Skip to content
View FIVEYOUNGWOO's full-sized avatar

Block or report FIVEYOUNGWOO

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
FIVEYOUNGWOO/README.md

Introduction :

I am deeply passionate about designing and validating Software-Defined Radio (SDR)-based radio frequency (RF) simulators/networks and optimizing them using reinforcement learning (RL) approaches. Over two years as an undergraduate intern and during my M.S. studies at the Smart Networking Lab (SNL) in Computer Science at Chosun University, I developed expertise in wireless communications and RF-based sensing.


Research Experience 1 (During M.S. degree):

Wireless technology has achieved remarkable advancements in frequency performance, yet these gains occur with increased energy consumption and computational complexity. To address these challenges, my work uses reinforcement learning to optimize resource allocation while balancing spectral and energy efficiency trade-offs. Specifically, I designed the reinforcement learning algorithm for massive antenna-aided base station (BS) transmission power allocation in multi-cellular networks. This work, published in IEEE Access, received the Best Paper Award at the 33rd Joint Conference on Communications and Information (JCCI).


Research Experience 2 (During B.S. degree) :

I also designed and validated SDR-based transceivers, including Multi-Hop Routing algorithms, TDD/TDMA, OFDM, and MIMO systems with digital signal processing techniques. These efforts resulted in nine research papers presented at international and domestic conferences, including the Best Paper Award at the 2021 Winter Conference on Korea Information and Communications Society (KICS).


Research Demonstration :

Lastly, I actively share my research methodologies and experimental results in wireless communication simulation and testbed development through my YouTube channel, fostering open collaboration and knowledge-sharing.


Preferred Programming Languages :

  • Python
  • LabVIEW & NXG
  • MATLAB & GNU Octave
  • Java
  • C/C++

Preferred Programming Frameworks :

  • PyTorch/TensorFlow (For Deep Learning Development)
  • Open AI Gym/StableBaselines (For Reinforcement Learning Development)
  • LabVIEW/Communications System Design Suite (For Wireless Communication System Design and Validation)
  • Python Flask (For Frontend development)
  • Android Studio/Java (For Mobile Application development)

Educational Backgrounds :

  • M.S. in the Department of Computer Engineering, Chosun University.
  • B.S. in the Department of Computer Engineering, Chosun University.

Popular repositories Loading

  1. DQN-Based-Power-Allocation-For-Multi-Cell-Massive-MIMO DQN-Based-Power-Allocation-For-Multi-Cell-Massive-MIMO Public

    Deep Q network-based power allocation for multi-cell massive MIMO cellular network.

    Jupyter Notebook 15 7

  2. IEEE-802.11n-CSI-Camera-Synchronization-Toolkit IEEE-802.11n-CSI-Camera-Synchronization-Toolkit Public

    IEEE 802.11n CSI and camera synchronization toolkit.

    C 11 4

  3. Tranditional-MIMO-Antenna-Selection Tranditional-MIMO-Antenna-Selection Public

    Tranditional MIMO antenna selection schemes in MATLAB.

    MATLAB 10

  4. Reinforcement-Learning-Based-MIMO-Antenna-Selection Reinforcement-Learning-Based-MIMO-Antenna-Selection Public

    DQN, DDQN, and Policy Gradient Algorithm-Based Antenna Selection Schemes in MIMO Systems.

    Jupyter Notebook 10 3

  5. LMS-Algorithm-Based-Adaptive-Equalizer-For-Digital-Communications LMS-Algorithm-Based-Adaptive-Equalizer-For-Digital-Communications Public

    Least Mean Squares-Based Adaptive Digital Equalizer in Labview NXG.

    8

  6. Advantage-Actor-Critic-Based-MIMO-Antenna-Selection Advantage-Actor-Critic-Based-MIMO-Antenna-Selection Public

    Advanced actor-critic-based antenna selection for MIMO systems.

    Jupyter Notebook 7 1