Transmit Pinching-Antenna Systems (T-PASS): Connecting Wired to Wireless Communications

A transmit pinching-antenna system (T-PASS) framework is proposed, in which a single pinched waveguide is employed to jointly serve one wired user equipment (UE) and multiple wireless UEs. The signal radiated by the pinching antennas (PAs) is used to serve the wireless UEs, whereas the residual guided signal at the waveguide termination is used to serve the wired UE. To facilitate T-PASS transmission and mitigate inter-user interference, a hybrid non-orthogonal multiple access (NOMA) scheme is introduced. Wireless UEs are scheduled by time-division multiple access (TDMA), and, in each slot, the scheduled wireless UE is paired with the wired UE through power-domain NOMA. Within this framework, the PA positions, PA radiation coefficients, power allocation, and TDMA time-slot allocation are jointly optimized to maximize a weighted sum rate (WSR). i) For the two-user case with one wired UE and one wireless UE, the optimal PA position and successive interference cancellation (SIC) decoding order are derived. Closed-form optimal power allocation is obtained, and a near-optimal PA radiation coefficient is determined through a low-complexity one-dimensional search. ii) For the multiuser case with one wired UE and multiple wireless UEs, four protocols with different PA-position and PA-radiation configurations are proposed. For each protocol, a low-complexity element-wise alternating optimization algorithm is developed to optimize the PA positions and radiation coefficients, while closed-form solutions are derived for the optimal power allocation and time-slot allocation. Numerical results are presented to show that: i) under typical T-PASS configurations, the wired UE is selected as the strong user in the optimal SIC decoding order; ii) the proposed T-PASS framework achieves a significantly higher WSR than conventional wireless-only PASS.

Enabling Wireless Power Transfer (WPT) in Pinching Antenna Systems (PASS)

A novel pinching antenna system (PASS) enabled wireless power transfer (WPT) framework is proposed, where energy harvesting receivers (EHRs) and information decoding receivers (IDRs) coexist. By activating pinching antennas (PAs) near both receivers and flexibly adjusting PAs' power radiation ratios, both energy harvesting efficiency and communication quality can be enhanced. A bi-level optimization problem is formulated to overcome the strong coupling between optimization variables. The upper level jointly optimizes transmit beamforming, PA positions, and feasible interval of power radiation ratios for power conversion efficiency (PCE) maximization under rate requirements, while the lower level refines power radiation ratio for the sum rate maximization. Efficient solutions are developed for both two-user and multi-user scenarios. 1) For the two-user case, where an EHR and an IDR coexist, the alternating optimization (AO)-based and weighted minimum mean square error (WMMSE)-based algorithms are developed to achieve the stationary solutions of transmit beamforming, PA positions, and power radiation ratios. 2) For the multi-user case, a quadratic transform-Lagrangian dual transform (QT-LDT) algorithm is proposed to iteratively update PCE and sum rate by optimizing PA positions and power radiation ratios individually. Closed-form solutions are derived for both maximization problems. Numerical simulation results demonstrate that the proposed PASS-WPT framework significantly outperforms conventional MIMO and the baseline PASS with fixed power radiation, which demonstrates that: i) Compared to the conventional MIMO and baseline PASS, the proposed PASS-WPT framework achieves 81.45% and 43.19% improvements in PCE of EHRs, and ii) also increases the sum rate by 77.81% and 31.91% for IDRs.

LLM Enabled Beam Training for Pinching Antenna Systems (PASS)

To enable intelligent beam training, a large language model (LLM)-enabled beam training framework is proposed for the pinching antenna system (PASS) in downlink multi-user multiple-input multiple-output (MIMO) communications. A novel LLM-based beam training supervised learning mechanism is developed, allowing context-aware and environment-adaptive probing for PASS to reduce overheads. Both single-user and multi-user cases are considered. 1) For single-user case, the LLM-based pinching beamforming codebook generation problem is formulated to maximize the beamforming gain. Then, the optimal transmit beamforming is obtained by maximum ratio transmission (MRT). 2) For multi-user case, a joint codebook generation and beam selection problem is formulated based on the system sum rate under the minimum mean square error (MMSE) transmit beamforming. The training labels for pinching beamforming are constructed by selecting the beam combination that maximizes system performance from each user's Top-S candidate beams. Based on pretrained Generative Pre-trained Transformers (GPTs), the LLM is trained in an end-to-end fashion to minimize the cross-entropy loss. Simulation results demonstrate that: i) For single-user case, the proposed LLM-enabled PASS attains over 95% Top-1 accuracy in beam selection and achieves 51.92% improvements in beamforming gains compared to conventional method. ii) For multi-user case, the proposed LLM-enabled PASS framework significantly outperforms both the LLM-based massive MIMO and conventional PASS beam training, achieving up to 57.14% and 33.33% improvements in sum rate, respectively.