scholarly journals Optimized Polynomial Classifier for Classification of M-PSK Signals

2021 ◽  
Vol 8 (4) ◽  
pp. 575-582
Author(s):  
Nooh Bany Muhammad ◽  
Mubashar Sarfraz ◽  
Sajjad A. Ghauri ◽  
Saqib Masood
Keyword(s):  
Fading Channels ◽  
Additive White Gaussian Noise ◽  
Classification Problem ◽  
Research Area ◽  
Rician Fading ◽  
Computational Techniques ◽  
Modulation Classification ◽  
Higher Dimension ◽  
Polynomial Classifier ◽  
Dimension Space

Automatic modulation classification (AMC) is the emerging research area for military and civil applications. In this paper, M-PSK signals are classified using the optimized polynomial classifier. The distinct features i.e., higher order cumulants (HOC’s) are extracted from the noisy received signal and the dataset is generated with different number of samples, various SNR’s and on several fading channels. The proposed classifier structure classifies the overall modulation classification problem into binary sub-classifications. In each sub-classification, the extracted features are expanded using polynomial expansion into higher dimension space. In higher dimension space numerous non-linearly separable classes becomes linearly separable. The performance of the proposed classifier is evaluated on Rayleigh and Rician fading channels in the presence of additive white gaussian noise (AWGN). The polynomial classifier performance is optimized using one of the famous heuristic computational techniques i.e., Genetic Algorithm (GA). The extensive simulations have been carried with and without optimization, which shows relatively better percentage classification accuracy (PCA) as compared with the state of art existing techniques.

An efficient adaptive modulation technique over realistic wireless communication channels based on distance and SINR

Frequenz ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Rahim Khan ◽  
Qiang Yang ◽  
Alam Noor ◽  
Sohaib Bin Altaf Khattak ◽  
Liang Guo ◽  
...  
Keyword(s):  
Wireless Communication ◽  
Fading Channels ◽  
Communication Systems ◽  
Additive White Gaussian Noise ◽  
Adaptive Modulation ◽  
Base Stations ◽  
Rician Fading ◽  
Available Bandwidth ◽  
Modulation Schemes ◽  
The Impact

Abstract A growing trend has been observed in recent research in wireless communication systems. However, several limitations still exist, such as packet loss, limited bandwidth and inefficient use of available bandwidth that needs further investigation and research. In light of the above limitations, this paper uses adaptive modulation under various parameters, such as signal to interference plus noise ratio (SINR), and communication channel’s distances. The primary goal is to minimize bit error rate (BER), improve throughput and utilize the available bandwidth efficiently. Additionally, the impact of Additive White Gaussian Noise (AWGN), Rayleigh and Rician fading channels on the performance of various modulation schemes are also studied. The simulation results demonstrate that our proposed technique optimally improves the BER and spectral efficiency in the long-range communication as compared to the fixed modulation schemes under the co-channel interference of surrounding base stations. The results indicate that the performance of fixed modulation schemes is suitable only either at high SINR and low distance or at low SINR and high distance values. Moreover, on the other hand, its performance was suboptimal in the entire wireless communication channel due to high distortion and attenuation. Lastly, we also noted that BER performance in the AWGN channel is better than Rayleigh and Rician channels with Rayleigh channel exhibiting poor performance than the Rician channel.

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.

Performance Comparison between DPSK and OQPSK modulation approaches in multi environments channels with Matlab Simulink models

2019 ◽  
Vol 7 (1) ◽  
pp. 30-39
Author(s):  
Fatima faydhe Al- Azzawi ◽  
Faeza Abas Abid ◽  
Zainab faydhe Al-Azzawi
Keyword(s):  
Phase Shift ◽  
Fading Channels ◽  
Additive White Gaussian Noise ◽  
Block Codes ◽  
Performance Comparison ◽  
Phase Shift Keying ◽  
Differential Phase Shift ◽  
Matlab Simulink ◽  
Communication Industry ◽  
Shift Keying

Phase shift keying modulation approaches are widely used in the communication industry. Differential phase shift keying (DPSK) and Offset Quadrature phase shift keying (OQPSK) schemes are chosen to be investigated is multi environment channels, where both systems are designed using MATLAB Simulink and tested. Cross talk and unity of signals generated from DPSK and OQPSK are examined using Cross-correlation and auto-correlation, respectively. In this research a proposed system included improvement in bit error rate (BER) of both systems in  the additive white Gaussian Noise (AWGN) channel, by using the convolutional and block codes, by increasing the ratio of energy in the specular component to the energy in the diffuse component (k) and  the diversity order BER in the fading channels will be improved in both systems.    

Sign in / Sign up

Export Citation Format

Share Document