Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Jang, Sung-Jeen; Han, Chulhee; Lee, Kwang-Eog; Yoo, Sang‐Jo

doi:10.1186/s13638-019-1433-1

Cited by 28 publications

(15 citation statements)

References 21 publications

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“…This dataset consists of 24 different types of attacks that are divided into four groups: DDOS, Probe, U2R, and R2L. According to some studies [30][31][32], the filtered 10% KDDCUP99 data described as shown in Table 5 is used when dealing with lots of network intrusion detection issues. In the experiments, the dataset is partitioned into two parts: 50% of data is utilized as training data and the remaining 50% data is considered as testing data.…”

Section: Kddcup99 Datamentioning

confidence: 99%

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Entropy clustering-based granular classifiers for network intrusion detection

Liu

Hao

Xing³

2020

J Wireless Com Network

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Support vector machine (SVM) is one of the effective classifiers in the field of network intrusion detection; however, some important information related to classification might be lost in the reprocessing. In this paper, we propose a granular classifier based on entropy clustering method and support vector machine to overcome this limitation. The overall design of classifier is realized with the aid of if-then rules that consists of a premise part and conclusion part. The premise part realized by the entropy clustering method is used here to address the problem of a possible curse of dimensionality, while the conclusion part realized by support vector machines is utilized to build local models. In contrast to the conventional SVM, the proposed entropy clustering-based granular classifiers (ECGC) can be regarded as an entropy-based support function machine. Moreover, an opposition-based genetic algorithm is proposed to optimize the design parameters of the granular classifiers. Experimental results show the effectiveness of the ECGC when compared with some classical models reported in the literatures.

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Section: Kddcup99 Datamentioning

confidence: 99%

“…In the experiments, the dataset is partitioned into two parts: 50% of data is utilized as training data and the remaining 50% data is considered as testing data. Moreover, in order to compare with other models, we also used the existing performance index [27][28][29][30][31][32]:…”

Section: Kddcup99 Datamentioning

confidence: 99%

Entropy clustering-based granular classifiers for network intrusion detection

Liu

Hao

Xing³

2020

J Wireless Com Network

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“…It estimates channel state for the next time instant and uses this estimation to rank and select the operating channel in clustering. In [20], the optimal band and channel selection mechanism for cluster-based CRN is proposed. Based on each MN's reporting in terms of spectrum sensing and traffic demand, CH determines cluster's operating band and channel using Q-learning.…”

Section: Related Workmentioning

confidence: 99%

Q-Learning Based Multi-Objective Clustering Algorithm for Cognitive Radio Ad Hoc Networks

Hossen

Yoo

2019

IEEE Access

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Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve radio operating behavior. Due to the dynamic nature of spectrum availability and wireless channel condition, it is very hard to maintain reliable network connectivity. Cluster-based CR ad-hoc networks (CRAHN) arrange CR nodes into groups to effectively maintain reliable autonomous networks. Clustering in CRAHN supports cooperative tasks such as spectrum sensing and channel managements and achieves network scalability and stability. In this paper, we proposed a Q-learning based cluster formation approach in CRAHN, in which Q-value is used to evaluate each node's channel quality. To form a distributed cluster network, channel quality, residual energy and neighbor node/network conditions are considered. By exchanging each node's status information in terms of channels and neighbors, each node knows neighboring topology and which node is the best candidate for cluster head (CH). Distributed CH selection, the optimum common active data channel decision, and gateway node selection procedures are presented in this paper. The proposed mechanism can extend the network lifetime, enhance the reachability not only between member nodes but also with other cluster networks, it can also provide stable and reliable service using the selected data channel and avoid possible interference between neighboring ad-hoc clusters. INDEX TERMS Reinforcement learning, clustering, cognitive radio, Q-learning, ad-hoc network.

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“…In [12], the authors proposed the use of a Q-learning based dynamic optimal band and channel selection scheme, which took into account the surrounding wireless environments and system demands in order to maximize the available transmission time and capacity at the given time and geographic area. The simulation results showed that their solution dynamically chose a band and channel suitable for the required data rate and operated properly according to the desired system performance.…”

Section: Related Workmentioning

confidence: 99%

SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge

et al. 2019

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DARPA, the Defense Advanced Research Projects Agency from the United States, has started the Spectrum Collaboration Challenge with the aim to encourage research and development of coexistence and collaboration techniques of heterogeneous networks in the same wireless spectrum bands. Team SCATTER has been participating in the challenge since its beginning, back in 2016. SCATTER’s open-source software defined physical layer (SCATTER PHY) has been developed as a standalone application, with the ability to communicate with higher layers through a set of well defined messages (created with Google’s Protocol buffers) and that exchanged over a ZeroMQ bus. This approach allows upper layers to access it remotely or locally and change all parameters in real time through the control messages. SCATTER PHY runs on top of USRP based software defined radio devices (i.e., devices from Ettus or National Instruments) to send and receive wireless signals. It is a highly optimized and real-time configurable SDR based PHY layer that can be used for the research and development of novel intelligent spectrum sharing schemes and algorithms. The main objective of making SCATTER PHY available to the research and development community is to provide a solution that can be used out of the box to devise disruptive algorithms and techniques to optimize the sub-optimal use of the radio spectrum that exists today. This way, researchers and developers can mainly focus their attention on the development of smarter (i.e., intelligent algorithms and techniques) spectrum sharing approaches. Therefore, in this paper, we describe the design and main features of SCATTER PHY and showcase several experiments performed to assess the effectiveness and performance of the proposed PHY layer.

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Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Cited by 28 publications

References 21 publications

Entropy clustering-based granular classifiers for network intrusion detection

Entropy clustering-based granular classifiers for network intrusion detection

Q-Learning Based Multi-Objective Clustering Algorithm for Cognitive Radio Ad Hoc Networks

SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge

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