Anisotropic Channel Equalization using optimized clustering and Kalman-HMM trained ADALINE
Artificial feature extraction models evolving around adaptive computational decisions which involve spectrum analysis and channel estimation for dedicated cognitive antenna allocation including smart pipelining applications has become a domain of interest for communication protocols which are specif...
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| Veröffentlicht in: | 2023 4th International Conference on Computing and Communication Systems (I3CS) S. 1 - 7 |
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| Sprache: | Englisch |
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16.03.2023
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| Abstract | Artificial feature extraction models evolving around adaptive computational decisions which involve spectrum analysis and channel estimation for dedicated cognitive antenna allocation including smart pipelining applications has become a domain of interest for communication protocols which are specifically designed to meet the upcoming architectures and frameworks of 5G and 6G based WLAN/SCADA/DWWAN networks. A novel anisotropic clustering approach has been designed to provide channel equalization in an orthogonally multiplexed, multi-access transmission channel using constellation estimation and entropy interpolation, by using Calinski-Harabasz tuning condition over affinity propagation clusters while employing a novel Kalman optimized and Markov trained Adaptive Linear Neuron (ADALINE) based artificial perceptron layer (ApNN). HuangHilbert spectrum margins have been provided to interpretate signal improvement after equalization for a Rayleigh Fading channel undergoing Active White Gaussian Noise (AWGN). MQAM Modulation is considered over an Orthogonal Frequency Division Multiple Access (OF DMA) channel for experimental analysis in a virtual software defined Wireless Local Area Network (Vi-WLAN) environment. |
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| AbstractList | Artificial feature extraction models evolving around adaptive computational decisions which involve spectrum analysis and channel estimation for dedicated cognitive antenna allocation including smart pipelining applications has become a domain of interest for communication protocols which are specifically designed to meet the upcoming architectures and frameworks of 5G and 6G based WLAN/SCADA/DWWAN networks. A novel anisotropic clustering approach has been designed to provide channel equalization in an orthogonally multiplexed, multi-access transmission channel using constellation estimation and entropy interpolation, by using Calinski-Harabasz tuning condition over affinity propagation clusters while employing a novel Kalman optimized and Markov trained Adaptive Linear Neuron (ADALINE) based artificial perceptron layer (ApNN). HuangHilbert spectrum margins have been provided to interpretate signal improvement after equalization for a Rayleigh Fading channel undergoing Active White Gaussian Noise (AWGN). MQAM Modulation is considered over an Orthogonal Frequency Division Multiple Access (OF DMA) channel for experimental analysis in a virtual software defined Wireless Local Area Network (Vi-WLAN) environment. |
| Author | Mudiar, Priyanko Raj Sarma, Kandarpa Kumar |
| Author_xml | – sequence: 1 givenname: Priyanko Raj surname: Mudiar fullname: Mudiar, Priyanko Raj email: prmudiar8944@gmail.com organization: Gauhati University,Department of Electronics & Communication Engineering,Guwahati,India – sequence: 2 givenname: Kandarpa Kumar surname: Sarma fullname: Sarma, Kandarpa Kumar email: kandarpaks@gauhati.ac.in organization: Gauhati University,Department of Electronics & Communication Engineering,Guwahati,India |
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| SubjectTerms | 256-Quaternary Amplitude Modulation (256-QAM) ADALINE Affinity Propagation Clustering (AP-Clustering) Calinski-Harabasz index (CH-index) Channel estimation Computational modeling Entropy Hidden Markov Model (HMM) Hidden Markov models Huang-Hilbert Transform (HHT) Kalman optimized Kernel Recursive Least Square Algorithm (Kalman-KRLS) Markov processes Maximum Correntropy Stochastic Gradient Ascent / Descent (SGA/SGD) Transforms Wireless LAN |
| Title | Anisotropic Channel Equalization using optimized clustering and Kalman-HMM trained ADALINE |
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