A Q-Learning-Based Channel Selection and Data Scheduling Approach for High-Frequency Communications in Jamming Environment

Li, Wen; Xu, Yuhua; Guo, Qiuju; Zhang, Yuli; Liu, Dianxiong; Li, Yangyang; Bai, Wei

doi:10.1007/978-3-030-32388-2_13

Cited by 7 publications

(15 citation statements)

References 25 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig. 3, the states in t 1 (the black solid line) and t 2 (the black dotted line) are same in [32].…”

Section: ) System Statementioning

confidence: 84%

“…The detailed simulation parameters are shown in Table 1. The performance of the proposed algorithm is compared with the sensing-based algorithm and the the Q-learning-based algorithm in our previous conference paper [32] (QLinConf).…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…• The joint problem of multichannel selection and data scheduling in jamming environment is formulated as a MDP problem. The system state which is different from our previous work [32] contains current jamming channel, jammed duration on current jamming channel, and current buffer length. The action of the agent includes the communication channels and the number of packets to be transmitted.…”

Section: Introductionmentioning

confidence: 93%

“…Different from our previous work [32] in which the system state contains the communications channel in last transmission, the current jamming channel and current buffer length, this paper adds the jammed duration on current jamming channel to the state. As shown in Fig.…”

Section: ) System Statementioning

confidence: 99%

“…A preliminary version of this work was [32] and the extensions of this paper are concluded as follows: 1) Multichannel selection based on channel diversity was analyzed in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Guo

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…As shown in Fig. 3, the states in t 1 (the black solid line) and t 2 (the black dotted line) are same in [32].…”

Section: ) System Statementioning

confidence: 84%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: ) System Statementioning

confidence: 99%

“…A preliminary version of this work was [32] and the extensions of this paper are concluded as follows: 1) Multichannel selection based on channel diversity was analyzed in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Guo

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach

Huang

Xu²,

Chen

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In this article, a joint relay and channel selection problem is investigated in multi‐relay anti‐jamming communication system. Considering the jamming pattern and the relay node (RN) location distribution are unknown, the relay and channel selection problem is formulated as Markov decision processes (MDPs). Different from the existing research on anti‐jamming communication, in this article, the source node (SN) and all RNs are considered as agents who collaboratively learn the environment and make anti‐jamming decisions. A reinforcement‐learning‐based joint relay and channel selection method is proposed to achieve relay‐aided anti‐jamming communication. Specifically, the SN tries to make the optimal selection of relay which is under the least jamming threat, while the RN will access the jamming‐free channels. Various simulation results show that the algorithm can quickly learn the unknown changing pattern of the jamming environment, and make the effective joint relay and channel decisions to obtain high communication throughput which is close to the optimal.

show abstract

Survey on physical layer security for 5G wireless networks

et al. 2020

View full text Add to dashboard Cite

Physical layer security is a promising approach that can benefit traditional encryption methods. The idea of physical layer security is to take advantage of the propagation medium's features and impairments to ensure secure communication in the physical layer. This work introduces a comprehensive review of the main information-theoretic metrics used to measure the secrecy performance in physical layer security. Furthermore, a theoretical framework related to the most commonly used physical layer security techniques to improve secrecy performance is provided. Finally, our work surveys physical layer security research over several enabling 5G technologies, such as massive multiple-input multiple-output, millimeter-wave communications, heterogeneous networks, non-orthogonal

show abstract

A Q-Learning-Based Channel Selection and Data Scheduling Approach for High-Frequency Communications in Jamming Environment

Cited by 7 publications

References 25 publications

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

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

Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach

Survey on physical layer security for 5G wireless networks

Contact Info

Product

Resources

About