Browsing by Author "Chen, Jen-Yeu"

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A Utility-based Self-Configuration Scheme for Macro-Small Cellular Systems
(2016)
Li, Cheng-Han
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Chen, Jen-Yeu
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Chang, Chung-Ju
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電機工程學系
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Department of Electrical and Computer Engineering
A heterogeneous macro-small cellular network architecture where some small cells are deployed within the coverage of a macrocell is widely considered a promising and practical solution for future 5G wireless communication network. More and more small-cell base stations (SBSs) are deployed in the LTE-A macrocell system to achieve a higher data rate and a better spectrum efficiency. As there could be a large number of SBSs which may together consume a considerable amount of energy, it becomes an important issue to appropriately manage these SBSs for power saving, i.e., to reach a better energy efficiency, upon achieving a certain spectrum efficiency. In this paper, we propose a utility-based self-configuration (USC) scheme in a macro-small cellular system to improve both the spectrum and the energy efficiencies. By the USC scheme, SBSs will adequately switch their operation modes based on a utility function considering both the SBSs\' statuses and user equipment\'s (UE) information. Simulation results show that the proposed USC scheme can achieve better system performances in terms of system throughput and system energy efficiency than a representative SBS power saving scheme in the literature, namely, traffic-aware power saving (TAPS) scheme, and the conventional scheme that all SBSs operate without power saving management.
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.
Machine Learning Based Rapid 3D Channel Modeling for UAV Communication Networks
(2019-01-01)
Wang, Jing-Ling
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Li, Yun-Ruei
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Adege, Abebe Belay
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Wang, Li-Chun
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Jeng, Shiann-Shiun
This paper applies Machine Learning (ML) to predict the quality of Air-to-Ground (A2G) links performance for Unmanned Aerial Vehicles Base Stations (UAV-BSs) services. UAV-BSs can instantly identify the status of the current 3D wireless channel in an unknown environment without relying on previous statistical channel modeling. The proposed method that employs the unsupervised learning clustering technology applying to A2G channel modeling in 3D wireless communication scenarios. As environment changing, the proposed method can derive the 3D temporary channel model based on collected RSS data and analyzing. To evaluate the proposed method, the simulation data and measurement data are used to co-verify the performance. As the results shown, the RMSE of conventional statistical channel model and proposed temporary channel model are very similar. The similarity achieves about 91.8% both of the simulation and experimental environments to verify the accuracy and feasibility of our proposed method, and that provides more fast and effective of 3D channel modeling approach.
TAPS: Traffic-Aware Power Saving Scheme for Clustered Small Cell Base Stations in LTE-A
(2015)
Lin, Kuan-Yu
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Chen, Jen-Yeu
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Ren, Fang-Ching
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Chang, Chung-Ju
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電機工程學系
To meet the soaring demand of high data rate from today\'s applications of mobile Internet, small cells would be widely deployed to achieve higher data rate and better spectral efficiency in 4G and B4G/5G cellular networks. However, when the network traffic load is low, a large number of small-cell base stations could incur a tremendous energy waste. To overcome this pitfall, in this paper, we propose a traffic-aware power saving scheme (TAPS) by which a small-cell base station can adequately switch its operation mode -- namely, normal, sniff and low-duty modes -- according to its traffic load to reduce its power consumption. Simulation results show that TAPS can save a large amount of energy but only slightly hamper system performance compared with the case without power-saving scheme.