A learning-based channel model for synergetic transmission technology

Chen, Liang; Li, Haihan; Li, Yunzhou; Zhou, Shidong; Jing, Wang

doi:10.1109/cc.2015.7275262

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Then, the path loss values can be calculated in the offline post-processing and mapped to locations. More details of the equipment can be found in [28].…”

Section: Comparison Of Different Modelsmentioning

confidence: 99%

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Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

et al. 2019

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Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to verify the feasibility of these data expansion mechanisms. Finally, some issues for future research are discussed.

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“…Then, the path loss values can be calculated in the offline post-processing and mapped to locations. More details of the equipment can be found in [28].…”

Section: Comparison Of Different Modelsmentioning

confidence: 99%

“…Then, the path loss values can be calculated in the offline post-processing and mapped to locations. More details of the equipment can be found in[28].The measurement routes and the position of the TD-SCDMA BS are illustrated in Figure3. The car moved from the south (sample index 1) to the north (sample index 517), and then turned to the west direction.…”

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confidence: 99%

Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

et al. 2019

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“…Increasing the big data rate exponentially is based on enabling full channel state information (CSI), where channel modeling is comprehensively discovered through transmitting information between transmitting and receiving antennas [98]. Moreover, the application of ML for synergetic transmission and interference controlling allows for the prediction, enhancement, and management of CSI, which will appreciably decrease the pilot overhead for achieving CSI [99].…”

Section: ) Wireless Channel Modeling For 6gmentioning

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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“…Naturally, computer science is penetrated to communication from high efficient spectrum utilization to novel service type and traffic forecast [19] [20]. [21] uses machine learning to predict channel state information in order to decease the pilot overhead. Especially for 5G, the "Big Data" appears and its related techniques are applied to conventional communication research in order to realize "5G vision".…”

Section: Jianhua Zhangmentioning

confidence: 99%

The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model

Yuan¹,

Li²,

Bhushan³

et al. 2016

China Commun.

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Recently, internet stimulates the explosive progress of knowledge discovery in big volume data resource, to dig the valuable and hidden rules by computing. Simultaneously, the wireless channel measurement data reveals big volume feature, considering the massive antennas, huge bandwidth and versatile application scenarios. This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication, especially for 5th Generation (5G) and beyond. Considering the big data research progress, then a cluster-nuclei based model is proposed, which takes advantages of both the stochastical model and deterministic model. The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects. Combining the channel properties variation principles with antenna size, frequency, mobility and scenario dug from the channel data, the proposed model can be expanded in versatile application to support future mobile research.

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A learning-based channel model for synergetic transmission technology

Cited by 7 publications

References 12 publications

Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

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

The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model

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