is usually adopted in THz communications. However, the hybrid beamformer design for the WSA is a non-convex optimization problem. It is necessary to investigate efficient and low-complexity solutions for the WSA beamformer design. Finally, active security attacks, for instance, eavesdroppers may send the jamming signal to the legitimate receiver while overhearing the secret message, need to be handled for THz physical layer security. In line with this, security mechanisms simultaneously combating active and passive eavesdropping are awaited to be explored as a future direction.
The terahertz (THz) band is expected to become a key technology to meet the ever-increasing traffic demand for future 6G wireless communications, and a lot of efforts have been paid to develop its capacity. However, few studies have been concerned with the transmission security of such ultra-high-speed THz wireless links. In this paper, we comprehensively investigate the physical layer security (PLS) of a THz communication system in the presence of multiple eavesdroppers and beam scattering. The method of moments (MoM) was adopted so that the eavesdroppers' channel influenced by the PEC can be characterized. To establish a secure link, the traditional beamforming and artificial noise (AN) beamforming were considered as transmission schemes for comparison. For both schemes, we analyzed their secrecy transmission probability (STP) and ergodic secrecy capacity (ESC) in non-colluding and colluding cases, respectively. Numerical results show that eavesdroppers can indeed degrade the secrecy performance by changing the size or the location of the PEC, while the AN beamforming technique can be an effective candidate to counterbalance this adverse effect.
Physical Layer Security (PLS) for Uplink Non-Orthogonal Multiple Access (UL-NOMA) is investigated, under cooperative multi-eavesdropping. Particular attention is paid to the effect that Successive Interference Cancellation (SIC) decoding order has over the system secrecy rate. Each NOMA user is subject to an individual minimum bit rate requirement, whereas multiple eavesdroppers cooperate among themselves to enhance their repercussion on the system. We demonstrate the impact SIC decoding order has on the secrecy performance, and the importance to look for an optimal solution to the decoding order selection problem. We show how this problem is indeed a very challenging combinatorial one with a factorial complexity related to the number of users and eavesdroppers involved in the system. Given the high computational power demand to find the optimal solution, we approach the challenge in two ways. First, we propose a near-optimal interference-based analytical approach to find a secure decoding order. The idea is to take advantage of the inherent NOMA characteristic of self-interfere other legitimate users, to also act as an interference source over the eavesdroppers. Secondly, we introduce deep learning as another option to achieve a near-optimal solution with a much lower complexity. We see how a deep neural network is able to improve the performance of the interference-based analytical solution, being robuster to users' and eavesdroppers' increment. Finally, we demonstrate the accuracy of the two proposed solutions through numerical simulations and highlight their importance and advantages.
This paper investigates physical layer security (PLS) in wireless interference networks. Specifically, we consider confidential transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob), in the presence of non-colluding passive eavesdroppers (Eves), as well as multiple legitimate transceivers. To mitigate interference at legitimate receivers and enhance PLS, artificial noise (AN) aided interference alignment (IA) is explored. However, the conventional leakage minimization (LM) based IA may exhibit confidential signal cancellation phenomenon. We theoretically analyze the cause and then establish a condition under which this phenomenon will occur almost surely. Moreover, we propose a means of avoiding this phenomenon by integrating the max-eigenmode beamforming (MEB) into the traditional LM based IA. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we derive a closed form expression for the secrecy outage probability (SOP), and establish a condition under which positive secrecy rate is achievable. To enhance security performance, an SOP constrained secrecy rate maximization (SRM) problem is formulated and an efficient numerical method is developed for the optimal solution. Numerical results demonstrate the effectiveness and the usefulness of the proposed approach.
In this letter, we propose a learning-based wireless powered secure transmission, in which a source utilizes energy harvested from a power beacon to communicate with a legitimate receiver, in the presence of an eavesdropper. In order to confuse the eavesdropper, we assume that the source transmits the artificial noise signals, in addition to the information signals. We first characterize the effective secrecy throughput of our system, showing its dependence on the transmission parameters, including the fraction of time allocated for wireless power transfer, the fraction of power allocated to the information signals, as well as the wiretap code rates. We then leverage the deep feedforward neural network to learn how the optimal transmission parameters that jointly maximize the effective secrecy throughput can be obtained. Through numerical results, we demonstrate that our learning-based scheme can achieve almost the same secrecy performance as the optimal solution obtained from the exhaustive search, while requiring much less computational complexity.
Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security (PLS) exploits channel randomness and spatial selectivity to confine information to an intended receiver with modest overhead. However, typical PLS techniques, such as using beamforming, artificial noise, and reconfigurable intelligent surfaces, often involve added active power or specialized deployment, and, in many designs, rely on precise time synchronization and perfect CSI estimation, which limits their practicality. To this end, we propose AmbShield, an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers' without requiring extra transmit power and with minimal deployment overhead. In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices. We further develop a unified analytical framework that analyzes the exact probability density function (PDF) and cumulative distribution function (CDF) of legitimate and eavesdropper signal-to-interference-noise ratio (SINR), and a closed-form secrecy outage probability (SOP). The analysis provides clear design guidelines on various practical system parameters to minimize SOP. Extensive experiments that include Monte Carlo simulations, theoretical derivations, and high-SNR asymptotic analysis demonstrate the security gains of AmbShield across diverse system parameters under imperfect synchronization and CSI estimation.