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The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs.

In the photovoltaic system, the performance, efficiency, and generated power of the PV system are affected by changes in the environment, disturbances, and parameter variations, and this leads to a deviation from the operating maximum power point (MPP) of the PV system. Therefore, the main aim of this paper is to ensure the PV system operates at the maximum power point under the influence of exogenous disturbances and uncertainties, i.e., no matter how the irradiation, temperature, and load of the PV system change, by proposing a maximum power point tracking for the photovoltaic system (PV) based on the active disturbance rejection control (ADRC) paradigm. The proposed method provides better performance with excellent tracking for the MPP by controlling the duty cycle of the DC–DC buck converter. Moreover, comparison simulations have been performed between the proposed method and the linear ADRC (LADRC), conventional ADRC, and the improved ADRC (IADRC) to investigate the effectiveness of the proposed method. Finally, the simulation results validated the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with excellent response and minimum output performance index (OPI).