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The ever-increasing proliferation of mobile users and new technologies, and the demands for ubiquitous connectivity, high data capacity, faster data speed, low latency, and reliable services have been driven the quest for the next generation, fifth generation (5G), of the wireless networks. Cloud radio access network (C-RAN) has been identified as a promising architecture for addressing 5G requirements. However, C-RAN enforces stringent requirements on the fronthaul capacity and latency. To this end, several fronthaul solutions have been proposed in the literature, ranging from transporting digitized radio signals over fiber and functional splits to an entirely analog-radio-over fiber (A-RoF) based fronthual. A-RoF is a highly appealing transport solution for fronthual of 5G and beyond owing to its high bandwidth and energy efficiency, low system complexity, small footprint, cost-effectiveness, and low latency. In this paper, a high capacity multiple-input-multiple-output (MIMO) enabled all-optical analog-millimeter-waveover fiber (A-MMWoF) fronthaul architecture is proposed for 5G and beyond of wireless networks. The proposed architecture employs photonic MMW signals generation and mode division multiplexing (MDM) along with wavelength division multiplexing (WDM) for transporting MMW MIMO signals in the optical domain. In support of the proposed architecture design, a comprehensive state-of-the-art literature review on the recent research works in high capacity A-RoF fronthaul systems and related transport technologies is presented. In addition, the corresponding potential challenges and solutions along with potential future directions are highlighted. The proposed design is flexible and scalable for achieving high capacity, high speed, and low latency fronthaul links. INDEX TERMS 5G, fronthaul, analog-radio-over fiber (A-RoF), millimeter-wave-over fiber (MMWoF), multiple-input-multiple-output (MIMO), space division multiplexing (SDM), mode division multiplexing (MDM), cloud radio access network (C-RAN).

This paper presents the design of a rectangular microstrip line-fed patch antenna array with a centre frequency of 3.8 GHz for WiMAX and Unmanned Air Vehicle (UAV) applications. A single element, 1x2 and 2x2 microstrip rectangular patch antennas were designed and simulated in Computer Simulation Tool (CST) Microwave Studio environment. The results of designed antennas were compared in terms of Return Loss (S11 parameters), bandwidth, directivity, gain and radiation pattern. Compared to traditional microstrip antennas the proposed array structure achieved a gain and directivity of 13.2 dB and 13.5 dBi respectively. The antenna was fabricated using Rogers Duroid RT-5880 substrate with a dielectric constant r of 2.2 and a thickness of 1.574 mm respectively. The array antennas were measured in the laboratory using Vector Network Analyser (VNA) and the results show good agreement with the array antenna simulation.

Diabetes is one of the most rapidly growing chronic diseases, which has affected millions of people around the globe. Its diagnosis, prediction, proper cure, and management are crucial. Data mining based forecasting techniques for data analysis of diabetes can help in the early detection and prediction of the disease and the related critical events such as hypo/hyperglycemia. Numerous techniques have been developed in this domain for diabetes detection, prediction, and classification. In this paper, we present a comprehensive review of the state-of-the-art in the area of diabetes diagnosis and prediction using data mining. The aim of this paper is twofold; firstly, we explore and investigate the data mining based diagnosis and prediction solutions in the field of glycemic control for diabetes. Secondly, in the light of this investigation, we provide a comprehensive classification and comparison of the techniques that have been frequently used for diagnosis and prediction of diabetes based on important key metrics. Moreover, we highlight the challenges and future research directions in this area that can be considered in order to develop optimized solutions for diabetes detection and prediction.

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