Browsing by Author "Lin, Hsin-Piao"

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Communications and Networking Technologies for Intelligent Drone Cruisers
(2019-01-01)
Wang, Li-Chun
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Lai, Chuan-Chi
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Shuai, Hong-Han
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Lin, Hsin-Piao
;
Li, Chi-Yu
Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of self-configuration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following live technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.
Learning-based Downlink User Selection Algorithm for UAV-BS Communication Network
(2020-01-01)
Wu, Chu-Peng
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Li, Yun-Ruei
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Wang, Jing-Ling
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Lin, Hsin-Piao
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Wang, Li-Chun
Recently, the development of Unmanned Aerial Vehicle (UAV) has been nearly matured and widely used in various fields. The combination of UAV and communication technologies, such as UAV Base Station (UAV-BS), can significantly increase the flexibility and scalability of the overall communication networks to provide more efficient communication services. While the UAVBS improves the network service efficiency, the quality of services (QoS) in the air-to-ground communication link is highly affected unless the right users are unknown. In this paper, we propose the learning-based downlink user selection algorithm. The 3D downlink channel can be fast identified to judiciously select the users subset. In our proposed framework, we combine the k-means clustering and Convolutional Neural Network (CNN) that can increase the estimation accuracy of 3D wireless channels to enhance the communication service efficiency of the UAV-BS network. The field measurement results show that proposed method can achieve an average bit error rate (BER) of 3.56x 10(-7), which is better than the distance-based selection scheme that has an average of BER 2.88x10(-3). The feasibility and effectiveness of the proposed method in real environment are proved, experimentally.
Mobility Predictions for IoT Devices Using Gated Recurrent Unit Network
(2020-01-01)
Belay, Abebe
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Lin, Hsin-Piao
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Wang, Li-Chun
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電機工程學系
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Department of Electrical and Computer Engineering
Wireless and mobile technologies, as well as their users, are growing rapidly as the Internet of Things (IoT) products, such as sensor-network technologies, mobile devices, and supporting applications, become widely dispersed. Owing to the dynamic changes in the wireless networks and the exponential growth of the IoT products, which make it difficult to locate large quantities of users and devices, providing accurate tracking and trajectory predictions in open and highly condensed wireless networks is extremely difficult. An adaptive and scalable system is required to offer accurate location-based services (LBSs) for the success of IoT. To enhance the attainment of IoT, we propose a hybrid of principal component analysis (PCA) and gated recurrent unit (GRU) algorithms for mobility predictions in a wireless urban area. During the system development processes, we first collect an LTE signal from three unmanned aerial vehicle base stations (UAV-BSs), the Wi-Fi signal strength from each reachable Wi-Fi access points (APs), and channel information from the Wi-Fi signal media. We then apply PCA to reduce the number of Wi-Fi features and to decrease signal noise. Next, we train the GRU algorithm to develop models that can predict the mobility of IoT device users. Finally, we evaluate the tracking and trajectory models. To evaluate the proposed techniques, we compare the common parameters of the GRU with those of other deep learning types. The proposed technique provides plausible and state-of-the-art results for mobility predictions of IoT devices in a wireless environment.
Multi-UAVs Communication for Payload Data Link Enhancement under Asynchronous Access
(2018-01-01)
Li, Yun-Ruei
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Lin, Hsin-Piao
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Wang, Li-Chun
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Adege, Abebe Belay
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Chen, Li-Yong
Multi-UAVs Payload Data Communication with Asynchronous Access
(2019-01-01)
Li, Yun-Ruei
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Lin, Hsin-Piao
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Wang, Jing-Ling
;
Wang, Li-Chun
;
Chiueh, Herming
This paper study the payload data communication of multi-UAVs system, and propose the system architecture. Considering the asynchronous interference of multi-UAVs communication, the filtered-OFDM is introduced to suppress the out-of-band emission and mitigate the inter-subband interference. According to simulation results, the proposed multi-UAVs system based on F-OFDM that is superior to OFDMA scheme in asynchronous interference suppression and spectral efficiency. Finally, we valid the multi-UAVs communication system work well under the considered air-to-ground channel.