arXiv subjects

Yuhang Shen

Publications and source records attributed to Yuhang Shen.

4 recordsLinked to original sources

arXiv2026

Energy-Efficient Online Scheduling for Wireless Powered Mobile Edge Computing Networks

Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enhance the computational capability of wireless devices (WDs). In WPMEC systems, WPT and computation offloading compete for limited wireless resources, which makes their joint scheduling particularly challenging. In this paper, we investigate the energy-efficient online scheduling problem for WPMEC networks with multiple WDs and multiple access points (APs). Based on Lyapunov optimization, we develop an online optimization framework that transforms the original stochastic problem into deterministic per-slot optimization problems. To reduce computational complexity, we introduce the concept of marginal energy efficiency and derive an associated optimality condition, based on which a relax-then-adjust approach is proposed to efficiently obtain feasible solutions. For the resulting non-convex computation offloading subproblem, we analyze the structural properties of its optimal solution and transform it into an assignment problem that can be solved efficiently. We further provide theoretical performance guarantees for both the per-slot and long-term solution, establishing a fundamental trade-off between latency and energy consumption. To improve practical performance, additional mechanisms are introduced to balance the magnitudes of different queues and reduce latency without increasing energy consumption. Extensive simulation results demonstrate the effectiveness and robustness of the proposed algorithm under various system settings.

cs.NI↗

arXiv2026

SSPFormer: Self-Supervised Pretrained Transformer for MRI Images

The pre-trained transformer demonstrates remarkable generalization ability in natural image processing. However, directly transferring it to magnetic resonance images faces two key challenges: the inability to adapt to the specificity of medical anatomical structures and the limitations brought about by the privacy and scarcity of medical data. To address these issues, this paper proposes a Self-Supervised Pretrained Transformer (SSPFormer) for MRI images, which effectively learns domain-specific feature representations of medical images by leveraging unlabeled raw imaging data. To tackle the domain gap and data scarcity, we introduce inverse frequency projection masking, which prioritizes the reconstruction of high-frequency anatomical regions to enforce structure-aware representation learning. Simultaneously, to enhance robustness against real-world MRI artifacts, we employ frequency-weighted FFT noise enhancement that injects physiologically realistic noise into the Fourier domain. Together, these strategies enable the model to learn domain-invariant and artifact-robust features directly from raw scans. Through extensive experiments on segmentation, super-resolution, and denoising tasks, the proposed SSPFormer achieves state-of-the-art performance, fully verifying its ability to capture fine-grained MRI image fidelity and adapt to clinical application requirements.

cs.CV↗

arXiv2022

Social Welfare Maximization for Collaborative Edge Computing: A Deep Reinforcement Learning-Based Approach

Collaborative Edge Computing (CEC) is an effective method that improves the performance of Mobile Edge Computing (MEC) systems by offloading computation tasks from busy edge servers (ESs) to idle ones. However, ESs usually belong to different MEC service providers so they have no incentive to help others. To motivate cooperation among them, this paper proposes a cooperative mechanism where idle ESs can earn extra profits by sharing their spare computational resources. To achieve the optimal resource allocation, we formulate the social welfare maximization problem as a Markov Decision Process (MDP) and decompose it into two stages involving the allocation and execution of offloaded tasks. The first stage is solved by extending the well-known Deep Deterministic Policy Gradient (DDPG) algorithm. For the second stage, we first show that we only need to decide the processing order of tasks and the utilized computational resources. After that, we propose a dynamic programming and a Deep Reinforcement Learning (DRL)-based algorithm to solve the two types of decisions, respectively. Numerical results indicate that our algorithm significantly improves social welfare under various situations.

cs.NI↗

arXiv2021

An Online Scheduling Algorithm for Energy Minimization in Wireless Powered Mobile Edge Computing Networks

The integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT), which is usually referred to as Wireless Powered Mobile Edge Computing (WP-MEC), has been recognized as a promising technique to enhance the lifetime and computation capacity of wireless devices (WDs). Compared to the conventional battery-powered MEC networks, WP-MEC brings new challenges to the computation scheduling problem because we have to jointly optimize the resource allocation in WPT and computation offloading. In this paper, we consider the energy minimization problem for WP-MEC networks with multiple WDs and multiple access points. We design an online algorithm by transforming the original problem into a series of deterministic optimization problems based on the Lyapunov optimization theory. To reduce the time complexity of our algorithm, the optimization problem is relaxed and decomposed into several independent subproblems. After solving each subproblem, we adjust the computed values of variables to obtain a feasible solution. Extensive simulations are conducted to validate the performance of the proposed algorithm.

cs.NI↗