In order to detect Android malware more effectively, an Android malware detection model
was proposed based on improved naive Bayes classification. Firstly, considering the …

A Deep Learning (DL) aided Logarithmic Likelihood Ratio (LLR) correction method is
proposed for improving the performance of Multiple-Input Multiple-Output (MIMO) receivers …

More than 10% of the Internet-of-Things (IoT) connections are expected to be realized
through Low Power Wide Area (LPWA) networks, representing several billions of connected …

In this paper, a new extrinsic information scaling and early stopping mechanism for long
term evolution (LTE) turbo code is proposed. A scaling factor is obtained by computing the …

Prominent turbo decoding algorithms used for wireless applications are Maximum A
posteriori Probability (MAP) and Soft Output Viterbi Algorithm (SOVA). These algorithms are …

In recent years, there has been significant interest in information-theoretic security
techniques that encrypt physical layer signals. We have proposed chaos modulation, which …

Early stopping (ES) methods reduce the average number of iterations required for decoding
turbo codes. These methods monitor the generated outputs along the iterations to terminate …

This article presents a new stopping criterion for the logarithmic-maximum a posteriori (Log-
MAP) turbo decoding. This method is inspired from the recent stopping criterion Sum alpha …

Min-Sum decoding (MS) is an alternative to belief propagation decoding with substantially
lower complexity. MS often results in an overestimation of the log likelihood ratio (LLR) in …

In pursuing of optimizing the performance of the Turbo decoding, early stopping (ES) rules
are deployed to reduce decoding latency and save resources. The aim of these rules is the …