Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision‐making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto‐regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh⁻¹ for singular values vs 7.09 cents kWh⁻¹ using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid‐connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods.

Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of $1\times {10}^{20}$ (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 $\left({\mathrm{mA}/\mathrm{m}}^2\right)$, respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells.

Organometal halide perovskites have recently attracted plenty of attention in solar cell applications due to their high charge-carrier mobilities, high photoconversion efficiencies and ease of fabrication. Herein, we demonstrate the broadband characteristics of CH3NH3PbI3-xClx thin film photodetectors that were fabricated using ambient processing conditions. Two different photodetector devices were designed and fabricated. The first device (architecture 1) involves spinning a thin perovskite film between the top and bottom electrodes, while the second (architecture 2) includes additional electron and hole transport layers between the contacts. The achieved responsivity was 2 A/W at 2 V for architecture 1 compared to 0.42 A/W at -2 V for architecture 2. We have observed that the perovskite film formed on top of the PEDOT: PSS layer is non-uniform with large crystalline domains. In some parts, the crystal domains were as large as 1.76 μm, whereas better surface coverage was achieved on top of the ITO, which had smaller crystal domains (1.4 μm). This resulted in the dark current of architecture 1 being one order of magnitude higher in comparison to architecture 2. This new fabrication method allows for low-cost optoelectronic device fabrication in uncontrolled laboratories. Contrary to recommendations in the literature, our findings show that simple fabrication with the PEDOT:PSS layer enables good photoconduction properties in room temperature conditions.