Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks | Zendy

Zeeshan Kaleem | Zendy; Muhammad Ali | Zendy; Ishtiaq Ahmad | Zendy; Waqas Khalid | Zendy; Ahmed Alkhayyat | Zendy; Abbas Jamalipour | Zendy

Open Access

Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks

Author(s) -

Zeeshan Kaleem,

Muhammad Ali,

Ishtiaq Ahmad,

Waqas Khalid,

Ahmed Alkhayyat,

Abbas Jamalipour

Publication year - 2021

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2021.3128508

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional overhead in the signal. In this paper, we developed a universal software radio peripheral (USRP) based intelligent AMC system to detect and classify various digital modulation schemes in real-time. For each modulation scheme, we extracted different spectral features for different values of signal-to-noise ratio (SNR) values. Based on the extracted features, we train the neural network to classify the modulation schemes. Experimental results show that we achieve around 97% classification accuracy in real-time as compared to the existing offline classification schemes. Moreover, we also compare the performance of the proposed model with HisarMod2019.1 model in terms of various metrics such as cross-entropy and mean square error. Results clearly demonstrates the efficiency of the proposal for real-time implementation and classification.

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