Hao-Hsuan Chang

Orcid: 0000-0002-6910-054X

According to our database1, Hao-Hsuan Chang authored at least 25 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Optimal Preprocessing of WiFi CSI for Sensing Applications.
IEEE Trans. Wirel. Commun., September, 2024

A 511-μW 89-dB-SNDR Asynchronous SAR-ISDM ADC With Noise Shaping Dynamic Amplifier and Time-Domain Noise-Slicing Technique.
IEEE J. Solid State Circuits, July, 2024

Multi-Person Respiration Rate Estimation With Single Pair Of Transmit And Receive Antenna.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access.
IEEE Trans. Wirel. Commun., August, 2023

Decentralized Deep Reinforcement Learning Meets Mobility Load Balancing.
IEEE/ACM Trans. Netw., April, 2023

2022
Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond.
IEEE Trans. Neural Networks Learn. Syst., 2022

A Single-Channel 1-GS/s 7.48-ENOB Parallel Conversion Pipelined SAR ADC With a Varactor-Based Residue Amplifier.
IEEE Trans. Circuits Syst. II Express Briefs, 2022

Resource Allocation for D2D Cellular Networks With QoS Constraints: A DC Programming- Based Approach.
IEEE Access, 2022

An Asynchronous Zero-Crossing-Based Incremental Delta-Sigma Converter.
Proceedings of the 2022 International Symposium on VLSI Design, Automation and Test, 2022

MADRL Based Scheduling for 5G and Beyond.
Proceedings of the IEEE Military Communications Conference, 2022

Federated Dynamic Spectrum Access through Multi-Agent Deep Reinforcement Learning.
Proceedings of the IEEE Global Communications Conference, 2022

2021
Deep Reinforcement Learning for Next Generation Wireless Networks with Echo State Networks.
PhD thesis, 2021

Federated Dynamic Spectrum Access.
CoRR, 2021

2020
Deep Residual Learning Meets OFDM Channel Estimation.
IEEE Wirel. Commun. Lett., 2020

Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots.
IEEE Trans. Wirel. Commun., 2020

Accelerating Model-Free Reinforcement Learning With Imperfect Model Knowledge in Dynamic Spectrum Access.
IEEE Internet Things J., 2020

Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm.
Proceedings of the NANOCOM '20: The Seventh Annual ACM International Conference on Nanoscale Computing and Communication, 2020

2019
Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach.
IEEE Internet Things J., 2019

Learn to Demodulate: MIMO-OFDM Symbol Detection through Downlink Pilots.
CoRR, 2019

Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks.
Proceedings of the IEEE INFOCOM 2019, 2019

Maximizing System Throughput in D2D Networks Using Alternative DC Programming.
Proceedings of the 2019 IEEE Global Communications Conference, 2019

Machine learning enabled distributed mobile edge computing network.
Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 2019

2014
End-point preserved stroke extraction.
Proceedings of the International Conference on Audio, 2014

Edge adaptive hybrid norm prior method for blurred image reconstruction.
Proceedings of the 2014 IEEE Asia Pacific Conference on Circuits and Systems, 2014

A region adaptive encoding algorithm for simple image compression.
Proceedings of the 2014 IEEE Asia Pacific Conference on Circuits and Systems, 2014


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