arXiv subjects

Ruihong Jiang

Publications and source records attributed to Ruihong Jiang.

7 recordsLinked to original sources

arXiv2025

Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction

Due to their inherent flexibility and autonomous operation, unmanned aerial vehicles (UAVs) have been widely used in Internet of Medical Things (IoMT) to provide real-time biomedical edge computing service for wireless body area network (WBAN) users. In this paper, considering the time-varying task criticality characteristics of diverse WBAN users and the dual mobility between WBAN users and UAV, we investigate the dynamic task offloading and UAV flight trajectory optimization problem to minimize the weighted average task completion time of all the WBAN users, under the constraint of UAV energy consumption. To tackle the problem, an embodied AI-enhanced IoMT edge computing framework is established. Specifically, we propose a novel hierarchical multi-scale Transformer-based user trajectory prediction model based on the users' historical trajectory traces captured by the embodied AI agent (i.e., UAV). Afterwards, a prediction-enhanced deep reinforcement learning (DRL) algorithm that integrates predicted users' mobility information is designed for intelligently optimizing UAV flight trajectory and task offloading decisions. Real-word movement traces and simulation results demonstrate the superiority of the proposed methods in comparison with the existing benchmarks.

cs.NI↗

arXiv2025

Average AoI in Pinching Antenna-assisted WPCNs with Probabilistic LoS Blockage

This paper analyzes the age of information (AoI) for a pinching antenna (PA)-assisted wireless powered communication network (WPCN) with probabilistic line-of-sight (LoS) blockage. AoI is a key metric for evaluating the freshness of status updates in IoT networks, and its optimization is crucial for ensuring the performance of time-critical applications. To facilitate analysis and gain useful insights, we consider a representative scenario, where an IoT device harvests energy from a base station (BS) equipped with a PA and transmits data packets to it. The IoT device harvests energy via the PA until its capacitor is fully charged, then transmits status updates using all stored energy. We derive closed-form expressions for the average AoI by analyzing the capacitor charging time, transmission success probability, and inter-arrival time of successful updates. To minimize the average AoI, we formulate an optimization problem of PA position, and propose a one-dimensional search to solve it. The simulation results show that the optimal PA position is the one closest to the IoT device, and this conclusion can be extended to the multi-IoT devices frequency division multiple access (FDMA) scenario. The PA-based systems significantly outperform the conventional fixed-antenna systems.

cs.IT↗

arXiv2025

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), struggle to meet the demanding requirements of real-time adaptability, scalability, and dynamic handling of user intents in increasingly heterogeneous and resource-constrained network environments. Large language models (LLMs) present a transformative paradigm by enabling natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement through advanced semantic understanding and structured reasoning capabilities. This paper provides a systematic and comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks. We first introduce foundational design concepts and distinguish LLM-enabled methods from conventional optimization paradigms. Subsequently, we critically analyze key enabling methodologies, including natural language modeling, solver collaboration, and solution verification processes. Moreover, we explore representative case studies to demonstrate LLMs' transformative potential in practical scenarios such as optimization formulation, low-altitude economy networking, and intent networking. Finally, we discuss current research challenges, examine prominent open-source frameworks and datasets, and identify promising future directions to facilitate robust, scalable, and trustworthy LLM-enabled optimization solutions for next-generation wireless networks.

cs.NI↗

arXiv2025

Spatially Adaptive SWIPT with Pinching Antenna under Probabilistic LoS Blockage

This paper considers a power-splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) system employing a reconfigurable pinching antenna (PA) under probabilistic line-of-sight (LoS) blockage. We formulate a joint optimization of the PA position and PS ratio to maximize the average signal-to-noise ratio (SNR) at the user, subject to its average energy harvesting (EH) and PA placement range. We derive the closed-form solution. Results show that the EH requirement has a deterministic impact on the optimal PA position and its feasible region, requiring the PA close to the user for large channel gain. Moreover, stronger waveguide attenuation lowers the overall SNR and shifts the optimal PA toward the feed point, while heavier LoS blockage degrades the SNR uniformly with little change in the optimal PA position. Spatial PA adaptation combined with dynamic PS ensures robust SWIPT performance, and mechanical reconfigurability enhances sustainability by guaranteeing energy feasibility in dynamic environments.

eess.SP↗

arXiv2025

LAURA: LLM-Assisted UAV Routing for AoI Minimization

With the rapid growth of the low-altitude economy, there is increasing demand for real-time data collection using UAV-assisted wireless sensor networks. This paper investigates the problem of minimizing the age of information (AoI) in UAV-assisted wireless sensor networks by optimizing the UAV flight routing. We formulate the AoI minimization task and propose a large language model (LLM)-assisted UAV routing algorithm (LAURA). LAURA employs an LLM as intelligent crossover operators within an evolutionary optimization framework to efficiently explore the solution space. Simulation results show that LAURA outperforms benchmark methods in reducing the maximum AoI, especially in scenarios with a large number of sensor nodes.

cs.NI↗

arXiv2024

STAR-RIS Enabled ISAC Systems: Joint Rate Splitting and Beamforming Optimization

This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communication and radar capabilities, enabling it to communicate with ground terminals (GTs) and concurrently probe for echo signals from a target of interest. Moreover, to manage interference and improve communication quality, the rate splitting multiple access (RSMA) scheme is incorporated into the system. The signal-to-interference-plus-noise ratio (SINR) of the received sensing echo signals is a measure of sensing performance. We formulate a joint optimization problem of common rates, transmit beamforming at the BS, and passive beamforming vectors of the STAR-RIS. The objective is to maximize sensing SINR while guaranteeing the communication rate requirements for each GT. We present an iterative algorithm to address the non-convex problem by invoking Dinkelbach's transform, semidefinite relaxation (SDR), majorization-minimization, and sequential rank-one constraint relaxation (SROCR) theories. Simulation results manifest that the performance of the studied ISAC network enhanced by the STAR-RIS and RSMA surpasses other benchmarks considerably. The results evidently indicate the superior performance improvement of the ISAC system with the proposed RSMA-based transmission strategy design and the dynamic optimization of both transmission and reflection beamforming at STAR-RIS.

cs.IT↗

arXiv2024

RF-based Energy Harvesting: Nonlinear Models, Applications and Challenges

So far, various aspects associated with wireless energy harvesting (EH) have been investigated from diverse perspectives, including energy sources and models, usage protocols, energy scheduling and optimization, and EH implementation in different wireless communication systems. However, a comprehensive survey specifically focusing on models of radio frequency (RF)-based EH behaviors has not yet been presented. To address this gap, this article provides an overview of the mainstream mathematical models that capture the nonlinear behavior of practical EH circuits, serving as a valuable handbook of mathematical models for EH application research. Moreover, we summarize the application of each nonlinear EH model, including the associated challenges and precautions. We also analyze the impact and advancements of each EH model on RF-based EH systems in wireless communication, utilizing artificial intelligence (AI) techniques. Additionally, we highlight emerging research directions in the context of nonlinear RF-based EH. This article aims to contribute to the future application of RF-based EH in novel communication research domains to a significant extent.

cs.IT↗