Non-orthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems are combined with relay and reconfigurable intelligent surfaces (RIS). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this paper, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.

This paper investigates the integration of fluid antennas (FAs) into uplink non-orthogonal multiple access networks suffering from imperfect successive interference cancellation (SIC). The dynamic reconfigurability of FAs offers significant potential for mitigating interference and enhancing network performance by adapting antenna positions in response to changing channel conditions. In this study, we propose a joint optimization framework to maximize the system's sum rate by optimizing key parameters, including FA positions, beamforming vector at the base station, and transmit power allocation for each user. The problem is formulated as a non-convex optimization task and solved using a new deep reinforcement learning (DRL)-based framework. The proposed DRL model incorporates a structured exploration strategy and reward shaping to efficiently learn optimal policies for resource allocation and antenna positioning in dynamic environments. Extensive simulations validate the effectiveness of the proposed approach, demonstrating that integrating FAs significantly improves the sum rate, particularly in scenarios with imperfect SIC.

This paper proposes an innovative approach to improve over-the-air federated learning (OTA-FL) systems by integrating fluid antennas (FAs) at the access point. By exploiting the mobility of FAs, we aim to increase the correlation among the users’ channels, thereby improving the learning performance. We analyze the performance of over-the-air computation and the convergence behavior of the OTA-FL system, highlighting the benefits of FAs. Since the learning performance improves as more devices participate in the FL aggregation, we formulate a non-convex optimization problem that maximizes the number of selected users by jointly optimizing FA positions and the beamforming vector, coupled with a user selection policy subject to a mean-squared error constraint. To address environmental dynamics, we describe the problem as a Markov decision process and develop a long short-term memory (LSTM)-based algorithm for efficient decision-making. Simulation results demonstrate that the proposed FA-assisted OTA-FL framework significantly outperforms conventional setups, achieving higher user selection rates and improved learning performance compared to existing benchmarks.

This article investigates the performance of physical-layer security (PLS) in wireless communication systems, where a reconfigurable intelligent surfaces (RISs) is deployed between the transmitter and legitimate receiver to enhance the communication security. The Fisher-Snedecor $\mathcal {F}$ distribution is adopted to model the underlying fading channels, owing to its accuracy, tractability and generality. On this basis, this article evaluates the performance of the proposed system by deriving the average secrecy capacity (ASC) and the secrecy outage probability (SOP) under correlated Fisher-Snedecor $\mathcal {F}$ channel coefficients. Furthermore, the asymptotic behavior of the ASC and SOP in the high- signal-to-noise ratio (SNR) regime is examined. Analyzing the correlated scenario is crucial as it provides a detailed understanding of how interdependencies among channel coefficients impact the system’s security and overall performance, offering valuable insights into real-world communication scenarios. Finally, this article verifies the analytical results through numerical illustrations, and demonstrates the effectiveness of employing RIS.

To address spectrum scarcity and support diverse services, including communication and learning tasks, this paper proposes a novel framework that enables the coexistence of over-the-air federated learning (OTA-FL) users with communication users within a reconfigurable intelligent surface (RIS)-aided cognitive radio network (CRN). The system leverages RIS technology to dynamically adjust the channel environment, thereby mitigating interference among heterogeneous data streams. We derive a closed-form expression to quantify the optimality gap between the actual federated learning (FL) model and the theoretical optimal FL model under varying interference levels in uplink communications. To standardize performance metrics across different user types, we define the achievable computation rate for model aggregation in each OTA-FL training round. The study formulates a hybrid rate maximization problem, involving the joint optimization of transmit power for primary user equipment, multiple secondary user equipment, RIS phase shifts, and the denoising factor at the secondary access point. This complex non-convex problem is reformulated as a Markov decision process and solved using deep reinforcement learning (DRL) approaches, specifically the multi-agent deep deterministic policy gradient (MADDPG). Numerical results validate the MADDPG, demonstrating the effectiveness of the DRL-enhanced RIS-aided hybrid CRN in supporting concurrent uplink transmissions.

This paper considers a multi-user system, where the users first harvest energy from the base station and then use the harvested energy to transmit information via non-orthogonal multiple access (NOMA). A pinching-antenna array is adopted to assist the energy transfer and information transmission, owing to its ability to adapt to dynamic propagation conditions. To enhance the system's energy efficiency (EE), we formulate a joint optimization problem involving antenna positioning, transmit power control, and time-switching ratio selection. The problem is non-convex due to the coupled variables, nonlinear energy-harvesting characteristics, and uncertainties in user locations and battery states. To effectively solve this problem, a deep reinforcement learning based algorithm is proposed to autonomously learn near-optimal resource allocation policies in dynamic environments. Simulation results demonstrate that the proposed PA-assisted scheme achieves significant gains in EE compared with conventional fixed-antenna schemes.