Machine learning based obstacle detection for Automatic Train Pairing

Sattiraju, Raja; Kochems, Jacob; Schotten, Hans D.

doi:10.1109/wfcs.2017.7991962

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…The self-sustaining and proactive wireless networks respond more robustly to channel distortions and efficient approaches to the physical layer. These are efficient advances for the physical layer in MIMO system approval for making real-time network decisions and achieving a big data rate when their capacity is proportional to the complexity based on training data under channel nonlinearity [143]- [145].…”

Section: A Supervised Learningmentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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Section: A Supervised Learningmentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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“…First, at the physical layer, AI and machine learning techniques have been shown to improve channel coding [28], ranging and obstacle detection [29], and physical layer security [30]. Research in each of these domains is still in a preliminary stage and requires further investigations.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

The Role of Blockchain in 6G: Challenges, Opportunities and Research Directions

Hewa

Gûr

Kalla

et al. 2020

2020 2nd 6G Wireless Summit (6G SUMMIT)

188

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The world is going through a fundamental transformation with the emergence of the intelligent information era.The key domains linked with human life such as healthcare, transport, entertainment, and smart cities are expected to elevate the quality of service with high-end user experience. Therefore, the telecommunication infrastructure has to meet unprecedented service level requirements such as ultra high data rates and traffic volume for the prominent future applications such as Virtual Reality (VR), holographic communications, and massive Machine Type Communications (mMTC). There are significant challenges identifiable in the communication context to match the envisaged demand surge. The blockchain and distributed ledger technology is one of the most disruptive technology enablers to address most of the current limitations and facilitate the functional standards of 6G. In this work, we explore the role of blockchain to address formidable challenges in 6G, future application opportunities and potential research directions.

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“…Initially applied to upper layers [4], ML has recently found applications also at the PHY Layer [5]- [7] such as channel coding [8]- [10], modulation recognition [7], obstacle detection [11], [12] and physical layer security [13] etc. The use of ML for channel estimation was initially investigated in works such as [14].…”

Section: A Related State Of the Artmentioning

confidence: 99%

Channel Estimation in C-V2X using Deep Learning

Sattiraju¹,

Weinand²,

Schotten³

2020

Preprint

Self Cite

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Channel estimation forms one of the central component in current Orthogonal Frequency Division Multiplexing (OFDM) systems that aims to eliminate the inter-symbol interference by calculating the Channel State Information (CSI) using the pilot symbols and interpolating them across the entire timefrequency grid. It is also one of the most researched field in the Physical Layer (PHY) with Least-Squares (LS) and Minimum Mean Squared Error (MMSE) being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on Convolutional Neural Networks (CNNs) for channel estimation in vehicular environments used in 3GPP Rel.14 Cellular-Vehicle-to-Everything (C-V2X) technology. To this end, we compare the performance of the proposed Deep Learning (DL) architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy C-V2X channel estimation methods especially at high mobile speeds.

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Machine learning based obstacle detection for Automatic Train Pairing

Cited by 14 publications

References 9 publications

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

The Role of Blockchain in 6G: Challenges, Opportunities and Research Directions

Channel Estimation in C-V2X using Deep Learning

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