Modulation classification of linearly modulated signals in slow flat fading channels

2011 ◽  
Vol 5 (5) ◽  
pp. 443 ◽  
Author(s):  
M. Derakhtian ◽  
A.A. Tadaion ◽  
S. Gazor
Keyword(s):  
Fading Channels ◽  
Modulation Classification ◽  
Flat Fading ◽  
Modulated Signals

scholarly journals A Robust Cooperative Modulation Classification Scheme with Intra sensor Fusion for the Time correlated Flat Fading Channels

2020 ◽  
Vol 70 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Goran Marković ◽  
Vlada Sokolović
Keyword(s):  
Fading Channels ◽  
Sensor Fusion ◽  
Large Scale ◽  
Negative Influence ◽  
Modulation Classification ◽  
Distributed Sensors ◽  
Performance Improvements ◽  
Flat Fading ◽  
Significant Performance ◽  
Single Sensor

Networks with distributed sensors, e.g. cognitive radio networks or wireless sensor networks enable large-scale deployments of cooperative automatic modulation classification (AMC). Existing cooperative AMC schemes with centralised fusion offer considerable performance increase in comparison to single sensor reception. Previous studies were generally focused on AMC scenarios in which multipath channel is assumed to be static during a signal reception. However, in practical mobile environments, time-correlated multipath channels occur, which induce large negative influence on the existing cooperative AMC solutions. In this paper, we propose two novel cooperative AMC schemes with the additional intra-sensor fusion, and show that these offer significant performance improvements over the existing ones under given conditions.

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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