The Communication Relationship Discovery Based on the Spectrum Monitoring Data by Improved DBSCAN

Liu, Changkun; Wu, Xinrong; Zhu, Lei; Yao, Changhua; Yu, Lian; Wang, Lei; Tong, Wei; Pan, Tongyan

doi:10.1109/access.2019.2938296

Cited by 14 publications

(12 citation statements)

References 37 publications

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“…Generally, radio stations usually adopt half-duplex communication, the quality of communication is susceptible to environmental influences. In order to improve the communication quality and prevent the loss of packets or frames during transmission, error control mechanism is usually adopted by various protocols, e.g., the stop-and-wait automatic repeat request (ARQ) mechanism [19]. The process of information interaction is shown in Fig.…”

Section: Information Interaction Modelmentioning

confidence: 99%

“…As shown in Fig. 4, the In the process of spectrum analysis, the method proposed in the paper [19] is used as a reference, set the scan cycle s = 1/3w to scan the spectrum signal, where w is the frequency hopping period of signals. If a signal is continuously transmitted during the scan cycle, the corresponding value in the time series is set to the ID of the radio station, otherwise it is set to 0.…”

Section: A Analysis Of Communication Rulementioning

confidence: 99%

“…However, the assumption of this method is too ideal and the localization is not accurate enough in practical scenarios. Moreover, for the non-cooperative communication relationship inference, Changkun Liu et al have intensively explored this direction from the perspective of spectrum analysis, and proposed multiple solutions based on communication rules and clustering [19]. However, during data mining, the signal strength, i.e, the measured power is selected as a key metric of speculating the communication relationship, which is prone to being interfered by the environment.…”

Section: Introductionmentioning

confidence: 99%

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Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

Niu

Shu

et al. 2020

IEEE Access

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With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption. INDEX TERMS Internet of Things (IoT), green, localization, communication relationship inference.

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Section: Information Interaction Modelmentioning

confidence: 99%

Section: A Analysis Of Communication Rulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

Niu

Shu

et al. 2020

IEEE Access

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“…There is almost no existing research on non-collaborative radio station communication behaviors due to many difficulties in the field. Previous studies [ 3 , 4 ] explored communication relationships between radio stations by analyzing the intercepted radio signals. However, these communication relationships are not our focus.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Chen

Lei

2020

Sensors

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This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

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“…The link establishment behaviors of a short-wave radio station belong to communication behaviors. Liu et al [3], [4] can accurately find the communication relationship between wireless radio stations from the monitored physical layer signals, and there is no need to crack the information carried by signals. Xiang et al [5] proposed an algorithm to mine command information flow under the condition that communication information is unknown between nodes in the communication network.…”

mentioning

confidence: 99%

Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

Chen

Lei

et al. 2020

IEEE Access

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It is difficult to recognize Automatic Link Establishment (ALE) behaviors of a shortwave radio station, if we do not acquire the radio station's communication protocol standard. A method is proposed to recognize different ALE behaviors by using an improved unidimensional DenseNet. In this work, we directly recognize ALE signals in physical layer without the radio station's communication protocol standard. Hence, we can avoid difficulties in demodulation, decryption and so on. Actually, the original DenseNet is used extensively in the field of computer vision, so the original DenseNet is firstly adapted for the unidimensional input. And then, two parallel dense blocks are used in our improved unidimensional DenseNet, which could improve the capability of network to extract ALE signals' deep features. The experimental results show that the proposed method is able to recognize different ALE behaviors of a shortwave radio station. And improved DenseNet has better recognition performance than simple DenseNet. The simple DenseNet only contains one dense block. Finally, the results of comparison experiments also show that some classic networks have worse performance in ALE behaviors recognition, such as LeNet-5, ResNet-34, and DenseNet-121. INDEX TERMS Recognition, unidimensional DenseNet, automatic link establishment, shortwave radio station, electronic countermeasure. I. INTRODUCTION The ALE behaviors of a shortwave radio station include Call, Handshake, Notification, Time Offset, Group Time Broadcast, Broadcast and Scanning Call. The Link Establishment (LE) behaviors recognition of a shortwave radio station is important and difficult in the field of electronic countermeasures. Mastering the ALE behaviors of enemy's radio station can effectively infer the communication intention and working status of the radio station in the wireless communication network. It provides a reference for further inferring the topological structure and tactical position of the shortwave radio station. As a reconnoiter in the field of electronic countermeasures, our goal is to recognize a radio station's ALE behaviors without the radio station's communication protocol (MIL-STD-188-141B) [1]. In this work, the purpose of ALE behaviors recognition is achieved by directly analyzing the signals in physical layer, avoiding The associate editor coordinating the review of this manuscript and approving it for publication was Miguel López-Benítez .

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The Communication Relationship Discovery Based on the Spectrum Monitoring Data by Improved DBSCAN

Cited by 14 publications

References 37 publications

Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

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