A Machine Learning Approach for Power Allocation in HetNets Considering QoS

Amiri, Roohollah; Mehrpouyan, Hani; Fridman, Lex; Mallik, Ranjan K.; Nallanathan, Arumugam; Matolak, David W.

doi:10.1109/icc.2018.8422864

Cited by 94 publications

(59 citation statements)

References 18 publications

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“…Deep neural network can solve complex non-linear problems such as resource allocation, user association and resource management. A machine learning approach used for resource allocation in [112], works by rewarding the QoS for each femtocell and macrocell user. This helps to allocate power allocation and gain efficient energy more effectively as the environment changes dynamically.…”

Section: ) Hetnetsmentioning

confidence: 99%

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

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Section: ) Hetnetsmentioning

confidence: 99%

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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“…The authors in [26] propose a learning to optimize framework to accelerate the branch-and-bound algorithm for centralized interference channel binary power control. Considering the quality of service and user fairness, the authors in [22] propose a cooperative Q-learning based power allocation mechanism in HetNets. However, all of DRAFT July 3, 2019 SUBMITTED PAPER 5 the channel information is assumed to be known at BSs and sharing Q-values to reduce search time of the agents may raise additional signalling overhead issues.…”

Section: A Related Workmentioning

confidence: 99%

Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

Zhang

Nakhai

Zheng

et al. 2019

IEEE Trans. Commun.

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This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the

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“…More practical approach of bringing DL in IoT environment was verified and compared by in phases using library with basic building blocks for computing [10]. Roohollah et al [6] implemented Deep Learning in IoT by using existing solutions to address the challenges of energy consumption and deployment using labeled data. Xuyu et al defined Deep Learning framework [1] for radio frequency (RF) sensing in IoT and implementation using proposed framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

Bhaddurgatte¹,

P.²

2019

IJRTE

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Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

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A Machine Learning Approach for Power Allocation in HetNets Considering QoS

Cited by 94 publications

References 18 publications

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

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