2022

1. Preprint

   Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation

   Abdelrahman Taha, Hao Luo, and Ahmed Alkhateeb

   arXiv preprint 2022

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   Current scene depth estimation approaches mainly rely on optical sensing, which carries privacy concerns and suffers from estimation ambiguity for distant, shiny, and transparent surfaces/objects. Reconfigurable intelligent surfaces (RISs) provide a path for employing a massive number of antennas using low-cost and energy-efficient architectures. This has the potential for realizing RIS-aided wireless sensing with high spatial resolution. In this paper, we propose to employ RIS-aided wireless sensing systems for scene depth estimation. We develop a comprehensive framework for building accurate depth maps using RIS-aided mmWave sensing systems. In this framework, we propose a new RIS interaction codebook capable of creating a sensing grid of reflected beams that meets the desirable characteristics of efficient scene depth map construction. Using the designed codebook, the received signals are processed to build high-resolution depth maps. Simulation results compare the proposed solution against RGB-based approaches and highlight the promise of adopting RIS-aided mmWave sensing in scene depth perception.

2. 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.

3. JMIR

   Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke

   Po-Yuan Su, Yi-Chia Wei, Hao Luo, Chi-Hung Liu, Wen-Yi Huang, Kuan-Fu Chen, Ching-Po Lin, Hung-Yu Wei, and Tsong-Hai Lee

   JMIR Medical Informatics 2022

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   Background: Timely and accurate outcome prediction plays a vital role in guiding clinical decisions on acute ischemic stroke. Early condition deterioration and severity after the acute stage are determinants for long-term outcomes. Therefore, predicting early outcomes is crucial in acute stroke management. However, interpreting the predictions and transforming them into clinically explainable concepts are as important as the predictions themselves. Objective: This work focused on machine learning model analysis in predicting the early outcomes of ischemic stroke and used model explanation skills in interpreting the results.

2021

1. S2S-BT

   Machine Learning Based mmWave Orchestration for Edge Gaming QoE Enhancement

   Hao Luo, and Hung-Yu Wei

   In Proc. IEEE VTC-Fall 2021

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   Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers imposes a heavy burden on the management of radio access network (RAN). This challenge increases the need for autonomous network management methods leveraging machine learning (ML) techniques. In particular, mmWave beam selection is a critical issue for the management of RAN due to the large training overhead on mmWave transceivers. To this end, a new beam tracking method based on sequence-to-sequence (Seq2Seq) learning is proposed. Besides, thanks to edge computing technologies, network management algorithms and delay-sensitive user applications can be hosted on edge servers in close proximity. Due to limited resources on the edge server, the resource allocation problem for beam tracking and edge gaming is investigated with the aim of maximizing game quality of experience (QoE). Simulation results verify the effectiveness of the proposed orchestration scheme.

2020

1. 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.