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On Optimizing Signaling Efficiency of Retransmissions for Voice LTE
(2020-01-01)
Hsu, Chia-An
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Lin, Kate Ching-Ju
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Ren, Yi
;
Tseng, Yu-Chee
;
資訊工程學系
The emergence of voice over LTE enables voice traffic transmissions over 4G packet-switched networks. Since voice traffic is characterized by its small payload and frequent transmissions, the corresponding control channel overhead would be high. Semi-persistent scheduling (SPS) is hence proposed in LTE-A to reduce such overhead. However, as wireless channels typically fluctuate, tremendous retransmissions due to poor channel conditions, which are still scheduled dynamically, would lead to a large overhead. To reduce the control message overhead caused by SPS retransmissions, we propose a new SPS retransmission protocol. Different from traditional SPS, which removes the downlink control indicators (DCI) directly, we compress some key fields of all retransmissions' DCIs in the same subframe as a fixed-length hint. Thus, the base station does not need to send this information to different users individually but just announces the hint as a broadcast message. In this way, we reduce the signaling overhead and at the same time, preserve the flexibility of dynamic scheduling. Our simulation results show that, by enabling DCI compression, our design improves signaling efficiency by 2.16x, and the spectral utilization can be increased by up to 60%.
ID-Free Multigroup Cardinality Estimation for Massive RFID Tags in IoT
(2019-01-01)
Lee, Tsu-Kuang
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Chen, Chih-Chieh
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Ren, Yi
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Lin, Cheng-Kuan
;
Tseng, Yu-Chee
The Internet of Things (IoT) has become the hottest in both the research community and industry. Among them, Radio Frequency Identification (RFID) plays a key role in IoT. On the RFID tags estimation problem, most existing researches are trying to identifying tags' ID rather than counting the number of tags. But the number of tags is useful information in many applications such as stock management and traffic flow management. Massive tags cause taking a lot of cost and time in the estimate. So an essential problem is how to quickly and accurately estimate the number of massive tags. In order to solve this problem, this paper proposes an accuracy and efficiency hybrid scheme by decreasing time and space complexity. The results of simulation conducted to test the effectiveness of the proposed approach, which matches well with the theoretical analytical model.
Design and Implementation of Image Electronic Fence with 5G technology for Smart Farms
(2019-01-01)
Hsu, Ching-Kuo
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Chiu, Yen-Hao
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Wu, Kun-Ru
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Liang, Jia-Ming
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Chen, Jen-Jee
The 5G era brings the rapid development of the Internet of Things (IoT). New technologies, such as image recognition, have promoted the traditional agriculture to a new milestone. Through advanced research and development of image recognition, it can intelligently monitor the growth of crops and effectively reduce agricultural damages, while avoiding crops being stolen and reducing the manpower of farms. In this paper, we design and implement an image electronic fence based on the technology of image recognition and sensor fusion with 5G technology for smart farms. By applying cameras and beacon tags, our system can identify whether the incoming/leaving people are authorized or not at the entry and exit of farms. In addition, with the high speed and low latency of 5G technology, the video data of cameras can be transmitted in a flash and fused with the beacon detection. In this way, the incoming/leaving people can be identified efficiently so as to avoid the farms being damaged. Based on field trials, we validate that the identification accuracy of our system approaches 90% in average.
Using V2X communications and data fusion to achieve lane-level positioning for road vehicles
(2020-01-01)
Lee, Tsu-Kuang
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Lin, Juyi
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Chen, Jen-Jee
;
Tseng, Yu-Chee
;
資訊工程學系
Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While global positioning system (GPS) is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using particle filter (PF), which is flexible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: controller area network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localisation with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.
Enhanced scheduling schemes with energy conservation for dynamic point selection in cloud radio access networks
(2020-02-01)
Hsu, Ching-Kuo
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Liang, Jia-Ming
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Wu, Kun-Ru
;
Chen, Jen-Jee
;
Tseng, Yu-Chee
In 5G mobile communications, Cloud-RAN (C-RAN) (Camps-Mur et al. in IEEE Commun Mag 57:99-105, 2019) is proposed to provide broadband services. It separates computation entities, i.e., baseband units (BBUs), from base stations and puts all BBUs in a centralized cloud. Existing researches have investigated how to enhance user equipments (UEs) to receive data from multiple collaborative cells by leveraging the dynamic point selection(DPS) technology. Collaborative transmission may cause higher energy consumption for UEs. Although 3GPP has defined the discontinuous reception (DRX) mechanism by regulating UEs to turn off their radio interfaces in a periodical manner, how to well coordinate DRX with DPS under the C-RAN architecture is still left as an open issue. This paper is the first one addressing this resource optimization problem in C-RAN with DPS, which asks how to optimize DRX parameters of UEs by considering their quality-of-service (QoS). We prove this problem to be NP-complete, and then propose two effective and efficient DPS solutions, called serving-ratio(SR) scheme and cost-aware (CA) scheme. SR serves UEs based on a special 'serving ratio' to ensure UEs receiving continuous subframes, especially for those in cell intersections. On the other hand, CA exploits the strategies of minimal scheduling costs to balance energy and throughput efficiency in a perspective way. Extensive simulation results validate that our schemes can successfully achieve higher system throughput, provide better resource utilization, and serve more UEs while guaranteeing their QoS and saving considerable energy as compared to existing schemes.
Augmenting Car Surrounding Information by Inter-Vehicle Data Fusion
(2019-01-01)
Lee, Tzu-Kuang
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Kuo, Yu-Chiao
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Huang, Shih-Hsuan
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Wang, Guan-Sheng
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Lin, Chih-Yu
Collecting vehicle surrounding information is a key issue for accident prevention and autonomous driving applications. Although GPS and 4G/LTE are widely accepted. it is still a challenge for a vehicle to get complete information of its surrounding vehicles. In this work, we consider the integration of multi-sensory data through V2V communications to help a vehicle to understand its complex surroundings. We propose a fusion algorithm that can integrate four types of sensory inputs: V2V communications, GPS, camera, and inertial data. We show that through such fusion, it is possible for a vehicle to visually see the driving states of its surrounding vehicles.
Demo: Tagging IoT Data in a Drone View
(2019-01-01)
Van, Lan-Da
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Chang, Chun-Hao
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Tong, Kit-Lun
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Wu, Kun-Ru
;
Zhang, Ling-Yan
Both cameras and IoT devices have their particular capabilities in tracking moving objects. Their correlations are, however, unclear. In this work, we consider using a drone to track ground objects. We demonstrate how to retrieve IoT data from devices, which are attached on human objects, and correctly tag them on the human objects captured by a drone view. This is the first work correlating IoT data and computer vision from a drone camera. Potential applications of this work include aerial surveillance, people tracking, and intelligent human-drone interaction.
Demo: A ROS-based Robot with Distributed Sensors for Seamless People Tracking
(2019-01-01)
Zhang, Ling-Yan
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Wu, Kun-Ru
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Ke, Ting-Yuan
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Wang, Chih-Hsiang
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Tseng, Yu-Chee
This paper presents a robot for people identification and tracking developed on robot operating system (ROS). It achieves modulized, light-weight, low-cost, and high-performance design goals even with the existence of distributed sensors. The key idea is to utilize wearable devices to enhance the people tracking capability of a robot through instant wireless communications and multi-sensory data fusion. Experimental results in a realistic environment demonstrate that our robot can keep tracking a specific person at a safe distance even without seeing the biological features of the person, who walks in a crowd with complex trajectories.
Hey! I Have Something for You: Paging Cycle Based Random Access for LTE-A
(2018-01-01)
Hsu, Chia-An
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Ren, Yi
;
Lin, Kate Ching-Ju
;
Tseng, Yu-Chee
;
資訊工程學系
The surge of M2M devices imposes new challenges for the current cellular network architecture, especially in radio access networks. One of the key issues is that the M2M traffic, characterized by small data and massive connection requests, makes significant collisions and congestion during network access via the random access (RA) procedure. To resolve this problem, in this paper, we propose a paging cycle-based protocol to facilitate the random access procedure in LTE-A. The high-level idea of our design is to leverage a UE's paging cycle as a hint to preassign RA preambles so that UEs can avoid preamble collisions at the first place. Our rpHint has two modes: (1) collision-free paging, which completely prevents cross-collision between paged user equipments (UEs) and random access UEs, and (2) collision-avoidance paging, which alleviates cross-collision. Moreover, we formulate a mathematical model to derive the optimal paging ratio that maximizes the expected number of successful UEs. This analysis also allows us to adapt dynamically to the better one between the two modes. We show via extensive simulations that our design increases the number of successful UEs in an RA procedure by more than 3x as compared to the legacy RA scheme of the LTE.
Three-Stage DRX Scheduling for Joint Downlink Transmission in C-RAN
(2020-02-01)
Liang, Jia-Ming
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Hsu, Ching-Kuo
;
Chen, Jen-Jee
;
Wu, Kun-Ru
;
Tseng, Yu-Chee
Cloud-RAN (C-RAN) is a promising network architecture to provide 5G broadband services. In C-RAN, the base stations (BSs) are separated as the computation entities (i.e., Baseband Units (BBUs)) and the radio entities (i.e., Remote Radio Heads (RRHs)), where the BBUs are put in a centralized cloud to realize centrally control and the RRH are left at cell sites for signal transmission. With C-RAN, multiple collaborative cells can transmit data to user equipments (UEs) by Joint Transmission (JT) technology to improve network efficiency. On the other hand, 3GPP standard has defined the Discontinuous Reception (DRX) mechanism, which allows UEs to turn off their radio interfaces periodically to save their energy. However, how to cooperate DRX with JT under the C-RAN architecture is still an open issue. Therefore, this letter studies how to optimize UEs' DRX parameters while considering their quality-of-service (QoS) in C-RAN with JT. To solve this problem, we propose an energy-efficient JT scheduling scheme. The main idea of our scheme is to well pair UEs and BSs with the consideration of joint transmission quality and then optimizes DRX parameters to further save energy. Extensive simulation results show that our scheme can improve system throughput, well utilize resource and save UEs' energy consumption.

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