Deep Reinforcement Learning for Joint Channel Selection and Power Allocation in Cognitive Internet of Things

Zheng, Wei; Wu, Guoqing; Qie, Wenbo; Zhang, Yong

doi:10.1007/978-3-030-37429-7_69

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…In order to solve the problem of resource allocation in complex communication network scenarios, we propose a reinforcement learning architecture to solve the problem of resource allocation optimization in communication networks. The existing reinforcement learning algorithms applied to resource allocation are mainly divided into distributed multi-agent reinforcement learning algorithm [5] and centralized single agent reinforcement learning algorithm [27,25]. The centralized algorithm needs global information, has better utility value, and can balance the whole network users.…”

Section: Related Workmentioning

confidence: 99%

“…Distributed algorithm only needs to know local information, so it has less communication cost. [27] proposes a centralized reinforcement learning algorithm based on DQN, which uses underlay access mode to maximize the spectrum efficiency of secondary users under the interference temperature limit acceptable for the primary user. But the network model of [27] does not consider network slicing.…”

Section: Related Workmentioning

confidence: 99%

“…[27] proposes a centralized reinforcement learning algorithm based on DQN, which uses underlay access mode to maximize the spectrum efficiency of secondary users under the interference temperature limit acceptable for the primary user. But the network model of [27] does not consider network slicing. [5] proposes a distributed reinforcement learning algorithm based on Q-Learning and SARSA.…”

Section: Related Workmentioning

confidence: 99%

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Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

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With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on under1 lay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

Self Cite

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“…In IoT networks, IoT devices can use Reinforcement Learning to make judgments based on inference under dynamic and uncertain network conditions. For example, RL has been utilized in cognitive radio networks during spectrum sharing for channel access between the primary users and secondary users [85][86][87].…”

Section: Deep Reinforcement Learning (Drl)mentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

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The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

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On-Line Learning-Based Allocationof Base Stations and Channels in Cognitive Radio Networks

Liu

et al. 2020

Lecture Notes in Computer Science

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Deep Reinforcement Learning for Joint Channel Selection and Power Allocation in Cognitive Internet of Things

Cited by 3 publications

References 14 publications

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

On-Line Learning-Based Allocationof Base Stations and Channels in Cognitive Radio Networks

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