Engineering, Artificial Intelligence, Signal Processing, Computer Networks and Communications
2
Scopus Publications
Scopus Publications
Novel filtering and regeneration technique with statistical feature extraction and machine learning for automatic modulation classification Sanzhar Sarmanbetov, Madiyar Nurgaliyev, Batyrbek Zholamanov, Kymbat Kopbay, Ahmet Saymbetov, Askhat Bolatbek, Nurzhigit Kuttybay, Sayat Orynbassar, Evan Yershov Digital Signal Processing A Review Journal, 2024 Automatic modulation classification (AMC) plays a crucial role in the stages of processing signals from unknown sources and monitoring the airwaves. This paper presents an AMC method based on machine learning (ML) using constellation diagrams, distribution test function and high-order cumulants (HOCs) and novel filtering and regeneration technique. A data clustering approach based on the signal phase characteristics is utilized to perform filtering, regeneration, and testing distribution functions. NI PXIe-1065 was used as a source of modulated signals, which is a multifunctional experimental complex for generating signals with various types of modulation. In this work, 5 types of modulation schemes were employed, specifically BPSK, QPSK, PSK-8, PSK-16 and PSK-32. The proposed classification method consists of three main stages. At the first stage, the constellation diagram is partitioned into clusters. At the second stage, the phase distribution function of signals within each cluster is analyzed, HOCs were calculated and novel filtering and regeneration techniques were employed. The main concept of the proposed approach is compressing angular ranges depicted on the constellation diagram. This involves filtering points and subsequently regenerating, which means relocating points that fall within adjacent phase ranges. Also, a statistical analysis of the phase transition distribution function in clusters is conducted at this step. At the last stage the obtained data set was trained using XGBOOST ML method. The proposed approach shows excellent results and can be a strong contender to existing NN based AMC methods, achieving 95% accuracy at an SNR value of 9 dB.
IMPLEMENTATION OF FUNCTIONAL BLOCK RADIO UNIT BASED ON SYSTEM-ON-CHIP and M.K. Ibraimov Eurasian Physical Technical Journal, 2023 This article discusses the implementation of the Radio Unit functional block based on the System-on-Chip. The primary focus was on integrating Radio Unit blocks such as modulation and Fast Fourier Transform on Field-Programmable Gate Array. Technical aspects of design, module testing, and Radio Unit block performance optimization are thoroughly examined. The results demonstrate that when separating the functionality of the 7.3 technology Fifth Generation (5G) radio block, the modulation module uses the minimum Field-Programmable Gate Array resources compared to other blocks. The Fast Fourier Transform block can meet delay requirements at the maximum Field-Programmable Gate Array size and clock frequency of 250 MHz. This article serves as a resource for engineers and researchers interested in optimizing the development and integration process of high-performance functional blocks in modern radio systems.
