| Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach HH Chang, H Song, Y Yi, J Zhang, H He, L Liu IEEE Internet of Things Journal 6 (2), 1938-1948, 2018 | 239 | 2018 |
| Deep Residual Learning Meets OFDM Channel Estimation L Li, H Chen, HH Chang, L Liu IEEE Wireless Communications Letters 9 (5), 615-618, 2019 | 138 | 2019 |
| Deep echo state Q-network (DEQN) and its application in dynamic spectrum sharing for 5G and beyond HH Chang, L Liu, Y Yi IEEE Transactions on Neural Networks and Learning Systems 33 (3), 929-939, 2020 | 60 | 2020 |
| Learning for detection: MIMO-OFDM symbol detection through downlink pilots Z Zhou, L Liu, HH Chang IEEE Transactions on Wireless Communications 19 (6), 3712-3726, 2020 | 60 | 2020 |
| Accelerating Model-Free Reinforcement Learning With Imperfect Model Knowledge in Dynamic Spectrum Access L Li, L Liu, J Bai, HH Chang, H Chen, JD Ashdown, J Zhang, Y Yi IEEE Internet of Things Journal 7 (8), 7517-7528, 2020 | 27 | 2020 |
| Decentralized deep reinforcement learning meets mobility load balancing HH Chang, H Chen, J Zhang, L Liu IEEE/ACM Transactions on Networking 31 (2), 473-484, 2022 | 23 | 2022 |
| Federated multi-agent deep reinforcement learning (fed-madrl) for dynamic spectrum access HH Chang, Y Song, TT Doan, L Liu IEEE Transactions on Wireless Communications 22 (8), 5337-5348, 2023 | 19 | 2023 |
| Resource Allocation for D2D Cellular Networks With QoS Constraints: A DC Programming-Based Approach HH Chang, L Liu, J Bai, A Pidwerbetsky, A Berlinsky, J Huang, ... IEEE Access 10, 16424-16438, 2021 | 9 | 2021 |
| Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks H Song, L Liu, HH Chang, J Ashdown, Y Yi IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops …, 2019 | 7 | 2019 |
| Federated Dynamic Spectrum Access Y Song, HH Chang, Z Zhou, S Jere, L Liu arXiv preprint arXiv:2106.14976, 2021 | 6 | 2021 |
| Federated Dynamic Spectrum Access through Multi-Agent Deep Reinforcement Learning Y Song, HH Chang, L Liu GLOBECOM 2022-2022 IEEE Global Communications Conference, 3466-3471, 2022 | 5 | 2022 |
| Maximizing System Throughput in D2D Networks using Alternative DC Programming HH Chang, L Liu, H Song, A Pidwerbetsky, A Berlinsky, J Ashdown, ... IEEE Global Communications Conference, 2019 | 4 | 2019 |
| Optimal preprocessing of WiFi CSI for sensing applications VV Ratnam, H Chen, HH Chang, A Sehgal, J Zhang IEEE Transactions on Wireless Communications, 2024 | 3 | 2024 |
| MADRL Based Scheduling for 5G and Beyond HH Chang, RBS Sree, H Chen, J Zhang, L Liu MILCOM 2022-2022 IEEE Military Communications Conference (MILCOM), 873-878, 2022 | 3 | 2022 |
| DRL meets DSA Networks: Convergence Analysis and Its Application to System Design R Safavinejad, HH Chang, L Liu arXiv preprint arXiv:2305.11237, 2023 | 2 | 2023 |
| Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm Q Fan, J Bai, HH Chang, L Li, S Liu, J Huang, J Burgess, A Berlinsky, ... Proceedings of the 7th ACM International Conference on Nanoscale Computing …, 2020 | 2 | 2020 |
| Dyna-ESN: Efficient Deep Reinforcement Learning for Partially Observable Dynamic Spectrum Access HH Chang, N Mohammadi, R Safavinejad, Y Yi, L Liu IEEE Transactions on Wireless Communications, 2024 | | 2024 |
| Multi-antenna WiFi based breathing rate estimation VV Ratnam, H Chen, A Sehgal, HH Chang US Patent 12,160,298, 2024 | | 2024 |
| Wifi csi preprocessing for sensing applications VV Ratnam, H Chen, A Sehgal, HH Chang, J Zhang US Patent App. 18/429,209, 2024 | | 2024 |
| Deep Reinforcement Learning for Dynamic Spectrum Access: Convergence Analysis and System Design R Safavinejad, HH Chang, L Liu IEEE Transactions on Wireless Communications, 2024 | | 2024 |