Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach

Li, Xingjian; Fang, Jun; Cheng, Wu; Huang, Duan; Chen, Zhi; Li, Hongbin

doi:10.1109/access.2018.2831240

Cited by 169 publications

(100 citation statements)

References 36 publications

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“…[24] adopted DRL to learn the jamming pattern in a dynamic and intelligent jamming environment and proposed an efficient algorithm to obtain the optimal anti-jamming strategy. [25] adopted DRL to learn the power adaption strategy of the primary user in a cognitive network, such that the secondary user is able to adaptively control its power and satisfy the required quality of services of both primary and secondary users. [26] studied the handover problem in a multi-user multi-BS wireless network and proposed a DRL-based handover algorithm to reduce the handover rate of each user under a minimum sum-throughput constraint.…”

Section: B Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Zhang

Tan

Liang

et al. 2019

IEEE Trans. Wireless Commun.

119

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We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by MCS switchings, we further introduce a switching cost factor in the proposed algorithm. Simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ∼ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, and is 30% ∼ 100% higher than those of the benchmark algorithms. Meanwhile, the proposed algorithm with the switching cost factor can achieve a better balance between the primary transmission rate and system overheads than both the optimal algorithm and benchmark algorithms.

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Section: B Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Zhang

Tan

Liang

et al. 2019

IEEE Trans. Wireless Commun.

119

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show abstract

“…With the prevalence of IoT and smart mobile devices, mobile crowdsensing becomes a cost-effective solution for network information collection to support more intelligent operations of wireless systems. The authors in [156] consider spectrum sensing and power control in non-cooperative cognitive radio networks. There is no information exchange between PUs and SUs.…”

Section: Power Control and Data Collectionmentioning

confidence: 99%

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,401

481

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This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

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“…Li [142] RL ALOHA-like spectrum access Naparstek and Cohen [199] DQN wt LSTM ALOHA-linke spectrum access Li et al [200] DQN Intelligent power control Yu et al [202] DQN Non-coopertive heterogenous network Yu et al [204] DQN wt RN Non-coopertive heterogenous network of the application and can be classified in several ways. Some of these classifications include geographical location based routing [206,207,208,209], hierarchical [210], QoS-based [211,212], and recently cross-layer optimized routing [213,214,69,215,216,217].…”

Section: Mac Protocol ML Algorithm Objectivementioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach

Cited by 169 publications

References 36 publications

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

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