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All Journal International Journal of Electrical and Computer Engineering Knowledge Engineering and Data Science
Trang, Kien
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Non-binary codes approach on the performance of short-packet full-duplex transmissions Vuong, Bao Quoc; Trang, Kien; Nguyen, An Hoang; Do, Hung Ngoc
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1683-1690

Abstract

This paper illustrates the enhancement of the performance of short-packet full-duplex (FD) transmission by taking the approach of non-binary low density parity check (NB-LDPC) codes over higher Galois field. For the purpose of reducing the impacts of self-interference (SI), high order of modulation, complexity, and latency decoder, a blind feedback process composed of channels estimation and decoding algorithm is implemented. In particular, this method uses an iterative process to simultaneously suppress SI component of FD transmission, estimate intended channel, and decode messages. The results indicate that the proposed technique provides a better solution than both the NB-LDPC without feedback and the binary LDPC feedback algorithms. Indeed, it can significantly improve the performance of overall system in two important factors, which are bit-error-rate (BER) and mean square error (MSE), especially in high order of modulation. The suggested algorithm also shows a robustness in reliability and power consumption for both short-packet FD transmissions and high order modulation communications.
A Comparative Study of Machine Learning-based Approach for Network Traffic Classification Trang, Kien; Nguyen, An Hoang
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques.
Co-Authors Do, Hung Ngoc Nguyen, An Hoang Vuong, Bao Quoc