Sixth-Generation (6G) networks will require massive connectivity, ultra-low latency, and robust security, making reliable Active User Detection (AUD) essential for interference control and physical layer protection. This letter proposes a Grover-based physical layer security (PLS) framework for a code-domain non-orthogonal multiple access (CD-NOMA) network, where the base station employs artificial-noise (AN)-assisted beamforming and identifies the active set via Grover's quantum search algorithm. We consider two threat models: passive eavesdroppers formed by detected inactive users, and active eavesdroppers selected as the top f% most frequent transmitters among detected active users. By aligning beams and AN with the Grover-based AUD output, the proposed scheme enlarges the main-wiretap rate gap and significantly improves the average secrecy rate compared with compressive sensing and classical correlation receiver baselines, while approaching maximum-likelihood detection performance with a quadratic reduction in search complexity. The impact of the information/AN power split, the base station transmit power, and the fraction of highly active users treated as eavesdroppers on secrecy is characterized through numerical simulations, and design insights are extracted for 6G PLS under both passive and active eavesdropping.
Sixth-Generation (6G) networks will require massive connectivity, ultra-low latency, and robust security, making reliable Active User Detection (AUD) essential for interference control and physical layer protection. This letter proposes a Grover-based physical layer security (PLS) framework for a code-domain non-orthogonal multiple access (CD-NOMA) network, where the base station employs artificial-noise (AN)-assisted beamforming and identifies the active set via Grover's quantum search algorithm. We consider two threat models: passive eavesdroppers formed by detected inactive users, and active eavesdroppers selected as the top f% most frequent transmitters among detected active users. By aligning beams and AN with the Grover-based AUD output, the proposed scheme enlarges the main-wiretap rate gap and significantly improves the average secrecy rate compared with compressive sensing and classical correlation receiver baselines, while approaching maximum-likelihood detection performance with a quadratic reduction in search complexity. The impact of the information/AN power split, the base station transmit power, and the fraction of highly active users treated as eavesdroppers on secrecy is characterized through numerical simulations, and design insights are extracted for 6G PLS under both passive and active eavesdropping.
The strong growth in the number of connected mobile devices has imposed new challenges in efficiently exploiting the available networks resources. Code Domain Non-Orthogonal Multiple Access (NOMA) technique appears as a tremendous efficient solution. Each device uses its assigned code to simultaneously transmit its data along with the user identifier, without any resource reservation exchange, saving precious wireless resources. However, this requires a receiver capable of blindly detecting the active users, which is highly complex. Driven by the promising superposition property of quantum architecture, the goal of this paper is to adapt and apply the quantum Grover algorithm for Active User Detection (AUD) purpose in the context of NOMA, to alleviate the search complexity. This adapted Grover’s algorithm is compared with the optimal classical Maximum Likelihood (ML) AUD receivers, as well as with the basic classical Conventional Correlation Receiver (CCR). A benchmark on the probability of AUD is assessed as a function of the Signal to Noise Ratio (SNR) of the received signal. We show that our adapted Grover’s algorithm is very promising in high SNR regime.
NOMA (Non-Orthogonal Multiple Access) has been recognized as new multiple access technology for wireless communication networks of the fifth generation and beyond, owing to its ability to improve spectral efficiency, support massive connectivity, and provide user fairness. However, it has been observed that the superposition-based transmission principle of NOMA is highly susceptible to eavesdropping attacks, especially in an open wireless network where all wireless communications are inherently broadcast in nature. Hence, ensuring secure wireless communications for NOMA systems has now become an important area of research. This research aims to improve the secrecy level of wireless communications for NOMA systems by employing Physical Layer Security (PLS) techniques. Specifically, two prominent PLS techniques, Artificial Noise (AN) and Cooperative Jamming (CJ), have been employed to provide security against passive eavesdropping attacks. The cooperative NOMA system architecture is considered, and the secrecy enhancement is analyzed for two possible scenarios when the eavesdropper is close to the relay node and when the eavesdropper is close to the base station. The secrecy performance of the proposed system is analyzed through various parameters, namely, Secrecy Outage Probability (SOP), Secrecy Capacity (SC), Secrecy Rate (SR), and Secrecy Energy Efficiency (SEE) in terms of the Signal to Noise Ratio (SNR). The numerical results for the secrecy metrics are obtained through MATLAB-based simulations, whereas the system-level model is developed using the Simulink platform to verify the suggested system's functionality in the presence of AN and CJ for the legitimate NOMA users and the eavesdropper. The results show that the secrecy performance is significantly enhanced with the introduction of AN and CJ, and the system shows robust performance in terms of energy efficiency. This paper provides significant insights into the design of the NOMA system from the secrecy perspective and validates the potential of PLS techniques for the design of the future wireless system.
In the sixth-generation (6G) communications, how to deploy and manage massively connected Internet-of-Things (IoT) nodes will be one of the key technical challenges, because 6G is expected to provide 10 times higher connection, compared to 5G. At the same time, due to the sharp growth in connected devices and newly adopted technologies, learning-based attacks and big data breaches are expected to occur more frequently. With the advances of quantum computing in the future, conventional cryptography-based security protocols may be obsolete in the future wireless networks, which makes physical layer security (PLS) an attractive alternative or complement for secure communications. In this context, cooperative beamforming (CB)-based PLS schemes are known to be effective solutions to guarantee high secrecy rate with IoT devices, which have limited power and hardware complexity. However, the existing CB-based PLS algorithms suffer from extremely low secrecy rate, in case that eavesdroppers are close to the intended receiver. To overcome such critical issue, in this paper, we propose a CB-based PLS with artificial noise (AN) injection, which can be realized in a fully distributed manner to minimize the overhead in the IoT networks with a large number of devices. We analyze the array factor using the virtual antenna array (VAA) created by the proposed PLS algorithm. Then, the secrecy rate is derived in a closed-form expression, which can be used to optimize the performance for given system parameters in both the absence and presence of channel state information (CSI) error. The proposed scheme provides considerably higher secrecy rate compared to the conventional CB-based PLS schemes, when an eavesdropper exists near to the intended receiver. Furthermore, through simulation and numerical results, we show that the secrecy rate of the proposed scheme can be maximized by adjusting the ratio between the data beamformer and AN injection beamformer components. As a result, the proposed method shows a performance improvement of up to two times compared to the conventional CB-based PLS schemes, in terms of the secrecy rate. Such performance gain increases as the angular location of Eve becomes closer to that of Bob, which corresponds to the most vulnerable situation of the conventional CB-based PLS algorithms.
Industry 5.0 introduces human-machine collaboration and resilient automation, demanding secure, low-latency connectivity for ultra-dense Industrial IoT (IIoT). Grant-free massive machine-type communications (mMTC) supports such connectivity but faces challenges including dense multiuser access, passive eavesdropping, and imperfect channel state information (CSI), which undermine physical layer security (PLS). This paper proposes a robust and low-complexity multiuser PLS scheme tailored for grant-free mMTC under CSI uncertainty. The scheme leverages dynamic user clustering based on spatial correlation and real-time interference to enable scalable, interference-aware beamforming. Furthermore, a joint optimisation of receive beamforming and adaptive artificial noise injection is performed, and enhanced by a regularised minimum mean square error (MMSE) framework to mitigate bounded CSI errors. Simulation results show that the scheme consistently outperforms existing benchmarks across secrecy capacity, bit error rate, and secrecy outage probability under different channel models, together with analyses of SOP sensitivity to CSI error and scalability to dense users/eavesdroppers, confirms its robustness, efficiency, and applicability to large-scale, secure IIoT communications in beyond 5G/6G networks aligned with Industry 5.0 requirements.
Sixth-generation (6G) networks must deliver ultra-low latency and near-100 percent reliability to support massive-scale Internet of Things (IoT) deployments and Hyper-Reliable Low-Latency Communications (HRLLC). Grant-free access protocols permit devices to transmit without prior scheduling; nevertheless, this uncoordinated transmission introduces uncertainty at the receiver, necessitating Active User Detection (AUD) to ascertain which devices are active. Quantum search methods—most notably Grover’s algorithm—can accelerate AUD, yet they require knowing the optimal number of iterations, which depends on the (typically unknown and time-varying) number of valid solutions induced by the current activity pattern and channel/noise conditions. To overcome this, we formulate an optimization problem that optimizes the number of Grover iterations to maximize detection accuracy and minimize computational cost without any prior activity information. We then apply a Deep Q-Network (DQN) to learn, via deep reinforcement learning, an adaptive policy for selecting the iteration count. Simulation results verify that the DQN converges to an optimal strategy and outperforms two baseline schemes under varying fading conditions and active-user transmit powers.
Physical layer security (PLS) has grown in popularity as it guarantees secure and reliable communication between wireless devices without restricting the ability of data analysts to eavesdrop. PLS benefits from the randomization property of the wireless channel, which improves immunity and protects against many dangers of information breaches from eavesdroppers. MIMO and Massive MIMO, which use numerous antennas at the transmitter and receiver ends, are fundamental technologies for improving efficiency in 5G and future network systems. The PLS still requires a significant amount of work before it can be employed in practical systems. The interception of eavesdroppers can be made difficult by adding artificial noise. This paper focuses on the physical layer security considering maximal ratio combining, and artificial noise with beamforming techniques to further enhance the secrecy rate. The proposed work demonstrates that the use of transmitter beamforming with artificial noise can significantly improve the degree of confidentiality of information being transmitted between a transmitter and an intended receiver while making it more difficult for eavesdroppers to intercept. The simulation results in terms of bit error rate show that the proposed method has a significant impact on the error rate performance of the eavesdropper.