An Unsupervised-Learning-Based Method for Multi-Hop Wireless Broadcast Relay Selection in Urban Vehicular Networks

Song, Weinan; Zeng, Fanhui; Hu, Jingzhi; Wang, Zhijun; Mao, Xinyu

doi:10.1109/vtcspring.2017.8108458

Cited by 27 publications

(11 citation statements)

References 19 publications

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“…An important research field for new generation wireless networks is how to allocate resources between different users; in [32], it is suggested to use ANN for proactive resource allocation. In [33], an unsupervised learning based relay selection method is introduced for multi-hop networks.…”

Section: Introductionmentioning

confidence: 99%

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

et al. 2020

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Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights. INDEX TERMS Path loss, deep learning, convolutional neural networks.

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Section: Introductionmentioning

confidence: 99%

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

et al. 2020

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“…They proved that their learning algorithm outperformed the energy vector-based algorithm. Song et al [58], discussed how k-means clustering and its classification capabilities can aid in the selection of an efficient relay node for urban vehicular networks. The authors investigated different methods for multi-hop wireless broadcasting and how k-means can be a key factor in the decision-making of the BSs, by learning from the distribution of the devices and choosing automatically which is the most suitable device to be used as a relay.…”

Section: B Unsupervised Learning In 5g Mobile and Wireless Communications Technologymentioning

confidence: 99%

Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

2019

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Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.INDEX TERMS Machine learning, 5G mobile communication, B5G, wireless communication, mobile communication, artificial intelligence. II. THE THREE TYPES OF LEARNING AND ITS APPLICATION IN WIRELESS COMMUNICATIONSThe article is divided according to the level of supervision that the ML procedure requires on the training stage. The major categories discussed in the following sections are supervised, unsupervised, and reinforcement learning

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“…On the other hand, Unsupervised Learning focuses, in general, on clustering: the corresponding models are efficient in user grouping, BS or RN selection and QoS levels formulation, concerning RRM tasks [88]- [92].…”

Section: F Summary -Commentsmentioning

confidence: 99%

“…In other words, accuracy is the sum of True Positive (TP) and True Negative (TN) predictions, divided by the number of the total predictions (TP + TN + False Positive (FP) + False Negative (FN)). Then, F1-score is given by the following formula: [83], [84], [85], [86], [87] Unsupervised Learning RN or BS selection unsupervised k-NN, k-Means clustering variations [88] Unsupervised Learning user grouping, clustering, handover management k-NN, k-Means, Agg-GNN, f-test [89], [90], [91], [92] Reinforcement Learning subcarrier allocation, power control, frequency selection MDP, DRL, Water-Filling, WMMSE [93]. [94], [95], [96], [97] Reinforcement Learning minimization of difference between requested and active KPIs (throughput, SNIR, CSI)…”

Section: Simulations and Comparisonmentioning

confidence: 99%

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

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In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multipleinput-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

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An Unsupervised-Learning-Based Method for Multi-Hop Wireless Broadcast Relay Selection in Urban Vehicular Networks

Cited by 27 publications

References 19 publications

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

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