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An equalization scheme employing convolutional neural networks (CNN) is proposed in generalized frequency division multiplexing (GFDM) for propagation in a hybrid microwave-optical system. Batch Gradient Descent is used to train the CNN equalizer quickly. The approach suggested eliminates nonlinearity and produces improved performance. By incorporating the GFDM network with successive interference cancellation (SIC)-based receiver, this solution greatly increases the storage ability of consumers and also offers a larger coverage range. The suggested CNN equalizer is correlated with other approaches, including statistical models and estimator techniques. The experimental finding shows that CNN produces the highest value that is marginally better than the other equalizers with a numerical difficulty reduced to zero. The simulation findings suggest that QAM-GFDM can attain the same bit error rate (BER) as cyclic-prefix (CP) OFDM without compromising spectrum economy. The tests obtained have shown that the new approach works much better than traditional techniques.

ABSTRACTHigh data rates, extremely low power consumption, and minimal end-to-end latency are considered to be mandatory requirements for 5G wireless networks. Rapid improvements in design and performance of 5G physical layer waveforms have become necessary. The drawback of Orthogonal Frequency Division Multiplexing (OFDM) is high PAPR, that causes signal distortion, which reduces system efficiency. Generalized frequency division multiplexing (GFDM) is a promising non-orthogonal multicarrier transmission scheme, which has recently received a great deal of attention towards future fifth generation (5G) wireless networks. It overcomes the limitations of orthogonal frequency division multiplexing (OFDM), while preserving most of the advantages of it. Selective Level mapping (SLM) is one of the PAPR reduction techniques, that uses the phase shift technology. In this paper, SLM based on Chaotic Biogeography Based Optimization (CBBO) algorithm is proposed to offer an efficient solution to the problem of high PAPR, existing in the GFDM waveforms. Experimental results prove that, the proposed CBBO–SLM technique provides significant improvement in terms of PAPR reduction, as compared to the conventional SLM methods, such as conventional GFDM and OFDM-SLM. The proposed novel scheme is most suitable for QAM and QPSK applications, as it provides good PAPR reduction performance, at lower computational complexity.

In this work, the integration of renewable hybrid energy (RHE) resources using extendable multiport DC–DC boost converter is investigated. Three renewable energy sources such as solar photovoltaic (PV) system, wind energy system and fuel cell (FC) are integrated into the grid via this converter and grid-tied inverter. The output voltage of the multiport DC–DC boost converter is controlled using adaptive neuro fuzzy inference system-based controller. The overall system model is developed and tested in the MATLAB simulation software and also implemented in real time. The overall system is tested for different operating conditions such as change in irradiance condition of the solar PV panel, change in wind speed condition of the wind turbine, change in hydrogen pressure conditions of the FC and sudden change in load conditions and corresponding results are measured and analysed. The efficiency of the proposed system is about 98.21%. Finally, experimental results of the proposed model are also presented to examine the suitability of the system.