Distributed RIS-Assisted Terahertz Communications: Performance Analysis and Optimization

On the Performance of Sub-THz/THz NOMA Systems Considering Practical Impairments

The integration of non-orthogonal multiple access (NOMA) with sub-Terahertz and Terahertz (sub-THz/THz) wireless communications can be a key enabler for future 6G networks, offering enhanced spectral efficiency and massive connectivity in highly directional and bandwidth-rich frequency bands. In this work, we investigate the performance of a power-domain downlink terrestrial-to-UAV NOMA system under realistic propagation conditions and hardware impairments. We develop a comprehensive analytical framework to evaluate the outage probability (OP) and average bit error probability (ABEP) of the considered system operating over multi-cluster fluctuating two-ray (MFTR) fading channels, which provide a flexible and physically motivated model for small-scale fading in sub-THz and THz propagation environments. The proposed model jointly accounts for frequency-dependent path loss with molecular absorption, pointing error–induced beam misalignment, and imperfect successive interference cancellation (SIC), while supporting practical square $M$ -QAM modulation. An exact analytically tractable expression is derived for OP and a high-signal-to-noise ratio (SNR) approximation is obtained for ABEP, along with asymptotic results in the high-SNR regime that provide insight into the system’s coding gain and diversity behavior. The analytical results reveal two distinct operating regimes. Under strong-interference conditions, persistent multiuser interference leads to irreducible ABEP floors, whereas in mild-interference scenarios, the ABEP decays with the average SNR according to a power-law behavior, with performance limited by the dominant physical impairment, either multipath fading or beam misalignment. Monte Carlo simulations validate the analysis and highlight key design trade-offs involving power allocation, SIC imperfection, modulation order, beamwidth, carrier frequency, and channel richness.

Geometric Topology-Based Association Strategy in Large-Scale RIS-Assisted THz Networks

Electromagnetic-Communication Joint Modeling and Application for RIS-Assisted Wireless Systems

Current reconfigurable intelligent surface (RIS) modeling approaches typically isolate electromagnetic antenna design from communication system analysis, thereby failing to capture the intricate characteristics or observe real signal dynamics of RIS-assisted communication; consequently, the aperture transit effect cannot be examined neither qualitatively nor quantitatively. To bridge this gap, this article proposes a comprehensive end-to-end simulation framework that jointly models physical RIS antenna behavior and the complete signal processing chain. The proposed model accounts for actual crosstalk between element links, achieving higher fidelity and realism. Validating with quadrature phase-shift keying (QPSK), this study visualizes signal evolution and quantitatively analyzes the impact of the aperture transit effect on system performance. Experiments conducted on a 2 GHz RIS system with a fixed signal-to-noise ratio (SNR) of 10 dB demonstrate that larger array dimensions, higher symbol rates, and wider emission angles significantly exacerbate this effect, leading to severe inter-symbol interference (ISI) and bit error rate (BER) degradation. By characterizing the BER as a function of SNR under various configurations, this work identifies the aperture transit effect as a fundamental source of intrinsic errors, providing essential guidelines for the design and optimization of future wideband RIS-assisted systems.

Fluid Antennas Empowered RIS-Aided Integrated Sensing and Communication Systems

Fluid antennas (FAs) represent a key enabling technology for the evolution of next-generation wireless networks. In this paper, we propose an FA-empowered integrated sensing and communication (ISAC) system in which a reconfigurable intelligent surface (RIS) is employed to mitigate performance degradation in communication and sensing caused by blockages of line-of-sight links. We consider a joint optimization of transmit beamforming matrices, RIS coefficients, and transmit antenna positions to maximize the sum rate subject to power budget, RIS, and antenna spacing constraints, as well as flexible regions. Due to the non-convexity and complexity of the objective, the alternative algorithm-based framework is used to iteratively solve the original optimization problem, in which the problem is decomposed into several subproblems using the fractional programming algorithm. Specifically, the subproblems regarding precoding, RIS phase shifts, and antenna positions are efficiently solved using semidefinite relaxation, Riemannian steepest-descent, and majorization minimization algorithms, respectively. Simulation results demonstrate the efficiency and superiority of the proposed framework over the conventional RIS-aided fixed-position antenna ISAC system.

Digital Twin-Assisted Beamforming for Millimeter Wave Massive MIMO

Millimeter wave (mmWave) Massive MIMO is a cornerstone technology for sixth-generation (6G) wireless networks, providing the directional gain necessary to overcome high path loss. However, the acquisition of high-fidelity Channel State Information (CSI) and the associated beamforming overhead remain significant bottlenecks, particularly in dynamic environments with frequent blockages. In this paper, we propose a fast and robust beamforming strategy enabled by a digital twin (DT) framework. Specifically, we develop a Conditional Generative Adversarial Network (cGAN)-based DT module that serves as a high-fidelity virtual surrogate for site-specific ray-tracing. By processing environmental 3D geometry and dynamic obstacle data, the cGAN predicts real-time Beam-Power Maps (BPM) with minimal computational latency. Building upon these predictions, we introduce a Graph Neural Network (GNN)-based resource allocation agent that models the network as a spatial interference graph to perform coordination and power control. Numerical results demonstrate that our proposed DT-assisted approach significantly reduces online interaction overhead by shifting the computational burden of ray-tracing to an offline generative phase. Furthermore, the framework achieves superior sum-rate performance and link robustness under dynamic blockages compared to conventional deep learning and heuristic benchmarks.

Low Complexity-Based Block Selection Scheme for RIS-Assisted Wireless Systems

In wireless networks with severe blockage, path loss critically limits communication coverage. Reconfigurable Intelligent Surfaces (RIS) offer a promising remedy. However, the fine-grained control of massive reflecting elements incurs prohibitive computational overhead, which hinders real-time deployment. To address these challenges, this paper proposes a low-complexity scheme integrating RIS block selection with adaptive beamforming. The large-scale RIS is partitioned into multiple sub-arrays to enable block-wise phase control. By activating only those blocks with dominant channel gains, the system maximizes reflection gain while minimizing control overhead. To avoid the exponential complexity of exhaustive search, we develop a deep neural network (DNN)-based prediction architecture. By learning the mapping from channel states to optimal configurations, the DNN enables instantaneous selection of near-optimal RIS block combinations. Simulation results show that the proposed data-driven scheme achieves near-optimal bit error rate (BER) performance compared to exhaustive search. Notably, it avoids the exponential complexity growth typically associated with an increasing number of reflecting elements. The proposed mechanism extends reliable coverage range and improves link stability, offering an efficient solution for future wireless networks.