Hi there! I’m a first-year PhD student in Electrical Engineering at Arizona State University, where I work with Prof. Ahmed Alkhateeb in the Wireless Intelligence Lab.

Previously, I completed my Bachelor’s and Master’s degress in Electrical Engineering at National Taiwan University, the latter under the supervision of Prof. Hung-Yu Wei. From Jul. 2018 to Feb. 2020, I interned at Institute of Information Science in Academia Sinica with advisor Prof. Hsin-Min Wang.

My research interests include wireless communication, machine learning, and edge computing. Currently, I am working on the sensing and imaging problems in wireless systems.

I am also a big fan of Phoenix Suns. Let’s go Suns!

News

Nov, 2022	One paper is submitted, available on arXiv.
Sep, 2022	One journal was accepted to IEEE Transactions on Network and Service Management.
Aug, 2022	Start my PhD at Tempe!🌵
Jan, 2022	One journal was accepted to JMIR Medical Informatics.
Jul, 2021	One paper was accepted to IEEE VTC2021-Fall.

Selected publications

1. IEEE TNSM

   Resource Orchestration at the Edge: Intelligent Management of mmWave RAN and Gaming Application QoE Enhancement

   Hao Luo, and Hung-Yu Wei

   IEEE Transactions on Network and Service Management 2022

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   Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers would impose a heavy burden on the management of the radio access network (RAN). This challenge increases the need for leveraging intelligent network management techniques. Thanks to edge computing, machine learning (ML) based network management algorithms and other delay-sensitive user applications can operate at the network edge. But, due to the limited resources on edge servers, developing an orchestration scheme for intelligent network management and user applications is necessary. In this paper, we provide an edge-centric resource management framework for intelligent RAN management and applications with the awareness of the users’ quality of experiences (QoE). Specifically, we consider the scenario of a mmWave communication system equipped with an ML-based mmWave beam tracking algorithm. The users under this system request mobile edge gaming services. We formulate a game QoE aware orchestration problem as a non-linear integer programming and prove its NP-hardness. To reduce the complexity, we decompose the original problem into two subproblems, the service placement problem for mobile edge gaming and the configuration selection and placement problem for mmWave beam tracking. Then, we solve the two subproblems consecutively with heuristic approaches. Simulation results demonstrate the effectiveness of the proposed orchestration scheme.

2. CDVAE-CLS-GAN

   Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Autoencoder Based Voice Conversion

   Wen-Chin Huang, Hao Luo, Hsin-Te Hwang, Chen-Chou Lo, Yu-Huai Peng, Yu Tsao, and Hsin-Min Wang

   IEEE Transactions on Emerging Topics in Computational Intelligence 2020

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   An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE) based VC (VAE-VC), for instance, strongly relies on this principle. In our prior work, we proposed a cross-domain VAE-VC (CDVAE-VC) framework, which utilized acoustic features of different properties, to improve the performance of VAE-VC. We believed that the success came from more disentangled latent representations. In this article, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech. More specifically, we first investigate the effectiveness of incorporating the generative adversarial networks (GANs) with CDVAE-VC. Then, we consider the concept of domain adversarial training and add an explicit constraint to the latent representation, realized by a speaker classifier, to explicitly eliminate the speaker information that resides in the latent code. Experimental results confirm that the degree of disentanglement of the learned latent representation can be enhanced by both GANs and the speaker classifier. Meanwhile, subjective evaluation results in terms of quality and similarity scores demonstrate the effectiveness of our proposed methods.