Mobile hotspot network enhancement system for high-speed railway communication

Kim, Junhyeong; Chung, Hwan Seok; Choi, Sung Woong; Kim, Il Gyu; Han, Yanyan

doi:10.23919/eucap.2017.7928212

Cited by 26 publications

(6 citation statements)

References 3 publications

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“…In [7], several trackside mm-wave network architectures where deployed both orthogonal frequency-division multiplexing (OFDM) and single carrier (SC) technology were designed in order to overcome challenges in HSR scenarios. In addition, some higher mm-wave frequency bands, such as the 30GHz [24] and the 24-40GHz band [25] were studied to increase the transmission capacity to a greater extent in train-ground communications.…”

Section: Related Workmentioning

confidence: 99%

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Deep Reinforcement Learning Coordinated Receiver Beamforming for Millimeter-Wave Train-ground Communications

Zhou¹,

Zhang²,

Chen³

et al. 2022

Preprint

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As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Qnetwork (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway.

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Section: Related Workmentioning

confidence: 99%

“…First, we replace the value function approximation is used to define the loss function as shown in (25).…”

Section: B Rx Beamforming Based On Dqnmentioning

confidence: 99%

Deep Reinforcement Learning Coordinated Receiver Beamforming for Millimeter-Wave Train-ground Communications

Zhou¹,

Zhang²,

Chen³

et al. 2022

Preprint

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“…28,29 Two vehicular cloud communication scenarios are considered. The first one is the vehicular cloud communication through Hotspot 30 and the second one is the vehicular cloud communication through cellular networks. 31 The communication standard for the Hotspot is IEEE 802.11u 32 and the communication standard for the cellular network is 3G, 4G, 5G, 6G, LTE, and so forth.…”

Section: Communication Modelmentioning

confidence: 99%

A heterogeneous fault diagnosis approach to enhance performance of connected vehicles

Senapati

Swain

et al. 2022

Int J Communication

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Evolution of wireless access technology, availability of smart sensors, and reduction in the size of the set up of the communication system have engrossed many researchers toward vehicular ad hoc network (VANET).Vehicle-to-vehicle and vehicle-to-access-point communication in a vehicular environment facilitates the deployment of VANET for many different purposes. The success of any application implemented in a VANET relies on timely and accurate data dissemination across the nodes of the network. Implementation of any application is not going to be fruitful if the communication unit transmits incorrect sensor data due to the presence of a fault. This article focuses on the automatic detection of hard and soft faults for vehicular sensors and the classification of faults into permanent, intermittent, and transient faults using cloud-based VANET. For the cloud service, ThingSpeak cloud is used. At the RSU of the VANET, hard fault detection is performed, and for this purpose, a time-out strategy is proposed. The observation center, after receiving sensor status data over a vehicular cloud, does soft failure detection. The soft fault is identified by utilizing a comparative-based technique during soft fault diagnosis. Soft faults are categorized using two machine learning algorithms: Support vector machine and logistic regression. The effectiveness of the suggested work is assessed using performance metrics like fault detection accuracy, false alarm rate, false positive rate, precision, accuracy, recall, and F1 score.

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“…The MHN system was initially designed for providing TE at speeds of up to 400 km/h with a broadband MWB. However, in order to meet mobility requirement of 500 km/h, one of the key requirements of IMT‐2020, we conducted a study on the numerology for an MHN‐E system under various numerology sets and 3GPP channel models . From this study, we observed that in the case of higher modulation, such as 64‐QAM and 256‐QAM, at speeds of up to 500 km/h, numerology sets with subcarrier spacing greater than or equal to 120 kHz could achieve reasonable performance.…”

Section: Key Enabling Technology: Architecture Of High‐speed Train Comentioning

confidence: 99%

5GCHAMPION - Disruptive 5G Technologies for Roll-Out in 2018

Strinati¹,

Mueck²,

Clemente³

et al. 2018

ETRI Journal

Self Cite

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The 5GCHAMPION Europe–Korea collaborative project provides the first fully‐integrated and operational 5G prototype in 2018, in conjunction with the 2018 PyeongChang Winter Olympic Games. The corresponding technological advances comprise both an evolution and optimization of existing technological solutions and disruptive new features, which substantially outpace previous generations of technology. In this article, we focus on a subset of three disruptive technological solutions developed and experimented on by 5GCHAMPION during the 2018 PyeongChang Olympic Games: high speed communications, direct satellite‐user equipment communications, and post‐sale evolution of wireless equipment through software reconfiguration. Evaluating effectiveness and performing trials for these key 5G features permit us to learn about the actual maturity of 5G technology prototyping and the potential of new 5G services for vertical markets and end user enhanced experience two years before the launch of large‐scale 5G services.

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Mobile hotspot network enhancement system for high-speed railway communication

Cited by 26 publications

References 3 publications

Deep Reinforcement Learning Coordinated Receiver Beamforming for Millimeter-Wave Train-ground Communications

Deep Reinforcement Learning Coordinated Receiver Beamforming for Millimeter-Wave Train-ground Communications

A heterogeneous fault diagnosis approach to enhance performance of connected vehicles

5GCHAMPION - Disruptive 5G Technologies for Roll-Out in 2018

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