Estimating the number of users in TDMA networks based on redundancy of adaptive channel coding
Blind identification of protocols in wireless communication networks is a critical subject in non-cooperative context. In this paper, a novel method is proposed to estimate the number of active users (NoAU) in wireless time division multiple access (TDMA) networks based on the redundancy of adaptive...
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| Published in | Digital signal processing Vol. 75; pp. 1 - 12 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.04.2018
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| ISSN | 1051-2004 1095-4333 |
| DOI | 10.1016/j.dsp.2017.12.006 |
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| Abstract | Blind identification of protocols in wireless communication networks is a critical subject in non-cooperative context. In this paper, a novel method is proposed to estimate the number of active users (NoAU) in wireless time division multiple access (TDMA) networks based on the redundancy of adaptive channel coding. The proposed method, which is based on decision trees and machine learning, can also be used in adaptive coding systems to identify the number of distinct code rates employed by a single transmitter. Simulation results show that the proposed method can estimate the number of codes with accuracy around 99% in noiseless situation. Moreover, for noisy environments, reasonable accuracies compared to naïve methods are obtained.
•A novel method is proposed to estimate the number of active users in wireless TDMA networks.•The method works based on the redundancy of adaptive channel coding.•The idea is to extract some features correlated to the number of users from the received data stream. |
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| AbstractList | Blind identification of protocols in wireless communication networks is a critical subject in non-cooperative context. In this paper, a novel method is proposed to estimate the number of active users (NoAU) in wireless time division multiple access (TDMA) networks based on the redundancy of adaptive channel coding. The proposed method, which is based on decision trees and machine learning, can also be used in adaptive coding systems to identify the number of distinct code rates employed by a single transmitter. Simulation results show that the proposed method can estimate the number of codes with accuracy around 99% in noiseless situation. Moreover, for noisy environments, reasonable accuracies compared to naïve methods are obtained.
•A novel method is proposed to estimate the number of active users in wireless TDMA networks.•The method works based on the redundancy of adaptive channel coding.•The idea is to extract some features correlated to the number of users from the received data stream. |
| Author | Teimouri, Mehdi Ahmadiyan, Seyed Morteza |
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| Keywords | Time division multiple access Decision trees Estimating number of active users Machine learning Adaptive coding |
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| SubjectTerms | Adaptive coding Decision trees Estimating number of active users Machine learning Time division multiple access |
| Title | Estimating the number of users in TDMA networks based on redundancy of adaptive channel coding |
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