Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G

Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated significant volume of high data traffic processing in next-generation systems. The primary challenges in mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and analog-to-digital converters. Nevertheless, hybrid analog–digital precoders are considered a cost-effective solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design. We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders to realize the joint optimization of computational complexity and communication power. Therefore, the alternating minimization algorithm enhances HP by improving the performance of the FCS through advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for PCS and FCS by 48.3% and 17.12%, respectively

IoT-Based Remote Health Monitoring System Employing Smart Sensors for Asthma Patients during COVID-19 Pandemic

COVID19 and asthma are respiratory diseases that can be life threatening in uncontrolled circumstances and require continuous monitoring. A poverty stricken South Asian country like Bangladesh has been bearing the brunt of the COVID19 pandemic since its beginning. The majority of the country's population resides in rural areas, where proper healthcare is difficult to access. This emphasizes the necessity of telemedicine, implementing the concept of the Internet of Things (IoT), which is still under development in Bangladesh. This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system, specifically for asthma patients who are at an increased risk of COVID19. Since on-time treatment is essential, this system will allow doctors and medical staff to receive patient information in real time and deliver their services immediately to the patient regardless of their location. The proposed system consists of various sensors collecting heart rate, body temperature, ambient temperature, humidity, and air quality data and processing them through the Arduino Microcontroller. It is integrated with a mobile application. All this data is sent to the mobile application via a Bluetooth module and updated every few seconds so that the medical staff can instantly track patients' conditions and emergencies. The developed prototype is portable and easily usable by anyone. The system has been applied to five people of different ages and medical histories over a particular period. Upon analyzing all their data, it became clear which participants were particularly vulnerable to health deterioration and needed constant observation. Through this research, awareness about asthmatic symptoms will improve and help prevent their severity through effective treatment anytime, anywhere.Comment: Wireless Communications and Mobile Computing (2022

Design and study of a miniaturized millimeter wave array antenna for wireless body area network

A miniaturized millimeter wave (mmWave) antenna for wireless body area networks is proposed in this paper. The antenna is found to be operational in the V-band, around the 60 GHz frequency range, with high efficiency of up to 99.98% in free space simulations. A multilayer, thin substrate was implemented in the design to enhance radiation efficiency and gain. The antenna seems to be most suitable for small electronic devices and wireless body area network (WBAN) applications because of its low profile and lighter weight concept. To enhance its performance, several arrays of different orders were created. The Parallel-Fed and Tapered Feed Line methods were followed to design the planar arrays with 1 × 2, 1 × 4, and 2 × 2 elements in the primary design. Free space results were compared, and a 2 × 2 element array was found to be the most balanced according to the simulations. To justify the eligibility of these designs for WBAN applications, a virtual human body model was created within the 3D computer-simulated environment and the simulations were repeated, where four equal-spaced distances were taken into account to identify the antenna and its array behavior more accurately. Simulations returned optimistic results for the 2 × 2 element planar array arrangement in almost all parameters, even when placed close to the human body at any distance greater than 2 mm

Long-baseline, sub-decimeter kinematic GPS positioning of moving object, with potential application to monitor ocean surface wave

Precise relative kinematic positioning of moving platforms using GPS carrier phase observables has numerous applications. One prominent application is utilization of highly stabilized GPS technology mounted on the buoy, which is specially designed for detecting tsunami wave at open sea. The essential point of this research is to investigate a potential use of a GPS tsunami buoy for the purpose of tsunami early warning system with long-baseline kinematic GPS processing method. The rule of thumb GPS positioning concept, GPS position results are affected by. baseline length mostly due to de-correlation of atmospheric errors. As baseline lengths increase, position results degrade due to the difficulty to correctly fix the cariier phase ambiguity to its integer value. carrier phase fixed ambiguity solutions are more accurate that float arnbiguify solutions. It is generally accepted that carrier phase can be successfUlly fixed for baselines of up to 10 km. After that, fixing ambiguities becomes more difficult and risky. It would be certainty more advantageous to have a reliable float solution rather than an unreliable fixed solution. In this study, we have developed a new quasi-real time long-baseline kinematic analysis method using dual-frequency carrier phase with floated ambiguities, implemented in the Bernese GPS Software Version 5.0. We demonstrate that early detection of a damaging tsunami can be achieved by tracking the anomalous changes in sea surface height. The movements of a GPS buoy relative to a base station with baseline length of 500 km have been monitored in quasi-real time mode, and the tsunami waves caused by the 5th September 2004 Off Kii Peninsula earthquake, Japan, have been successful detected as they went by, even though these were only 15 cm high. The filtered record of the solution closely resembles that of short baseline, with RMS of 3.4 cm over 2.5 hours. To test the robustness of our Iong-baseline kinematic GPS method under various meteorological, we conducted the GPS tsunami buoy data analysis continuously for 8 days to monitor the motion of the buoy. The average scatterings of GPS buoy heights by the low-pass filtered 1 -Hz positioning result after tidal correction are about 3.4 cm and 1.2 cm under both typhoon and calm weather conditions. This accuracy is precise enough to be applicable to a tsunami early warning system. Since our long-baseline kinematic GPS analysis is effective to a long baseline up to 500 km, we can place a GPS buoy far offshore, which ensures an adequate evacuation time even, for people living on the coast

Access and utilisation of primary health care services comparing urban and rural areas of Riyadh Providence, Kingdom of Saudi Arabia

The Kingdom of Saudi Arabia (KSA) has seen an increase in chronic diseases. International evidence suggests that early intervention is the best approach to reduce the burden of chronic disease. However, the limited research available suggests that health care access remains unequal, with rural populations having the poorest access to and utilisation of primary health care centres and, consequently, the poorest health outcomes. This study aimed to examine the factors influencing the access to and utilisation of primary health care centres in urban and rural areas of Riyadh province of the KSA

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model

In the recent digital age, information and communication technologies are rapidly contributing to remodel the media and journalism. Numerous technologies can be utilized by the media industry to capture news or events, taking footage and pictures of a breaking news. Technology and the media are interwoven, and neither can be detached from contemporary society in most nations. Unsurprisingly, technology has affected how and where information is shared. Nowadays, it is impractical to discuss media and the methods in which societies communicate without addressing the rapidity of technology change. Thus, the aerial journalism term has emerged, which refers to the ability of creating and conveying media content in a timely and efficient fashion. This work aims to integrate a drone with AI to empower aerial journalism via training a neural network to obtain an accurate channel using the NN-RBFN approach. The proposed work can enhance aerial media missions including investigative reporting (e.g., humanitarian crises), footage of news events (e.g., man-made and/or natural disasters), and livestreams for short-term, large-scale events (e.g., Olympic Games). In our digital media era, such a smart journalism approach would help to become far more sustainable and an eco-efficient process. Both MATLAB and 3D Remcom Wireless Insite tools have been used to carry out the simulation work. Simulated results indicate that the proposed NN-RBFN managed to obtain an accurate channel propagation model in a 3D scenario with a high accuracy rate reaching 99%. The proposed framework also could offer various media and journalism services (e.g., high data rate, wider coverage footprint) in timely and cost-effective manners in both normal scenarios or even in hard-to-reach zones and/or short-term, large-scale events

EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

Healthcare is one of the most promising domains for the application of Internet of Things- (IoT-) based technologies, where patients can use wearable or implanted medical sensors to measure medical parameters anywhere and anytime. The information collected by IoT devices can then be sent to the health care professionals, and physicians allow having a real-time access to patients’ data. However, besides limited batteries lifetime and computational power, there is spatio-temporal correlation, where unnecessary transmission of these redundant data has a significant impact on reducing energy consumption and reducing battery lifetime. Thus, this paper aims to propose a routing protocol to enhance energy-efficiency, which in turn prolongs the sensor lifetime. The proposed work is based on Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) for Healthcare using IoT, where Dual-Prediction Mechanism is used to reduce data transmission between sensor nodes and medical server if predictions match the readings or if the data are considered critical if it goes beyond the upper/lower limits of defined thresholds. The proposed system was developed and tested using MATLAB software and a hardware platform called “MySignals HW V2.” Both simulation and experimental results confirm that the proposed EERP-DPM protocol has been observed to be extremely successful compared to other existing routing protocols not only in terms of energy consumption and network lifetime but also in terms of guaranteeing reliability, throughput, and end-to-end delay

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor

Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm

Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system using Convolutional Neural Network (CNN) with a smaller number of gas sensor elements. We have identified redundant gas sensor elements in a gas sensor array and removed them to reduce the power consumption without significant deviation in the node’s performance. The inevitable variation in the performance due to removing redundant sensor elements has been compensated using specialized data pre-processing (zero-padded virtual sensors and spatial augmentation) and CNN. The experiment is demonstrated to classify and quantify the four hazardous gases, viz., acetone, carbon tetrachloride, ethyl methyl ketone, and xylene. The performance of the unoptimized gas sensor array has been taken as a “baseline” to compare the performance of the optimized gas sensor array. Our proposed approach reduces the power consumption from 10 Watts to 5 Watts; classification performance sustained to 100 percent while quantification performance compensated up to a mean squared error (MSE) of 1.12 × 10−2. Thus, our power-efficient optimization paves the way to “computation on edge”, even in the resource-constrained 6G-IoT paradigm