Hao-Hsuan Chang

Email: haohsuan [at] vt.edu

[Curriculum Vitae] [Google Scholar] [Github]

Research Interests

Dynamic Spectrum Access, Resource Allocation, Non-convex Optimization,

Deep learning, Reservoir computing, Deep Reinforcement learning

News

Publications

Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond
Hao-Hsuan Chang*, Lingjia Liu, and Yang Yi
IEEE Transactions on Neural Networks and Learning Systems

Accelerating Model-Free Reinforcement Learning With Imperfect Model Knowledge in Dynamic Spectrum Access
Lianjun Li, Lingjia Liu, Jianan Bai, Hao-Hsuan Chang*, Hao Chen, Jonathan D. Ashdown, Jianzhong Zhang, and Yang Yi
IEEE Internet of Things Journal, 2020

Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots
Zhou zhou, Lingjia Liu, and Hao-Hsuan Chang*
IEEE Transactions on Wireless Communications, 2020

Deep Residual Learning Meets OFDM Channel Estimation
Lianjun Li, Hao Chen, Hao-Hsuan Chang*, and Lingjia Liu
IEEE Wireless Communications Letters, 2020

Maximizing System Throughput in D2D Networks using Alternative DC Programming
Hao-Hsuan Chang*, Lingjia Liu, Hao Song, Alex Pidwerbetsky, Allan Berlinsky, Jonathan Ashdown, Kurt Turck, and Yang Yi
IEEE Global Communications Conference, 2019

Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
Hao-Hsuan Chang*, Hao Song, Yang Yi, Jianzhong Zhang, Haibo He, and Lingjia Liu
IEEE Internet of Things Journal, 2019
Code

Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks
Hao Song, Lingjia Liu, Hao-Hsuan Chang*, Jonathan Ashdown, and Yang Yi
IEEE Conference on Computer Communications Workshops, 2019