Anisotropic Channel Equalization using optimized clustering and Kalman-HMM trained ADALINE - Mudiar, Priyanko Raj; Sarma, Kandarpa Kumar (2023 4th International Conference on Computing and Communication Systems (I3CS), 2023)
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.