Skip to content
View neardws's full-sized avatar
πŸ˜…
Wubba Lubba Dub-Dub
πŸ˜…
Wubba Lubba Dub-Dub

Highlights

  • Pro

Organizations

@uestcer @cqu-bdsc @FlashRL @ai-for-driving

Block or report neardws

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
neardws/README.md

Hi! U are the -th visitor

I am currently a Postdoctoral Research Fellow in cooperation with Prof. Shaohua Wan at the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen, China. I received the Ph.D. degree in Computer Science from the College of Computer Science at the Chongqing University (CQU), Chongqing, China, in 2023 and the B.S. degree in Network Engineering from the School of Computer and Control Engineering at the North University of China (NUC), Taiyuan, China, in 2017. I have authored and co-authored more than 10 papers with total google scholar .

My research interests include:

  • Vehicular Networks
  • Vehicular Cyber-Physical Systems
  • Edge Computing
  • Deep Reinforcement Learning
  • Game Theory

πŸ”₯ News

  • 2023.04: My GitHub stars have reached 100!
  • 2022.10: πŸŽ‰πŸŽ‰ One paper is accepted by JSA!
  • 2022.10: πŸŽ‰πŸŽ‰ My google scholar citations have reached 100!
  • 2022.09: One invention patent is granted!
  • 2022.06: One paper is accepted by IEEE ITSC 2022!

πŸ•’ Recent Research

DT-VEC

Enabling Digital Twin in Vehicular Edge Computing: A Multi-Agent Multi-Objective Deep Reinforcement Learning Solution
Xincao Xu, Kai Liu, Penglin Dai, and Biwen Chen

  • We present a DT-VEC architecture, where the heterogeneous information can be sensed by vehicles and uploaded to the edge node via V2I communications. The DT-VEC are modeled at the edge node, forming a logical view to reflect the physical vehicular environment.
  • We model the DT-VEC by deriving an ISAC-assisted sensing model and a reliability-guaranteed uploading model.
  • We formulate the bi-objective problem to maximize the system quality and minimize the system cost, simultaneously. In particular, we define the quality of DT-VEC by considering the timeliness and consistency, and define the cost of DT-VEC by considering the redundancy, sensing cost, and transmission cost.
  • We propose a multi-agent multi-objective (MAMO) deep reinforcement learning solution implemented distributedly in the vehicles and the edge nodes. Specifically, a dueling critic network is proposed to evaluate the advantage of action over the average of random actions.
  • Submitted to IEEE Transactions on Consumer Electronics (under review)
VCPS

Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach
Xincao Xu, Kai Liu, Penglin Dai, Ruitao Xie, and Jiangtao Luo

  • We present a VEC architecture, in which heterogeneous information can be cooperatively sensed and uploaded via V2I communications. Logical views can be constructed by fusing the heterogeneous information at edge nodes.
  • We derive a cooperative sensing model based on the multi-class M/G/1 priority queue. On this basis, we define a noval metric AoV by modeling the timeliness, completeness, and consistency of the logical views.
  • We formulate the problem, which aims at maximizing the quality of VCPS.
  • We propose a multiagent DRL solution, where a difference-reward-based credit assignment is designed to divide the system reward into the difference reward for vehicles, reflecting their individual contributions.
  • Submitted to IEEE Transactions on Intelligent Transportation Systems (under review)
JSA 2022

Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach
Xincao Xu, Kai Liu, Penglin Dai, Feiyu Jin, Hualing Ren, Choujun Zhan, and Songtao Guo

  • We present a NOMA-based VEC architecture, where heterogeneous resources of edge nodes are cooperated for real-time task processing.
  • We derive a V2I transmission model by considering both intra-edge and inter-edge interference and formulate a cooperative resource optimization (CRO) problem by jointly optimizing real-time task offloading and heterogeneous resource allocation, aiming at maximizing the service ratio.
  • We decompose the CRO into two subproblems, namely, task offloading and resource alloction. The first subproblem is modeled as an EPG with Nash equilibrium (NE) existence and converagence, and a multi-agent D4PG algorithm is proposed to achieve the NE by adopting the potential function as reward function. The second subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed based on a gradient-based iterative method and KKT condition.
  • Accepted by Journal of Systems Architecture (JCR Q1)

πŸ“– Publications

JCR: Journal Citation Reports by Clarivate Com.
SCI: Journal Partition List by National Science Library, Chinese Academy of Sciences
CCF: Recommended Publications by China Computer Federation
*: Corresponding Author

Journal

Conference

Chinese Papers

  • Xincao Xu, Kai Liu*, Chunhui Liu, Hao Jiang, Songtao Guo and Weiwei Wu, Potential Game Based Channel Allocation for Vehicular Edge Computing, Tien Tzu Hsueh Pao/Acta Electronica Sinica, volume 49, issue 5, pp.851-860, July 2021. [CCF T1]
  • Xincao Xu, Yi Zhou, Kai Liu, Chaocen Xiang, Yantao Li and Songtao Guo, Potenial Game based Distributed Channel Allocation in Vehicular Fog Computing Environments, 14th China Conference on Internet of Things (CWSN’20), Dunhuang, China, September 18-21, 2020. (Best Paper Candidate)

πŸ’» Coding

Neardws's GitHub stats

πŸ“Š Weekly development breakdown

Python     πŸ•“ 34m β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ 98.8%
SSH Config πŸ•“ 0s  β–Žβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘  1.2%

Pinned Loading

  1. learning-package learning-package Public

    Forked from cqu-bdsc/learning-package

    A learning package for research students of computer science.

    1 1