scholarly journals An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Ahmed K. Ali ◽  
Ergun Erçelebi
Keyword(s):  
Fading Channels ◽  
Input Data ◽  
Additive White Gaussian Noise ◽  
Recognition Rate ◽  
Higher Order ◽  
Superior Performance ◽  
Modulation Classification ◽  
Classification Rate ◽  
Automatic Modulation Classification ◽  
Awgn Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of automatic modulation classification (AMC) which deals with modulation classification and is a pattern recognition problem. However, the algorithms that focus on multilevel quadrature amplitude modulation (M-QAM) which underneath different channel scenarios is well detailed. A search of the literature revealed that few studies were performed on the classification of high-order M-QAM modulation schemes such as 128-QAM, 256-QAM, 512-QAM, and 1024-QAM. This work focuses on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting higher order cumulant’s (HOC) features from input data received raw. The HOC signals were extracted under the additive white Gaussian noise (AWGN) channel with four effective parameters which were defined to distinguish the types of modulation from the set: 4-QAM∼1024-QAM. This approach makes the classifier more intelligent and improves the success rate of classification. The simulation results manifest that a very good classification rate is achieved at a low SNR of 5 dB, which was performed under conditions of statistical noisy channel models. This shows the potential of the logarithmic classifier model for the application of M-QAM signal classification. furthermore, most results were promising and showed that the logarithmic classifier works well under both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero). It can be considered as an integrated automatic modulation classification (AMC) system in order to identify the higher order of M-QAM signals that has a unique logarithmic classifier to represent higher versatility. Hence, it has a superior performance in all previous works in automatic modulation identification systems.

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals Per User Based Interference Alignment for Uplink CoMP with Perfect CSI under Different Channel Models

2021 ◽  
pp. 444-449
Author(s):  
Amalia Eka Rakhmania ◽  
◽  
Mochammad Taufik ◽  
Aad Hariyadi ◽  
Hudiono Hudiono ◽  
...  
Keyword(s):  
Fading Channels ◽  
Additive White Gaussian Noise ◽  
Interference Alignment ◽  
Channel Models ◽  
Low Snr ◽  
Rayleigh Channel ◽  
Awgn Channel ◽  
Nakagami Fading Channel ◽  
Sum Rate ◽  
Uplink Comp

Per-user based SLNR-SINR interference alignment (IA) algorithm for uplink CoMP is evaluated under different channel models. Rayleigh, Rician, and Nakagami fading channel is used in transmission with the Additive White Gaussian Noise (AWGN) channel set as reference. Performance evaluation is based on its achievable sum rate and convergence rate by varying the channel, the number of serving cells, and UE's transmit and receive antenna combination. Assuming perfect CSI, the result shows that the maximum achievable sum rate is when the communication occurs in the AWGN channel. There are 3 bps/Hz difference between communication over Rayleigh and Nakagami channel, and 1.5 bps/Hz difference between Rician and Nakagami channel. Among the three fading channels, a smaller number of transmitting and receiving antenna in UE resulted in a higher sum rate with 58.97 bps/Hz for IA in the Nakagami channel with {3, 2, 4, 4, 2} configuration. In the low SNR region, the lowest sum rate for this configuration achieves 15.87 bps/Hz for IA in the Rayleigh channel.

scholarly journals Performance Analysis of Automatic Modulation Classification using Time Frequency Transforms under Non-Ideal Channel Conditions

2019 ◽  
Vol 8 (12) ◽  
pp. 1685-1691
Keyword(s):  
Performance Analysis ◽  
Fading Channels ◽  
Classification Accuracy ◽  
Modulation Classification ◽  
Time Frequency ◽  
Automatic Modulation Classification ◽  
Performance Metric ◽  
S Transform ◽  
Channel Conditions

Classification of different analog and digital modulation classes using Time-Frequency Transforms (TFTs) through MST and MFSWT under ideal channel conditions is presented in this paper. It also deals with performance analysis of proposed Modified S- Transform (MST) and Modified Frequency Slice Wavelet Transform (MFSWT) based Automatic Modulation Classification (AMC) methods under different channel conditions such as Gaussian and fading channels. The performance of the proposed TFT based AMC methods under AWGN (with SNR values varied from -10 dB to 20 dB) and fading channels is examined through simulation. Moreover, the performance of the proposed TFT based AMC is compared with that of the existing techniques in terms of performance metric namely classification accuracy which is also discussed in this paper.

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