Browsing by Author "Wang, Hao-Chen"

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Design and Analysis of Waveguide-Coupled Photonic THz Transmitters With an Extremely Wide Fractional Bandwidth
(2018-10-01)
Chen, Nan-Wei
;
Wun, Jhih-Min
;
Wang, Hao-Chen
;
Chao, Rui-Lin
;
Koh, Christopher
The design and analysis of novel waveguide-coupled photonic transmitters, which can have an extremely wide +/- 3-dB fractional optical-to-electrical (O-E) bandwidth at near the tera-hertz (THz) operating frequency regime (100%; 0.1-0.3 THz), are presented. This novel module is composed of a customized WR-10-based wideband antenna and an ultrafast/high-power photodiode with a 0.33-THz 3-dB O-E bandwidth, which is flip-chip bonded onto an aluminum nitride substrate, integrated with an impedance matching circuit as well as a bow-tie radiator to serve as the antenna feed. The WR-10-based dual-ridge horn structure is adopted to realize the extremely wideband antenna because the dominant TEM mode supported by the dual ridges has no cutoff frequency. The proposed antenna exhibits more than 18 dB gain across the whole operating bandwidth (0.1-0.3 THz). With the transmitter operating at an output photocurrent of 10 mA and employing the same dual ridge-horn antenna at the receiving end, we can detect a reasonable power (31.6 mu W) at 0.24 THz through 1 cm wireless transmission.
Toward UAV-Based Airborne Computing
(2019-12-01)
Lin, Kate Ching-Ju
;
Wang, Hao-Chen
;
Lai, Yuan-Cheng
;
Lin, Ying-Dar
;
交大名義發表
The next generation of mobile networks, 5G, aims at supporting lower end-to-end latency, higher reliability and higher throughput, which can be improved by MEC and multi-RAT offloading, respectively. With MEC, a base station in 5G can be equipped with computing power, which can be called as an edge in MEC. With the assistance of the edges, traffic with computational tasks can be directly executed in local servers, without forwarding the tasks to the cloud or core network. Edge computing hence reduces the latency and the traffic load significantly. Conventional multi- RAT offloading decides based only on either communication resources or computing resources. In this work, we argue that, to better utilize the communication and computing resources, neighboring edges should share their resources and cooperatively offload the requests from their clients. To this end, we introduced a double offloading mechanism, called LCCOP, to offload incoming traffic to the best pair of radio and edge subject to the endto- end latency of the requesting connections. We conduct simulations to compare LCCOP with the conventional offloading schemes. The results show that LCCOP's double offloading can significantly improve the satisfaction ratio by up to 83 percent and 143 percent, respectively, as compared to pure computation offloading and pure communication offloading.