A heterogeneous information fusion deep reinforcement learning for intelligent frequency selection of HF communication

Liu, Xin; Xu, Yuhua; Cheng, Yunpeng; Li, Yangyang; Zhao, Lei; Zhang, Xiaobo

doi:10.1109/cc.2018.8456453

Cited by 29 publications

(26 citation statements)

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“…RL based solutions have been extensively used in wireless communications to estimate the dynamic system model on the fly [98,99,100,101,102,103,104,105,106]. In the context of the physical layer, RL based solutions can extensively improve the system data rate, bit error rate, goodput (i.e., the amount of useful information that successfully arrived at the destination over the time-varying channel) and energy efficiency [107,108,109,110].…”

Section: Adaptive Rate and Power Controlmentioning

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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Section: Adaptive Rate and Power Controlmentioning

confidence: 99%

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

et al. 2019

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“…Intelligent learning algorithms have been applied to cope with the rapidly changing jamming environment via learning the pattern of the jamming [18]- [25]. A deep learning algorithm has been used in [18] for cognitive transmitters to decides whether to transmit or not and for the jammer to predict the next successful transmissions.…”

Section: Related Workmentioning

confidence: 99%

“…Above anti-jamming work does not consider HF communication environment. A heterogeneous information fusion deep reinforcement learning which considered the spectrum state and channel gain state was proposed in [25] under complex HF communication environment, in which the channel was time-varying and there existed malicious jamming.…”

Section: Related Workmentioning

confidence: 99%

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Joint Channel Selection and Data Scheduling in HF Jamming Environment: An Interference-Aware Reinforcement Learning Approach

Guo

et al. 2019

IEEE Access

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In this paper, we study the joint problem of multichannel selection and data scheduling for high-frequency (HF) communication under jamming environment. Prior anti-jamming work mainly discussed fixed transmission time and considered saturated scenarios in which the agent always had packets to transmit. But HF dynamic spectrum environment and time-varying communication demand make traditional anti-jamming methods ineffective. To cope with above challenge, dynamic transmission time and packet scheduling are considered in this manuscript. The transmitter selects the working channel and the number of packets to be transmitted according to current jamming environment and buffer state. Simultaneously, channel diversity which allows the transmitter to select multiple channels to send same packet is also considered to overcome the unreliable and unstable characteristics of HF channel. We formulate the decision making process as a Markov decision process (MDP) and propose an interference-aware reinforcement learning algorithm. The proposed algorithm combining Q learning with upper confidence bounds for trees (UCT) balances exploration and exploitation of action set. Simulation results show that higher network throughput and less packet loss are obtained by the proposed algorithm. INDEX TERMS Anti-jamming, channel selection, data scheduling, Q-learning, HF communications.

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“…With the development of artificial intelligence (AI) [3], communication devices are becoming increasingly intelligent [4]. Users are able to learn the jamming modes with the help of AI technologies, the probability of being jammed can be reduced by making the right decision, such as switching to idle communication frequencies [5] or adjusting the communication power [6].…”

Section: Introductionmentioning

confidence: 99%

On the Performance of Deep Reinforcement Learning-Based Anti-Jamming Method Confronting Intelligent Jammer

Wang

Liu

et al. 2019

Applied Sciences

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With the development of access technologies and artificial intelligence, a deep reinforcement learning (DRL) algorithm is proposed into channel accessing and anti-jamming. Assuming the jamming modes are sweeping, comb, dynamic and statistic, the DRL-based method through training can almost perfectly avoid jamming signal and communicate successfully. Instead, in this paper, from the perspective of jammers, we investigate the performance of a DRL-based anti-jamming method. First of all, we design an intelligent jamming method based on reinforcement learning to combat the DRL-based user. Then, we theoretically analyze the condition when the DRL-based anti-jamming algorithm cannot converge, and provide the proof. Finally, in order to investigate the performance of DRL-based method, various scenarios where users with different communicating modes combat jammers with different jamming modes are compared. As the simulation results show, the theoretical analysis is verified, and the proposed RL-based jamming can effectively restrict the performance of DRL-based anti-jamming method.

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A heterogeneous information fusion deep reinforcement learning for intelligent frequency selection of HF communication

Cited by 29 publications

References 0 publications

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

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

Joint Channel Selection and Data Scheduling in HF Jamming Environment: An Interference-Aware Reinforcement Learning Approach

On the Performance of Deep Reinforcement Learning-Based Anti-Jamming Method Confronting Intelligent Jammer

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