A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

Pinching antennas (PAs) represent a revolutionary flexible antenna technology that leverages dielectric waveguides and electromagnetic coupling to mitigate large-scale path loss. This letter is the first to explore channel estimation for Pinching-Antenna SyStems (PASS), addressing their uniquely ill-conditioned and underdetermined channel characteristics. In particular, two efficient deep learning-based channel estimators are proposed. 1) PAMoE: This estimator incorporates dynamic padding, feature embedding, fusion, and mixture of experts (MoE) modules, which effectively leverage the positional information of PAs and exploit expert diversity. 2) PAformer: This Transformer-style estimator employs the self-attention mechanism to predict channel coefficients in a per-antenna manner, which offers more flexibility to adaptively deal with dynamic numbers of PAs in practical deployment. Numerical results demonstrate that 1) the proposed deep learning-based channel estimators outperform conventional methods and exhibit excellent zero-shot learning capabilities, and 2) PAMoE delivers higher channel estimation accuracy via MoE specialization, while PAformer natively handles an arbitrary number of PAs, trading self-attention complexity for superior scalability.

In this letter, we study the hybrid-field cascaded channel estimation in the extremely large-scale RIS (XL-RIS) assisted multi-user millimeter wave systems, where the cascaded channel is composed of far-field and near-field radiation components, and has spatial non-stationarity caused by visibility regions. We propose a U-shaped network based on the dedicated multilayer perceptron (MLP) architecture, termed as U-MLP, to realize the high-dimensional channel reconstruction with limited pilot overhead. In U-MLP, a basic feature extraction module-Permutator is designed to capture the long-range dependency of non-stationary channel, while the U-shaped backbone is constructed to exploit effective latent representation of the high-dimensional cascaded channel. Numerical results show that the proposed U-MLP outperforms existing channel estimation benchmarks with less pilot overhead.

The pinching-antenna architecture has emerged as a promising solution for reconfiguring wireless propagation environments and enhancing system performance. While prior research has primarily focused on sum-rate maximization or transmit power minimization of pinching-antenna systems, the critical aspect of energy efficiency (EE) has received limited attention. Given the increasing importance of EE in future wireless communication networks, this work investigates EE optimization in a non-orthogonal multiple access (NOMA)-assisted multi-user pinching-antenna uplink system. The problem entails the joint optimization of the users’ transmit power and the pinching-antenna position. The resulting optimization problem is non-convex due to tightly coupled variables. To tackle this, we employ an alternating optimization framework to decompose the original problem into two subproblems: one focusing on power allocation and the other on antenna positioning. A low-complexity optimal solution is derived for the power allocation subproblem, while the pinching-antenna positioning subproblem is addressed using a particle swarm optimization algorithm to obtain a high-quality near-optimal solution. Simulation results demonstrate that the proposed scheme significantly outperforms both conventional-antenna configurations and orthogonal multiple access-based pinching-antenna systems in terms of EE.

In this letter, we investigate an uplink communication scenario in which multiple users communicate with an access point (AP) employing non-orthogonal multiple access (NOMA). A pinching antenna, which can be activated at an arbitrary point along a dielectric waveguide, is deployed at the AP to dynamically reconfigure user channels. The objective is to maximize the system sum rate by jointly optimizing the pinching-antenna’s position and the users’ transmit powers. Two scenarios are considered: one without quality-of-service (QoS) guarantees, and the other with QoS guarantees. In the former case, users transmit at full power, and the antenna position is determined using the particle swarm optimization (PSO) algorithm. In the latter, an alternating optimization approach is adopted, where a low-complexity solution is derived for power allocation, and a modified PSO algorithm is applied to optimize the antenna position. Numerical results show that the proposed pinching-antenna-assisted system significantly improves the sum rate compared to the conventional fixed-antenna architecture. Furthermore, the NOMA-based approach consistently outperforms its TDMA-based counterpart. Finally, the proposed PSO-based method achieves near-optimal performance, particularly when the QoS constraints are moderate.

The arrival of the sixth generation (6G) wireless communication will intensify the developments of the Internet of Things (IoT) for widely applications in resilient industry 5.0. Driving the development of the intelligent consumer industry, consumer electronic devices have progressively become indispensable components of the constantly rising IoT. However, the steady rise of consumer IoT intelligent terminals has led to large-scale connections, spectrum scarcity, and energy consumption emerging as crucial challenges for the future wireless communication in resilient industry 5.0. To mitigate the aforementioned issues, a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted cognitive radio (CR)-non-orthogonal multiple access (NOMA) network are proposed. Specifically, we comprehensively elaborate on systems performance in terms of outage probability (OP), ergodic rate (ER) and energy efficiency (EE). Furthermore, the asymptotic OP and ER of the primary and secondary consumer electronic devices under the high signal-to-noise ratio (SNR) regions are investigated to undertake a thorough analysis. We find that the outage and ER performance gradually increase with the transmit SNR, and tend to a fixed value at high SNR regions. In addition, increasing the number of STAR-RIS components will enhance the reliable, ER and EE performance of the considered systems. Finally, the accuracy of analysis and the superiority of the proposed scheme are verified through Monte Carlo simulations.