An Overview of Machine Learning Approaches in Wireless Mesh Networks

Karunaratne, Samurdhi; Gacanin, Haris

doi:10.1109/mcom.2019.1800434

Cited by 22 publications

(9 citation statements)

References 22 publications

(33 reference statements)

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“…It was also verified that the proposed OL detector can yield an attractive performance for time-varying channels. As succeed in other communications areas [18]- [22], the use of machine-learning would be of attractive for the construction of future MIMO detectors.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs

2020

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We consider an uplink multiuser multiple-input multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). In this system, the construction of a low-complexity detector is quite challenging due to the non-linearity of an end-to-end channel transfer function. Recently, a supervisedlearning (SL) detector was proposed by modeling the complex non-linear function as a tractable Bernoullimixture model. It achieves an optimal maximum-likelihood (ML) performance, provided the channel state information (CSI) is perfectly known at a receiver. However, when a system-size is large, SL detector is not practical because of requiring a large amount of labeled data (i.e., pilot signals) to estimate the model parameters. We address this problem by proposing a semi-supervised learning (SSL) detector in which both pilot signals (i.e., labeled data) and some part of data signals (i.e., unlabeled data) are used to estimate them via expectation-maximization (EM) algorithm. We further extend the proposed detector for timevarying channels, by leveraging the idea of online learning, which is called online-learning (OL) detector. Simulation results demonstrate that the proposed SSL detector can achieve the almost same performance of the corresponding SL detector with significantly lower pilot overhead. In addition, it is shown that the proposed OL detector is more robust to channel variations compared with the existing detectors.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs

2020

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“…With the features of flexibility, low cost, scalability, multiple hops and so on, WMN will also play an important role in next-generation communication. With the fast grow and development of Internet of Things (IoT), WMN can well integrate with and enhance IoT [79] . The coverage will be extended and energy will be saved by the support of WMN.…”

Section: Opportunities and Technologies In Future Researchmentioning

confidence: 99%

The development of green wireless mesh network: A survey

Chai¹,

Zeng²

2021

JSEGC

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Wireless mesh network (WMN) is a type of self-healing, self-configuration, and peer-to-peer wireless network. Without expensive and fixed base stations, WMN can be established fast, easily, and flexibly at a low cost. The convenience and flexibility of establishing WMN brings a wide range of applications. The request of high bandwidth, high coverage and high transmission rate can be satisfied. Current research on WMN involves routing, resource allocation, mobility control, security and so on. With the increasing concern of green communication, improving energy efficiency is more and more important nowadays. As energy harvesting can overcome energy constraints and extend the network lifetime, it has attracted the great attention from many researchers when designing network. Further, WMN is an important networking mode in next-generation communication to guarantee the quality of service and reduce the networking complexity. Due to such importance, a survey of the research and development in WMN including the future research direction and opportunities is given in this paper.

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“…The scheme developed in [54] uses a machine learning technique to predict link quality, while the schemes presented in [55,56] adopt a deep learning model that uses traffic patterns in a router to predict the next node in the routing path. The scheme proposed in [57] uses a nonlinear regression technique to estimate link quality, while the scheme established in [58] uses machine learning to improve multihop wireless routing. To manage resources in a distributed computing environment, a reinforcement learning-based approach was developed in [59].…”

Section: Middleware Platforms For Distributed Computing Systemsmentioning

confidence: 99%

“…Link quality estimation service: An accurate estimation of link quality is essential for reliable communication. A link quality estimation service is responsible for using an online machine learning algorithm, such as that presented in [58], to predict link quality on the basis of network-level parameters, such as throughput, packet loss rate, and traffic volume.…”

Section: Machine Learning-based Resource Management Layermentioning

confidence: 99%

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Shah

2021

Sensors

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Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

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An Overview of Machine Learning Approaches in Wireless Mesh Networks

Cited by 22 publications

References 22 publications

Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs

Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs

The development of green wireless mesh network: A survey

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

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