Pinching antenna system (PASS) configures the positions of pinching antennas (PAs) along dielectric waveguides to change both large-scale fading and small-scale scattering, which is known as pinching beamforming. A novel non-orthogonal multiple access (NOMA) assisted PASS framework is proposed for downlink multi-user multiple-input multiple-output (MIMO) communications. The transmit power minimization problem is formulated to jointly optimize the transmit beamforming, pinching beamforming, and power allocation. To solve this highly nonconvex problem, both gradient-based and swarm-based optimization methods are developed. 1) For gradient-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed. The Lipschitz gradient surrogate function is constructed based on MM to tackle the nonconvex terms of this problem. Then, the joint optimization problem is decomposed into subproblems that are alternatively optimized based on PDD to obtain stationary closed-form solutions. 2) For swarm-based method, a fast-convergent particle swarm optimization and zero forcing (PSO-ZF) algorithm is proposed. Specifically, the PA position-seeking particles are constructed to explore high-quality pinching beamforming solutions. Moreover, ZF-based transmit beamforming is utilized by each particle for fast fitness function evaluation. Simulation results demonstrate that: i) The proposed NOMA assisted PASS and algorithms outperforms the conventional NOMA assisted massive antenna system. The proposed framework reduces over 95.22% transmit power compared to conventional massive MIMO-NOMA systems. ii) Swarm-based optimization outperforms gradient-based optimization by searching effective solution subspace to avoid stuck in undesirable local optima.

Pinching-antenna systems (PASS) improve wireless links by configuring the locations of activated pinching antennas along dielectric waveguides, namely pinching beamforming. In this paper, a novel adjustable power radiation model is proposed for PASS, where power radiation ratios of pinching antennas can be flexibly controlled by tuning coupling spacing between pinching antennas and waveguides. The closed-form coupling spacings are derived to achieve flexible and equal-power radiation. Based on the commonly-assumed equal-power radiation, a practical PASS framework relying on discrete activation is considered, where pinching antennas can only be activated among a set of predefined locations. A transmit power minimization problem is formulated, which jointly optimizes the transmit beamforming, pinching beamforming, and the numbers of activated pinching antennas, subject to each user’s minimum rate requirement. (1) To obtain globally optimal solutions of the resulting highly coupled mixed-integer nonlinear programming (MINLP) problem, branch-and-bound (BnB)-based algorithms are proposed for both single-user and multi-user scenarios. (2) A low-complexity many-to-many matching algorithm is further developed. Combined with the Karush-Kuhn-Tucker (KKT) theory, locally optimal and pairwise-stable solutions are obtained within polynomial-time complexity. Simulation results demonstrate that: (i) PASS significantly outperforms conventional multi-antenna architectures, particularly when the number of users and the spatial range increase; and (ii) The proposed matching-based algorithm achieves near-optimal performance, resulting in only a slight performance loss while significantly reducing computational overheads. Code is available at https://github.com/xiaoxiaxusummer/PASS_Discrete.

Emerging multimodal systems present new requirements for mobile edge networks to handle multimodal data. In this paper, a novel multimodal mobile edge computing (MEC) framework is proposed, which synchronizes the multimodal data acquisition, communication, and computation to ensure both consistency and efficiency. The key objective is to simultaneously maximize multimodal data throughput and minimize the energy consumption of mobile terminals (MTs) under synchronization and resource constraints. A multi-objective optimization (MOP) is formulated, where the sensor activation time, computation offloading, and resource allocation are jointly optimized. To solve this nonconvex problem, a dual-layer Lagrangian multiplier method (D-LMM) is developed. It decouples the optimization into an upper-level throughput maximization and a lower-level energy minimization. The former is converted into a convex problem via quadratic transformation, yielding a stationary solution for sensor activation times, while the latter is solved by alternating optimization. The D-LMM algorithm is proven to converge to a local optimum. Simulation results verify that the proposed framework significantly improves throughput and reduces MT energy consumption. The synchronization-aware multimodal coordination further ensures sufficient data collection and robust performance across varying network scales and resource conditions, enabling reliable downstream operations.

The popularity of artificial intelligence generated content (AIGC) is prompting the deployment of large language model (LLM) from cloud to edge networks, leading to mobile edge generation (MEG). Due to high latency and limited computational capabilities of mobile devices, personalized image generation for mobile healthcare and education requires edge-mobile generation paradigm. In this paper, a novel non-orthogonal multiple access (NOMA) assisted multi-user MEG framework is proposed for text-guided mobile image generation. NOMA enables concurrent access from multiple user equipments (UEs) to the edge-deployed large model, facilitating adjustable generation splitting. Specifically, the edge server (ES) partially generates the image and transmits it via downlink NOMA, while UEs complete the remaining parts using lightweight models. Both unlimited and limited energy budget scenarios are considered. 1) For unlimited energy budget, a joint generation splitting ratio and NOMA power allocation optimization problem is formulated, which minimizes the maximum (min-max) latency of UEs to ensure fairness. The closed-form globally optimal solutions based on Karush-Kuhn-Tucker (KKT) and Lambert-W theory are derived. Moreover, the superiority of MEG-NOMA over conventional MEG-orthogonal multiple access (OMA) is mathematically proved. 2) For limited energy budget, a multi-objective programming problem is formulated to minimize the latency of each UE, which leads to a user-centric latency minimization problem. The closed-form solutions of generation splitting ratio and power allocation are derived. Simulation results illustrate that the proposed MEG-NOMA outperforms the MEG-OMA in both two-user and multi-user cases. Compared to conventional MEG-OMA, the MEG-NOMA framework reduces the min-max latency and the user-centric latency by 33.01% and 9.86%, respectively.

A novel mobile edge generation (MEG) framework is proposed to efficiently operate large models at edge networks for low-latency image generation. The generation of large-scale image content is split into two parts, namely primary and secondary regions, with an adjustable generation splitting ratio. The primary region is generated by a large generative model (LGM) at the edge cloud and then transmitted to the mobile device, whereas the remaining secondary regions is created by a tiny generative model (TinyGM) at the mobile device, thus reducing transmission and computation overheads. Both single-user and multi-user cases are considered to characterize the tradeoff between mobile energy consumption and generation delay. For the single-user case, a multi-objective programming (MOP) is formulated for the joint optimization of generation splitting and mobile power control, which simultaneously minimizes the generation delay and mobile energy consumption. This MOP is transferred into single-objective optimization using the ϵ-constraint method. The closed-form optimal solution is derived to obtain Pareto-optimal energy-delay (E-D) region. It is revealed that MEG achieves significant performance gains then conventional fully edge generation (FEG) when signal-to-noise ratio (SNR) or mobile generative cost is low. For the multi-user case, a joint generation splitting and resource allocation problem is formulated, which minimizes the maximum generation delay subject to ϵ-bounded mobile energy consumption and resource constraints. An McCormick-relaxation branch-and-bound (M-BnB) algorithm is proposed to obtain the globally optimal solution. Simulation results demonstrate the Pareto-optimal E-D region in single-user and multi-user cases. Furthermore, MEG flexibly reduces delay compared to conventional FEG and model split schemes while maintaining generative quality.

A novel pinching antenna system (PASS)-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. PASS consists of multiple waveguides spanning over thousands of wavelength, which equip numerous low-cost dielectric particles, named pinching antennas (PAs), to radiate signals into free space. The positions of PAs can be reconfigured to change both the large-scale path losses and phases of signals, thus facilitating the novelpinching beamformingdesign. A sum rate maximization problem is formulated, which jointly optimizes the transmit and pinching beamforming to adaptively achieve constructive signal enhancement and destructive interference mitigation. To solve this highly coupled and nonconvex problem, both optimization-based and learning-based methods are proposed. 1) For the optimization-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed, which handles the nonconvex complex exponential component using a Lipschitz surrogate function and then invokes PDD for problem decoupling. 2) For the learning-based method, a novel Karush-Kuhn-Tucker (KKT)-guided dual learning (KDL) approach is proposed, which enables KKT solutions to be reconstructed in a data-driven manner by learning dual variables. Following this idea, a KDL-Transformer algorithm is developed, which captures both inter-PA/inter-user dependencies and channel-state-information (CSI)-beamforming dependencies by attention mechanisms. Simulation results demonstrate that: i) The proposed PASS framework significantly outperforms conventional massive multiple input multiple output (MIMO) system even with a few PAs. ii) The proposed KDL-Transformer can improve over 20% system performance than MM-PDD algorithm, while achieving a millisecond-level response on modern GPUs.