A complete reverse engineering (or blind identification) in an electronic battlefield determines the information conveyed by a received signal. Most of the research in the field of blind signal identification is around one-way and non-network communications in which the goal is to determine the information transmitted by a single transmitter. The first step of signal identification in communications networks is to determine the number of active users. In this paper, estimation of the number of users in a time-division multiple access (TDMA) network is considered. In order to estimate the number of users, a physical layer analysis can be applied to the received electromagnetic signals. However, due to some difficulties such as hardware limitations or closeness of active users, this method cannot always be employed. In these situations, a solution is to analyze the information in the upper layers of the network. In this paper, a method is proposed to estimate the number of active users using the redundant data generated by adaptive channel coding. Simulation results show that the proposed method is quite resistant against channel errors. In fact, the accuracy of the proposed method for signal to noise ratio of 7.3 dB is around 80%.
Ghayoori Sales, M., Bazdar, G., & Sarkardeh, A. (2018). Introduction of the Entropy-Based Method for Finding Influential Nodes in Information Dissemination on Online Social Networks. Electronic and Cyber Defense, 6(2), 1-10.
MLA
Majid Ghayoori Sales; Gholamreza Bazdar; Abolfazl Sarkardeh. "Introduction of the Entropy-Based Method for Finding Influential Nodes in Information Dissemination on Online Social Networks". Electronic and Cyber Defense, 6, 2, 2018, 1-10.
HARVARD
Ghayoori Sales, M., Bazdar, G., Sarkardeh, A. (2018). 'Introduction of the Entropy-Based Method for Finding Influential Nodes in Information Dissemination on Online Social Networks', Electronic and Cyber Defense, 6(2), pp. 1-10.
VANCOUVER
Ghayoori Sales, M., Bazdar, G., Sarkardeh, A. Introduction of the Entropy-Based Method for Finding Influential Nodes in Information Dissemination on Online Social Networks. Electronic and Cyber Defense, 2018; 6(2): 1-10.
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