Pinching Antenna System-Assisted Hybrid AirComp-NOMA Uplink: Joint Precoding and Antenna Placement Optimization

This paper studies a pinching antenna system (PAS)-assisted hybrid uplink architecture that integrates over-the-air computation (AirComp) and non-orthogonal multiple access (NOMA) to simultaneously support distributed data aggregation and individual communication services. A base station with a dielectric waveguide hosting multiple pinching antennas receives signals from AirComp and NOMA users over shared time-frequency resources. To assess joint computation-communication performance, a hybrid metric combining the AirComp computation rate and the NOMA sum rate is proposed. Based on this metric, a joint optimization problem is formulated to maximize the hybrid rate by optimizing user transmit precoding, receive combining, and antenna deployment, subject to power, quality-of-service, and aggregation accuracy constraints. An alternating optimization framework is developed to solve the resulting non-convex problem. Numerical results show that the proposed design achieves significant performance gains over several benchmark schemes.

Secrecy Performance Analysis of Pinching-Antenna Systems Under Pinching-Position Uncertainty

This paper investigates the secrecy performance of pinching-antenna systems (PAS) under practical pinching-position activation uncertainty. By dynamically selecting the radiation point along a dielectric waveguide, PAS enables low-cost spatial reconfigurability and enhanced secure transmission. Unlike existing studies that assume ideal activation control, we account for spatial inaccuracies caused by hardware limitations and environmental perturbations, which induce statistical dependence between the legitimate and eavesdropping channels. To capture this effect, a copula-based framework is employed to model the joint distribution of the corresponding signal-to-noise ratios (SNRs), and approximate expressions for the secrecy outage probability (SOP) are derived. Simulation results validate the theoretical findings and demonstrate that PAS retains robust secrecy performance compared with conventional fixed-antenna systems, even in the presence of activation uncertainty.

AI-Empowered Resource Allocation for Wirelessly Powered Pinching-Antenna Systems

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.

Fluid Antenna Systems under Channel Uncertainty and Hardware Impairments: Trends, Challenges, and Future Research Directions

Fluid antenna systems (FAS) have recently emerged as a promising paradigm for achieving spatially reconfigurable, compact, and energy-efficient wireless communications in beyond fifth-generation (B5G) and sixth-generation (6G) networks. By dynamically repositioning a liquid-based radiating element within a confined physical structure, FAS can exploit spatial diversity without relying on multiple fixed antenna elements. This spatial mobility provides a new degree of freedom for mitigating channel fading and interference, while maintaining low hardware complexity and power consumption. However, the performance of FAS in realistic deployments is strongly affected by channel uncertainty, hardware nonidealities, and mechanical constraints, all of which can substantially deviate from idealized analytical assumptions. This paper presents a comprehensive survey of the operation and design of FAS under such practical considerations. Key aspects include the characterization of spatio-temporal channel uncertainty, analysis of hardware and mechanical impairments such as RF nonlinearity, port coupling, and fluid response delay, as well as the exploration of robust design and learning-based control strategies to enhance system reliability. Finally, open research directions are identified, aiming to guide future developments toward robust, adaptive, and cross-domain FAS design for next-generation wireless networks.

Robust Resource Allocation for Over-the-Air Computation Networks with Fluid Antenna Array

Fluid antenna (FA) array is envisioned as a promising technology for next-generation communication systems, owing to its ability to dynamically control the antenna locations. In this paper, we apply FA array to boost the performance of over-the-air computation networks. Given that channel uncertainty will impact negatively not only the beamforming design but also the antenna location optimization, robust resource allocation is performed to minimize the mean squared error of transmitted messages. Block coordinate descent is adopted to decompose the formulated non-convex problem into three subproblems, which are iteratively solved until convergence. Numerical results show the benefits of FA array and the necessity of robust resource allocation under channel uncertainty.

Enhancement of Over-the-Air Federated Learning by Using AI-based Fluid Antenna System

This letter investigates an over-the-air federated learning (OTA-FL) system that employs fluid antennas (FAs) at the access point (AP) to enhance learning performance by leveraging the additional degrees of freedom provided by antenna mobility. First, we analyze the convergence of the OTA-FL system and derive the optimality gap to illustrate the influence of FAs on learning performance. Then, we formulate a nonconvex optimization problem to minimize the optimality gap by jointly optimizing the positions of the FAs and the beamforming vector. To address the dynamic environment, we cast this optimization problem as a Markov decision process (MDP) and propose the recurrent deep deterministic policy gradient (RDPG) algorithm. Finally, extensive simulations show that the FA-assisted OTA-FL system outperforms systems with fixed-position antennas and that the RDPG algorithm surpasses the existing methods.

Physical Layer Security for NOMA Systems: Requirements, Issues, and Recommendations

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 view-point, 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.