Autonomous Modulation Classification using Single Inception Module based Convolutional Neural Network

Mumtaz, Muhammad Zeeshan; Khurram, Muhammad; Adnan, Muhammad; Fazil, Adnan

doi:10.1109/ibcast51254.2021.9393285

2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) 2021

DOI: 10.1109/ibcast51254.2021.9393285

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Autonomous Modulation Classification using Single Inception Module based Convolutional Neural Network

Muhammad Zeeshan Mumtaz

Muhammad Khurram

Muhammad Adnan

et al.

Abstract: Modulation recognition plays a crucial role in noncooperative communication in which receivers have no prior information regarding transmitter modulation scheme. This paper presents a novel convolutional neural network with single Stem and Inception module for autonomous modulation classification from raw I/Q received channels. This addresses the computationally intensive problem of conversion of I/Q channels to constellation image processing. The proposed system is capable of classifying 11 standard modulatio… Show more

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2023

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Cited by 2 publications

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Deep Learning-Based Detector With Modulation Parameter-Independent Structure for OFDM With Index Modulation

Kim,

Park

2023

IEEE Access

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Every existing deep learning (DL)-based detector for orthogonal frequency division multiplexing with index modulation (OFDM-IM) is optimized for one instance for modulation parameters (MPs) such as the modulation orders and the number of activae subcarriers but does not operate well for other sets of MPs. In this paper, we propose a novel index-bit-detection neural network with modulation parameter-independent structure (IBDNN-MPI) for OFDM-IM, which aims to operate with various MPs. IBDNN-MPI adopts a concatenation of Inception module and fully-connected layers to detect index bits effectively. It is shown via simulations that IBDNN-MPI implementations show better bit error rate (BER) performance and less complexity than existing DL-based detectors optimized for each set of MPs. They have BER performances close to the maximum likelihood and the log-likelihood ratio detectors. Moreover, they can be applicable to dual-mode OFDM-IM at slightly increased computational complexity and cover the scenarios with imperfect channel state information.INDEX TERMS Deep learning, Inception module, orthogonal frequency division multiplexing with index modulation (OFDM-IM), log-likelihood ratio (LLR) detector, maximum likelihood (ML) detector, parameter-independent detector. I. INTRODUCTION 1 O RTHOGONAL frequency division multiplexing (OFDM) 2 is a well-known multi-carrier system in high-speed 3 wireless communication and has an issue that the sub-channel 4 orthogonality can be lost and inter channel interference (ICI) 5 can occur due to the characteristic of rapidly varying wire-6 less channels [1]. Recently, OFDM with index modulation 7 (OFDM-IM) [2] and dual mode OFDM-IM (DM-OFDM-IM) 8 [6]-[9] are proposed. In OFDM-IM, only a part of subcarriers 9 are activated and modulated to carry message bits which are 10 called carrier bits. According to the indices of the activated 11 subcarriers, additional message bits are virtually transmitted, 12 which are called index bits. The OFDM-IM can have higher 13 energy efficiency and reliability than the conventional OFDM 14 by mitigating the ICI. In DM-OFDM-IM [6], all subcarriers 15 are activated and modulated with two clearly distinguishable 16 constellations while only a part of subcarriers are activated in 17 OFDM-IM. The index bits in DM-OFDM-IM are transmitted 18 depending on the pattern which constellation is selected 19 This article has been accepted for publication in IEEE Access.

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Deep Learning-Based Detector With Modulation Parameter-Independent Structure for OFDM With Index Modulation

Kim,

Park

2023

IEEE Access

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Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Abd-Elaziz,

Abdalla,

Elsayed

2023

Sensors

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Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

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Autonomous Modulation Classification using Single Inception Module based Convolutional Neural Network

Cited by 2 publications

References 16 publications

Deep Learning-Based Detector With Modulation Parameter-Independent Structure for OFDM With Index Modulation

Deep Learning-Based Detector With Modulation Parameter-Independent Structure for OFDM With Index Modulation

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

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