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.

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.

In the traditional cloud–edge collaborative Internet of Things (IoT), the high-communication cost and slow convergence of the models often result in high-delay and energy consumption. In this article, a model and data dual-driven resource optimization mechanism is proposed for cloud–edge collaborative IoT applications. The model and data dual-driven mechanism is a joint delay and energy consumption optimization mechanism based on optimal transport and federated actor–critic (OTFAC) is proposed, which combines the offline and online learning. To be specific, in the model-driven offline learning phase, an optimization problem on the bandwidth and computation resource allocation is first formulated. The optimal transport (OT)-based offline optimization model is constructed. And then the OT-based algorithm is proposed to solve the optimization problem. In the data-driven online learning phase, federated actor–critic-based online optimization model are constructed. And then, the federated learning (FL) and actor–critic (AC) learning-based online resource optimization algorithm is designed to further reduce the delay and energy consumption with edge servers serving as local aggregators. Simulation results illustrate that the proposed OTFAC in this article reduces the average delay by 55% and the average energy consumption by 51% as compared with the benchmark hierarchical aggregation method HierFAVG. Compared with benchmark FL-based deep deterministic policy gradient method DDPG, the average delay is reduced by 47% and the average energy consumption is reduced by 43% by the proposed method.

Users’ behaviors in wireless metaverse networks are usually affected by the surrounding people and limited network resources. How to allocate network resources in a human-centric way remains an open problem in wireless metaverse scenarios. To capture the psychological influence of bandwagon effects on users’ behaviors, we first propose bandwagon effect-based metaverse behavior metrics, including the metaverse bandwagon threshold and metaverse bandwagon probability, based on the multi-dimensional contract theory. The bandwagon effect-based metaverse behavior metrics are used to quantify the number of service adopters, which consider both users’ behaviors and resource allocation strategies. Moreover, the metaverse behavior utility is derived for wireless metaverse networks based on the multi-attribute utility theory. To solve the metaverse behavior utility maximization problem, a bandwagon effect optimal transport-based (BEOT) algorithm is proposed to optimize the resource allocation strategies considering users’ behavior characteristics. Compared with the maximum metaverse behavior utility of virtual reality tracking-based resource allocation (VRT), soft actor-critic with graph convolutional networks (SAC-GCN) and the deep Q-learning-based (DQL) algorithms, simulation results show that the maximum metaverse behavior utility of proposed BEOT algorithm is improved by 22.55%, 12.36% and 18.21%, respectively.