Implementation of 5G beamforming techniques on cylindrical arrays

In this paper we study the performance of a Uniform Cylindrical Array for a 5G base station working in the mmW region. Conventional and Capon beamforming design are considered. A comparison against a base station equipped with three Uniform Planar Arrays, one per sector, is presented. Average per-user achievable rate results are provided with different system configuration in terms of network loading and number of antennas, showing that Uniform Cylindrical Array could represent an interesting solution for 5GmmW networks

Assessment of MU-MIMO schemes with cylindrical arrays under 3GPP 3D channel model for B5G networks

Beyond 5G technologies promise groundbreaking advances on the performance of cellular networks, by taking advantage of Massive MIMO in mmWave scenarios. The aim of this study is to analyze and test the performance of a 5G cell site equipped with large antenna arrays. It is of particular interest the comparison between the typical trisector cell design with a planar array for each sector, and the less investigated cylindrical array, able to maintain a constant pattern through the whole azimuthal range. To validate our analysis, we adopt the latest 3GPP-compliant 3D channel model and we evaluate the performance of multi-user and multi-layer precoding and combining schemes. Several MIMO configurations are taken into account, and we show that cylindrical arrays can improve the overall system performance, both in terms of achievable per-user rate and outage probability

Performance Analysis of Multi-User MIMO Schemes under Realistic 3GPP 3-D Channel Model for 5G mmWave Cellular Networks

Novel techniques such as mmWave transmission and massive MIMO have proven to present many attractive features able to support high data demand for 5G NR technologies. Towards the standardization of 5G networks, channel modeling has become an important step in order to test the reliability of theoretical studies. In this paper, we study the performance of a 5G network at mmWave range for the downlink. We consider a single trisectorized base station equipped with planar arrays, and we model users as a spatial Poisson process in a hexagonal grid. We adopt the latest 3GPP channel model described in TR 38.901 and we provide a thorough description and step-by-step tutorial of it along with our customizations and MATLAB scripts for channel generation in the presented scenario. Moreover, we evaluate the performance of Multi-User Multi-Layer MIMO techniques, such as Signal-to-Leakage-plus-Noise Ratio (SLNR) precoding and MMSE combined with different system configurations by means of achievable per-user rate

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel

Sensing of DVB-T signals for white space cognitive radio systems

In cognitive radio networks, systems operating in digital television white spaces are particularly interesting for practical applications. In this paper, we consider single- antenna and multi-antenna spectrum sensing of real DVB-T signals under different channel conditions. Some of the most important algorithms are considered and compared, including energy detection, eigenvalue based techniques and methods exploiting OFDM signal knowledge. The obtained results show the algorithm performance and hierarchy in terms of ROC and detection probability under fixed false alarm rate, for different channel profiles in case of true DVB-T signals

Evaluation of MU-MIMO Digital Beamforming Algorithms in B5G/6G LEO Satellite Systems

Satellite Communication (SatCom) systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever-increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co-channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed-form solution, we propose a beamforming algorithm based on maximizing the users' Signal-to-Leakage-and-Noise Ratio (SLNR) served by a Low Earth Orbit (LEO) satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on Channel State Information (CSI) at the transmitter, i.e., Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF), and those only requiring the users' locations, i.e., Switchable Multi-Beam (MB). Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per-user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed SLNR beamforming technique is able to outperform both MMSE and ZF schemes in the presented SatCom scenario

Evaluation of multi-user multiple-input multiple-output digital beamforming algorithms in B5G/6G low Earth orbit satellite systems

Satellite communication systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever-increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co-channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed-form solution, we propose a beamforming algorithm based on maximizing the users' signal-to-leakage-and-noise ratio served by a low Earth orbit satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on channel state information at the transmitter, that is, minimum mean square error and zero forcing, and those only requiring the users' locations, that is, switchable multi-beam. Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per-user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed signal to-leakage-plus-noise ratio beamforming technique is able to outperform both minimum mean square error and multi-beam schemes in the presented satellite communication scenario

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes

Simplified Negative Pressure Wound Therapy Device for Application in Low-Resource Settings

Negative pressure wound therapy (NPWT) provides proven wound healing benefits and is often a desirable wound treatment. Unfortunately, NPWT devices are not widely available in low-resource settings (LRSs). In order to overcome identified NPWT barriers, a simplified NPWT (sNPWT) system was designed and iteratively improved during field-based testing. The sNPWT technology, our device design iterations, and the design-based results of our field tests are described. The sNPWT system includes a bellows hand pump, an occlusive drape, and a tube with tube connectors, connecting the drape to the pump. The most critical property of a sNPWT system is that it must be airtight. The details of the design iterations needed to achieve an occlusive system are explained. During the design process, the sNPWT system was tested during the earthquake relief in Haiti. This testing found that a liquid sealant was necessary to seal the drape to the peri-wound skin. A study conducted in Rwanda verified that a liquid latex sealant was safe to use and that the tube connector must be connected to the drape with an airtight method during the manufacturing process. This work has shown that sNPWT is feasible in LRSs. Since the completion of the clinical testing, the design has been further evolved and the developers are working with contract manufactures to produce the final design and preparing for regulatory approval applications

Challenges of Meeting Surgical Needs in the Developing World

The burden of surgical conditions and diseases is increasing in low-income and middle-income countries, but the capacity to meet the demands they present is not following pace. Ongoing initiatives, such as brief visits by surgeons from advantaged countries, sending surgical residents to spend time in a developing country as part of their training, or ships weighing anchor offshore and offering some limited on-shore or on-board services, have not proven successful. More comprehensive and sustainable solutions include the development of local training programs, better retention of trainees with adequate incentives particularly in rural areas, and engaging government and professional associations, as well as academic institutions, to develop and implement policies to address local training needs