Xu Gan

Xu Gan, Yuanwei Liu

A novel architecture of the center-fed pinching antenna system (C-PASS) is proposed. In contrast to the conventional end-fed PASS, signals are fed from the center input ports and propagate towards both sides of the waveguide. By doing so, spatial-multiplexing gain can be achieved in a single waveguide. Based on the proposed C-PASS, closed-form expressions for the degree of freedom (DoF) and power scaling laws are derived. These theoretical results reveal that C-PASS can achieve \emph{twice} the DoF and an additional multiplexing gain of $\mathcal{O}(P_T \ln^4 N/N^2)$ compared to the conventional PASS, where $P_T$ and $N$ represent the transmit power and pinching antenna number, respectively. Numerical results are provided to demonstrate that substantial capacity improvements can be achieved through the enhanced DoF and multiplexing gain of the C-PASS.

Xu Gan, Chongjun Ouyang, Yuanwei Liu

A unified analytical framework for joint design of communication and control (JDCC) is proposed. Within this framework, communication transmission delay and steady-state control variance are derived as the two fundamental JDCC performance metrics. The Pareto boundary is then established to characterize the optimal communication-control trade-off in JDCC systems. To further obtain closed-form expressions, their performance regions are derived under maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming. For system reliability evaluation, the communication-only and control-only outage probabilities are first derived. Based on these, the JDCC outage probability is defined to quantify the probability that the communication-delay and control-error requirements cannot be simultaneously satisfied. Its analytical expressions are then derived under both MRT and ZF schemes. Finally, numerical results validate the theoretical results and reveal that: (1) the Pareto boundary characterizes the trade-off frontier and performance limit of JDCC systems and (2) the JDCC reliability is jointly determined by the uplink-downlink closed-loop control and its coupling with communication.

Xu Gan, Zhaolin Wang, Yuanwei Liu

A dual-scale deployment (DSD) framework is proposed for pinching antenna systems (PASS), under which four protocols are provided. 1) For the coarse-scale deployment, the pinching antenna (PA) is transferred over a large-scale range at the waveguide level. 2) For the fine-scale deployment, the PA is adjusted with high precision within a small-scale region. By simultaneously optimizing both scales, the proposed DSD framework can unleash the full potential of PA deployment, while maintaining low computational complexity. Based on this framework, we establish a practical power consumption model and derive theoretical energy efficiency expressions for PASS. Then, an energy-efficiency maximization problem is formulated to jointly optimize the transmit precoding, PA radiation power, and dual-scale PA deployment. To solve this non-convex, highly coupled problem, a low-complexity penalty-based alternating optimization algorithm is proposed. Simulation results validate the accuracy of theoretical results and the convergence of the proposed algorithm. It is demonstrated that the proposed DSD framework is highly effective for PASS, delivering about $70\%$ higher energy efficiency than the conventional cell-free architecture and nearly a \emph{twofold} improvement relative to MIMO systems.

Yufeng He, Zefan Cai, Xu Gan et al.

Current image captioning works usually focus on generating descriptions in an autoregressive manner. However, there are limited works that focus on generating descriptions non-autoregressively, which brings more decoding diversity. Inspired by the success of diffusion models on generating natural-looking images, we propose a novel method DiffCap to apply continuous diffusions on image captioning. Unlike image generation where the output is fixed-size and continuous, image description length varies with discrete tokens. Our method transforms discrete tokens in a natural way and applies continuous diffusion on them to successfully fuse extracted image features for diffusion caption generation. Our experiments on COCO dataset demonstrate that our method uses a much simpler structure to achieve comparable results to the previous non-autoregressive works. Apart from quality, an intriguing property of DiffCap is its high diversity during generation, which is missing from many autoregressive models. We believe our method on fusing multimodal features in diffusion language generation will inspire more researches on multimodal language generation tasks for its simplicity and decoding flexibility.