State-of-the-Art Antenna Technology for Cloud Radio Access Networks (C-RANs)

The cloud radio access network (C-RAN) is one of the most efficient, low-cost, and energy-efficient radio access techniques proposed as a potential candidate for the implementation of next-generation (NGN) mobile base stations (BSs). A high-performance C-RAN requires an exceptional broadband radio frequency (RF) front end that cannot be guaranteed without remarkable antenna elements. In response, we present state-of-the-art antenna elements that are potential candidates for the implementation of the C-RAN’s RF front end. We present an overview of C-RAN technology and different types of planar antennas operating at the future proposed fifth-generation (5G) bands that may include the following: (i) ultra-wide band (UWB) (3–12 GHz), (ii) 28/38 GHz, and (iii) 60-GHz radio. Further, we propose different planar antennas suitable for the implementation of C-RAN systems. We design, simulate, and optimize the proposed antennas according to the desired specifications covering the required frequency bands. The key design parameters are calculated, analyzed, and discussed. In our research work, the proposed antennas are lightweight, low-cost, and easy to integrate with other microwave and millimeter-wave (MMW) circuits. We also consider different implementation strategies that can be helpful in the execution of large-scale multiple-input multiple-output (MIMO) networks

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.Comment: Published in MDPI Electronics Journa

FPGA Hardware Implementation of DOA Estimation Algorithm Employing LU Decomposition

In this paper, authors present their work on field-programmable gate array (FPGA) hardware implementation of proposed direction of arrival estimation algorithms employing LU factorization. Both L and U matrices were considered in computing the angle estimates. Hardware implementation was done on a Virtex-5 FPGA and its experimental verification was performed using National Instruments PXI platform which provides hardware modules for data acquisition, RF down-conversion, digitization, etc. A uniform linear array consisting of four antenna elements was deployed at the receiver. LabVIEW FPGA modules with high throughput math functions were used for implementing the proposed algorithms. MATLAB simulations of the proposed algorithms were also performed to validate the efficacy of the proposed algorithms prior to hardware implementation of the same. Both MATLAB simulation and experimental verification establish the superiority of the proposed methods over existing methods reported in the literature, such as QR decomposition-based implementations. FPGA compilation results report low resource usage and faster computation time compared with the QR-based hardware implementation. Performance comparison in terms of estimation accuracy, percentage resource utilization, and processing time is also presented for different data and matrix sizes