In order to generate electricity from solar PV modules, this study proposed a novel high-voltage gain step-up (HVGSU) DC–DC converter for solar photovoltaic system operation with a maximum power point (MPP) tracker. The PV array can supply power to the load via a DC–DC converter, increasing the output voltage. Due to the stochastic nature of solar energy, PV arrays must use the MPPT control approach to function at the MPP. This study suggests a novel HVGSU converter that uses the primary boost conversion cell and combines switched capacitors and voltage multiplier cells. The proposed topology is upgradeable for high-voltage gain step-up and can be incorporated as well. A clamp circuit reuses the energy that leaks out so that the switch voltage stress and power loss are kept to a minimum. One thing that makes it stand out is that the voltage stress on the diodes and switch stays low and constant even as the duty cycle changes. Additionally, the inductor greatly reduces the diodes’ reverse recovery losses. There is a lot of information about steady-state analyses, operation principles, and design guidelines. A prototype circuit is built to test the maximum power point tracking operation with voltage conversion from 20–40 V to 380 V at 150 W. The results of the experiments support the theoretical analysis and claimed benefits. The proposed converter has the ability to track the maximum power point and a high conversion efficiency over a wide range of power. A weighted efficiency of 90–96% is shown by the prototype.

We present a subspace-based polynomial rooting algorithm to estimate the frequency bias (FB) of generalized frequency division multiplexing (GFDM) systems employing null subcarriers and repetitive sub-symbols. The estimation process is classified into fractional FB (FFB) and integer FB (IFB) estimation. The use of repetitive sub-symbols creates a quasi-periodic structure in the FB-distorted received signal, allowing the proposed algorithm to estimate the FFB using the root-MUSIC algorithm. Based on this, the proposed algorithm compensates for the FFB in the received signal and then estimates the null subcarrier pattern (NSP) in the frequency domain. As a result, the IFB estimate can be obtained in a maximum likelihood (ML) manner. Before the NSP estimation, this study uses a sub-symbol combiner to enhance signal strength of the FFB-aligned signal, ensuring the reliability of the IFB estimate. Computer simulations show that the proposed subspace-based algorithm has several advantages over traditional FB estimation methods: 1. Unlike some existing algorithms that use a training sequence to estimate FB, the proposed approach is a semi-blind algorithm because it can deliver information through repeated sub-symbols while estimating FB; 2. The proposed algorithm demonstrates excellent estimation accuracy compared to most traditional FB estimation algorithms; and 3. The proposed algorithm is computationally efficient, making it applicable to real-time applications in future communication systems.