Search Results for author: Hao-Hsuan Chang

Found 4 papers, 0 papers with code

Federated Dynamic Spectrum Access

no code implementations28 Jun 2021 Yifei Song, Hao-Hsuan Chang, Zhou Zhou, Shashank Jere, Lingjia Liu

In this article, we introduce a Federated Learning (FL) based framework for the task of DSA, where FL is a distributive machine learning framework that can reserve the privacy of network terminals under heterogeneous data distributions.

Federated Learning Multi-agent Reinforcement Learning

Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond

no code implementations12 Oct 2020 Hao-Hsuan Chang, Lingjia Liu, Yang Yi

However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to achieve convergence.

Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots

no code implementations25 Jun 2019 Zhou Zhou, Lingjia Liu, Hao-Hsuan Chang

Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights.

Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach

no code implementations28 Oct 2018 Hao-Hsuan Chang, Hao Song, Yang Yi, Jianzhong Zhang, Haibo He, Lingjia Liu

To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics.

Q-Learning

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