Developing wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface (IRS)-enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). The IRS facilitates secure transmission for the primary transmitter (PT) by intelligently adjusting the phase shifts of the signals from the PT, while simultaneously transmitting its own data to an Internet of Things (IoT) device. In light of the existence of numerous eves and unpredictable channels in real-world situations, we concurrently optimize the active beamforming of the PT and the phase shifts of the IRS to enhance the secrecy of IRS-assisted secure relay networks while adhering to quality-of-service standards and secure communication rates. To address this intricate non-convex stochastic optimization issue, we propose a secure beamforming technique named DDPG-SR, utilizing an effective deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) scheme to determine the optimal beamforming approach against Eves. This method seeks to establish an optimal beamforming strategy to counteract Eves under dynamic environmental circumstances. Comprehensive simulation experiments confirm the effectiveness of our proposed solution, showcasing enhanced performance relative to conventional IRS methods, IRS backscattering-based anti-evesdropping techniques, and other benchmark tactics for secrecy performance.

The rapid evolution of 6G networks demands innovative solutions to address the dual challenges of ensuring robust physical layer security (PLS) and optimizing energy efficiency. This paper introduces an innovative framework based on deep reinforcement learning (DRL) for enhancing security and energy efficiency in terahertz (THz) communication within intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) systems. Utilizing the deep deterministic policy gradient (DDPG) algorithm, we introduce a two-stage policy learning approach designed to optimize secrecy energy efficiency (SEE) while ensuring secure communication, even in the presence of multiple eavesdroppers (Eves). The first stage focuses on optimizing the secrecy rate (SR) by dynamically adjusting IRS phase shifts and transmit power allocation, ensuring secure communication links. The second stage refines the policy to minimize energy consumption without compromising the achieved security. Our framework addresses the challenges of imperfect channel state information (CSI) and inherent uncertainties in THz communication, making it highly adaptable to real-world scenarios. Through extensive simulations, we demonstrate that the proposed DDPG-based approach outperforms traditional methods such as advantage actor-critic (A2C) and alternating optimization (AO) in terms of convergence speed, stability, and final SEE performance. The results highlight the effectiveness of IRS in enhancing signal reflection and coverage, significantly improving both security and energy efficiency in THz-based 6G networks. This work provides a scalable and sustainable solution for next-generation wireless communication systems, paving the way for secure, energy-efficient, and high-capacity 6G networks.

Cell-free massive multiple-input–multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing–communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO– ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas, orthogonal time–frequency space (OTFS) modulation, and unmanned aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate–sensing trade-off, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing–communication design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

In order to improve the security aspects of a nonorthogonal multiple access (NOMA) downlink network, we investigate the use of the proximal policy optimization (PPO) technique in this study. This network is uniquely augmented with reconfi- gurable intelligent surfaces (RIS) mounted on unmanned aerial vehicles (UAVs). The main objective of this article is to prevent possible eavesdroppers from accessing the network while still assuring continuous and secure connectivity for legitimate users. The network configuration includes a UAV outfitted with a RIS, which plays an important role in signal propagation. This work aims to increase the secrecy rates of all user communications in eavesdropping prone locations. By adaptively modifying the phase shifts and power distributions through the RIS the proposed PPO algorithm not only shows adaptability and effectiveness in securing wireless communications, but also highlights the significant advances made in communication system security through this technology.

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

Efficient decentralized power management is crucial for enhancing the reliability, resilience, responsiveness, and sustainability of secondary power distribution systems, thereby preventing major power outages and providing rapid responses. However, existing secondary power distribution networks are prone to failures, thus compromising their operational trustworthiness and efficiency. This work proposes an intelligent, decentralized control system with distributed processing capabilities. The proposed system is designed to automate fault detection and rectification along with optimized power management at secondary distribution nodes. The system enables rapid fault detection (line-to-line, line-to-ground, and overload) and initiates a fault-based response to isolate the load through controlled relays. Additionally, an intelligent power management system automatically rectifies surge faults (short-lived faults) and reports non-surge faults (persistent faults) to the control center. It continuously updates the status of real-time power parameters to the database using a Global System for Mobile Communications (GSM)-based communication system with a frequency of 60 s per sample for power management. The Proteus-based simulation and a scaled-down model validate the efficiency and supremacy of the proposed system over the existing control system for power distribution nodes. The results demonstrate that our model detects critical faults and initiates the response within 100 and 200 milliseconds, respectively. Surge faults are automatically rectified within 90 s, while non-surge faults are reported to the database after 90 s. This approach significantly reduces downtime, enables energy accountability, and supports sustainable energy management through a decentralized and distributed control system.